A beginner’s guide to Software Engineering
Curious about a career as a software engineer? You’re in the right place. Whether you want to end up coding games, or if you’re just asking yourself, What is coding?, we can help. Read on to learn all you’ve ever wanted to know about computer programming. We go into the nuances of software developer vs. software engineer. We discuss the value of enrolling in a free coding bootcamp. We even give you recommendations on programming certifications.
Dive in. Great software engineering jobs await.
Beginner’s Guide to Data Science
So you’re wondering how to become a data scientist. You’re curious about data scientist jobs, and maybe you’re considering joining a data science bootcamp. Maybe you’re interested in finding out more about data scientist salaries or probability theory.

You’re in the right place. Here’s all you need to know about data science certificates, artificial intelligence jobs, how much data scientists make, and more. Is a data bootcamp for you? Can you find remote data scientist jobs? Find out by reading on.
A beginner’s guide to Business Intelligence
So you’re curious about business intelligence jobs. You’re in the right place. Here, you’ll find all you want to know about remote data analyst jobs. We’ll go into business analyst vs data analyst. We’ll explore BI reporting and why it matters. We’ll show you how experts use Google data analytics and give you the knowledge base that’ll help you get a Google data analytics certification down the line. Business intelligence reporting and cloud BI can be your entry into a great new career. Find out how by reading on.

Quality Assurance

The most common Business Intelligence questions
Business Intelligence careers
Business Intelligence applications
Want to become a Business Intelligence Analyst?
Our pitch to you
The most common Business Intelligence questions
What is business intelligence?
In short, Business Intelligence (BI) is about examining business data to help people make informed decisions. It focuses on reporting, dashboards, and data visualization and storytelling. To achieve this, it uses software to extract, transform, and load data from diverse sources. Larger firms that are ‘data-driven’ are likely powered by enterprise business intelligence.
This makes BI applications vital. BI gives companies insight into users, market trends, and their own in-house projects. This helps them find new chances for growth and lowers risks. Also, by looking at past, current, and predictive views of data, BI helps leaders grasp key performance indicators (KPIs) so they can see if their actions are truly working. If not, then BI can help them find the KPIs that can guide the business better.
BI plays a crucial role in data-driven cultures. It fosters deeper inquiry into how the business works.
What does a data analyst do?
A day in the life of a data analyst can look akin to a day for a BI worker. Both of these techies may use tools such as Google data analytics. However, these jobs are not the same.
Both in-house and remote data analysts go deeper than people in BI. While BI and data analysis focus on gaining insight from data, data analysts tend to have more technical skills. For instance, they may use Python to forecast and to automate data analysis.
This means that day-to-day, these techies are hands-on with data. They collect, process, and interpret it. This uncovers key insights, trends, and patterns that can guide the business.
So what is a big data analyst? Someone who uses a slightly more technical skill set to transform raw data into easy-to-grasp reports and visualizations. This helps businesses make informed decisions. Like BI, it spurs refinement in strategies and improves outcomes. Unlike BI, it uses a little more technical know-how to get there.
What are the daily activities of a business intelligence analyst?
BI analysts’ days revolve around turning business data into insights. That means their daily tasks involve data collection, analysis, and visualization.
They gather data from sources such as databases, spreadsheets, and external APIs. They clean and organize this data and ensure accuracy. Then, they find trends, patterns, and correlations to answer specific business questions.
Next they make dashboards and reports to explain the data. They use many tools to do this, but a common business intelligence tools list includes Tableau, Power BI, and Qlik Sense. This makes their insights easy to understand.
But collaboration is also key. BI analysts often liaise with diverse colleagues to uncover their needs and provide tailored insights. They might hold meetings to discuss findings, receive feedback, and adjust analyses.
In addition, the field of BI never stays still. That means BI workers need to stay up-to-date on industry trends and new BI tech.
Is business intelligence the same as data science?
While they both handle data, they are not the same. Data science (DS) is more technical than BI. In fact, it is even more tech-heavy than data analysis. DS unites statistics and computing to build predictive systems. This means that data scientists focus on working with coders in their day-to-day work. They’re often hands-on with code in a way that BI workers aren’t.
BI is about storytelling and making business data clear to humans. People working in BI talk with colleagues to find out what’s needed. They then apply business frameworks such as marketing funnels to address these needs. This helps leaders grasp what the business is doing and backs up the findings with hard numbers. This makes BI a more communication-centric field.
DS, on the other hand, is about building and honing machine learning models. Often, these models are user-facing. Think of the video or product recommendations you get on your go-to platforms, for instance. The systems behind those suggestions were built and trained by data scientists.
Because of this, the tools for each field also differ. BI uses Excel, Tableau, Power BI, and SQL. Data scientists favor R, Snowflake, Redshift as well as Python and its libraries such as Pandas, Scikit Learn, and Torch.
Why is customer intelligence so important to companies?
Business intelligence reporting can take many forms, one of which informs customer intelligence (CI). These insights are key to companies because of what they fuel:
  • PersonalizationCI allows companies to tailor products and messages to enhance experiences and increase engagement.
  • Customer retentionCI data can help companies find pain points and address issues promptly, leading to improved satisfaction among their users.
  • Effective marketingCampaigns based on CI yield higher conversion rates and return on investment because they can mesh with customer tastes and behaviors.
  • Resource optimizationCI helps companies direct resources to high-value users and find the places that most need extra investment.
  • Risk mitigationCI helps firms foresee shifts in markets and customer tastes. This makes them more prepared for change down the line.
How is artificial intelligence related to business intelligence?
These fields are quite distinct. Artificial intelligence (AI) uses machine learning to mimic human problem solving, decision making, and more. BI looks at business data and gains insight from it.
AI can inform BI, though. Here are a few ways:
  • AutomationAI can automate routine tasks such as data cleaning and report generation. This saves BI workers time.
  • Data explorationAI can explore data to unearth hidden relationships. It can also suggest areas for further examination.
  • PersonalizationAI-driven BI can tailor info for users, whether they are executives, managers, or frontline employees.
  • Real-time insightsAI helps BI systems process data in real-time. This can offer up-to-the-minute insights that are crucial for timely decision-making in fast-paced business environments.
  • Continuous learningAI-powered BI systems can learn from new data inputs and adjust their algorithms over time. This improves the accuracy and relevance of insights.
Business Intelligence careers
Business intelligence jobs
So now that you know what BI is all about, let’s talk about the BI jobs out there. Often, there’s much more to them than you might find in a business intelligence analyst job description. BI has tons of tech careers open for you, and each has its own unique flavor. And, to top it off, entry-level BI salaries are quite good. Of course, BI analyst jobs have their own quirks. You can see that in detail above as we talk about business analysts vs data analysts.
But you might still wonder about business intelligence vs business analytics. Are they the same thing? The job titles and descriptions you might find on career sites can seem confusing.
Here, we’ll help clear things up for you. There are two key BI roles to know. Their daily tasks reflect what you can expect to do in most BI jobs. And just to answer a question in advance: yes, you can find remote jobs in BI. This is tech we’re talking about, after all.
Let’s dive into the two main types of BI roles.
BI developer
People in these roles design and maintain BI reports that cater to business needs. That means they spend a good deal of time talking to stakeholders and leaders.
A large portion of their work involves extract, transform, and load processes. This includes getting data from diverse sources, structuring it, and storing it for later analysis. This ensures data accuracy, consistency, and quality. Also, data security is key to their work. This is especially true with cloud BI. Because of this, BI devs make sure that data is safe and that their firms stay in line with laws and regulations.
To do all this, BI devs use BI tools like Tableau, Power BI, or QlikView to design dashboards, reports, and visualizations. This helps stakeholders quickly grasp insights, trends, and patterns to guide them to well-informed decisions.
BI devs can also help document data models and reports. This ensures that knowledge and data standards cascade across their teams. They may also provide training to users so that non-tech staff can navigate and interpret the BI solutions.
BI administrator
Let’s start out by talking about BI devs vs BI admins. While they both focus on BI data, their realms of work differ. Where a BI dev creates the tools for business data analysis, BI admins are more operational. For instance, they handle user access, security, and system maintenance. So they are also vital to proper BI at a company.
They control who has access to distinct levels of data and reports. This helps keep the firm in line with local law and keeps data secure. To do all this, they set up user profiles to ensure that only approved people can access sensitive data.
They also monitor the performance of BI platforms. They find and resolve issues that could hinder data retrieval and analysis. This may involve tuning databases and using caching mechanisms to enhance system responsiveness.
In addition, they might also help with maintenance and troubleshooting. They can lend a hand with software updates, patches, and backups. In the event of tech glitches or failures, they tackle issues promptly to minimize disruptions.
Of course, collaboration is also vital for people in these roles. They work closely with BI devs, analysts, and business stakeholders. Through frequent meetings, they align on business requirements to match their BI work with the firm’s goals and needs.
Data analyst jobs
Here we get to one more key thing to talk about: business analytics vs data analytics (DA) in terms of jobs. Above, we discussed how BI and DA differ in focus. Now that we know a little bit about BI jobs, we can look at those in DA. Like in BI, you can find remote data analyst jobs easily. Also like BA, entry-level data analyst salaries are quite respectable.
There are many paths you can choose within DA. Each one is unique, so let’s go into the main career routes people in DA tend to choose. See which one of the three described seems best for you.
Product analyst
This combines data analysis, strategic thinking, and a focus on the user. These people play a vital role in bridging the gap between user needs and product development. They collect and inspect data, feedback, and market trends to unearth insights to guide product upgrades.
Day-to-day, they work with people across varied teams such as managers, designers, engineers, and marketers. They dig into user actions, usage patterns, and metrics to see what’s working and what’s not. Where things aren’t going to plan, they apply data to unearth how to fix the issues.
Skills in storytelling are vital. Product analysts need to convince diverse groups of people of their findings and recommendations. They do this by crafting visualizations, reports, and presentations to explain complex insights.
This role also involves evaluation and adaptation. Product analysts track the impact of changes and new features. They then measure success against KPIs. This process helps refine product strategies and ensures that the product evolves to meet user expectations.
Marketing analyst
This is one more of the remote data analytics jobs you can find hiring across career sites. This role offers a blend of delving into data, looking into buyer behavior, and forward-thinking decision-making. Marketing analysts dive into data from many sources such as campaigns, website traffic, social media, and market trends. This helps them gain insights that steer promo plans.
These techies inspect data to spot patterns, trends, and correlations. They gauge the impact of ad campaigns and assess market tastes to fine-tune ad tactics. These insights then help their coworkers tailor messages to target the right people.
As such, working across diverse teams is key. People in these roles work closely with creators and sales pros. They provide data-driven insights that inform choices in content, distribution channels, and promo activities. Their work also helps companies set goals and track the success of marketing actions.
As in other jobs discussed above, an eye for detail and a knack for storytelling are vital. In this role, people translate complex data findings into recommendations for non-technical stakeholders. They create reports, dashboards, and visualizations that make the data clear. This then informs the steps a firm takes.
The field requires adaptability, though. Marketing and tech are known for how quickly they can change. Because of this, marketing analysts stay up-to-date on new tools, methods, and trends to ensure their insights are germane and useful.
Finance analyst
As you might guess, people in these roles are also key. They explore financial statements, economic trends, and market data to unearth patterns and trends that can inform fiscal roadmaps.
As the name suggests, people in these roles focus on data analysis. They check out fiscal reports to observe revenue, costs, and profit margins. This helps them assess the company's financial health. Using this data, they can locate areas where the firm can reduce spending, enhance revenue, and optimize.
As in the role described above, close collaboration is key. In this role, people work with diverse teams. They provide fiscal insights that support decision-making. This can take the form of checking the viability of a new project or predicting the financial outcomes of a business expansion.
This means that these people share complex data with non-financial teammates. To do this, people in these roles need top-notch skills in communication and visualizing data. Often, this job involves building reports that break down data into clear insights. This is all done to help leaders grasp the fiscal impacts of their choices.
Of course, problem-solving skills are also key. Finance analysts troubleshoot anomalies and suggest fixes. They may also forecast financial trends and outcomes to guide long-term strategic planning.
They are also highly valued. People with these skills will find it easy to land a job. But to stay at the top of their game, they must stay current with regulations, trends, and next-gen tech. Still, the role can be quite rewarding. It offers a blend of number crunching, strategic thinking, working with diverse teams, and direct impact on a firm's financial success.
Business Intelligence applications
So now that we’ve discussed business intelligence vs data analytics, let’s get a little more in-depth. BI can be used in diverse sectors, and it can have a real impact on a firm’s success. This means that the tools people use for BI truly matter. For instance, it’s key for people in BI to use the best power BI dashboards.
In fact, the applications of BI range so widely that BI experts have to know a wide range of tools and approaches. This includes not only dashboards but also databases and data manipulation and visualization tools.
That might sound like a lot, but you can get a handle on all of it in just four months. But before you get there, let’s take a closer look at the tech we’re talking about.
Working with databases
BI is mostly about taking business data and making it easy to grasp for non-techie people. But, although that sounds like a soft-skills heavy role, some hard skills are also key. For instance, knowing how to work with databases will make any BI pro even more attractive to employers.
This is because many companies now use cloud business intelligence. This means the data you will be using will be stored on databases. So, if you want to be part of the latest enterprise BI teams, knowing the ins-and-outs of how to extract data is vital. 
Effective database management is crucial to ensure accurate, accessible, and secure data. After all, this data is the backbone of BI work. It is what allows BI analysts to craft the analyses and presentations that positively impact the business at large.
Let’s look at databases a little more closely.
Postgres
Postgres, short for PostgreSQL, is an open-source database system known for its advanced features and robust performance. It has gained widespread use among coders, businesses, and organizations.

It can be applied in diverse ways. This can range from simple web-based tools to complex data warehousing. For instance, its extensibility allows users to create custom data types, operators, and functions. It also focuses on data integrity. It offers various tools for this, including multi-version concurrency control.
All this is key for people in BI. It allows them to tailor the database to their needs. Custom functions, data types, and extensions can be vital to the daily work of someone in BI because these robust features let them extract the right info. This helps them dig into large datasets and uncover insights. After all, finding new ways of looking at business with data is what BI is all about.
As it is open source, people can help augment it and expand what it can do. This leads to frequent updates, security patches, and new features. In essence, it is an enterprise-grade database system that strikes a balance between traditional relational databases and modern, diverse data management needs. It is a core tool for data storage, retrieval, and analysis that can fuel BI workflows.
Other Databases
While Postgres is a great tool, it’s not the only database out there. In fact, once you’re working in BI in the real world, your company might use one of many different databases. This will likely depend on what the database will be used for and the project’s scale. In any case, here are five you might see:
  • Microsoft SQL ServerThis is widely used for data storage, retrieval, and analysis. It offers crucial BI features such as seamless integration with Power BI and SQL Server Analysis Services.
  • Oracle DatabaseThis enterprise-grade database is known for scalability and performance. It's often chosen because it can handle large and complex datasets. Its robust nature is something BI techies deeply value.
  • MySQLThis open-source database is widely used because of its ease of use and low cost. You will most likely see it on smaller BI projects. For instance, it’s a common database used for web apps.
  • Amazon RedshiftThis is a tool offered by Amazon Web Services. It’s tailored for large-scale data analytics. This makes it well suited for BI, as it can tackle massive volumes of business data.
  • Microsoft Azure SQL DatabaseThis cloud-based database is part of the Microsoft Azure ecosystem. Because of this, it works seamlessly with Microsoft BI tools like Power BI. It is also known to be scalable and flexible.
Manipulating data
Once you can work with databases, you can start playing with data. There are a few core tools that help BI experts accomplish this. Let’s look at a few of them.
SQL
Structured Query Language (SQL) is a programming language that coders use when working with relational databases. It is the prime way that people talk to these repositories. As such, BI experts rely on it to manipulate and query data. SQL lets them extract, transform, and analyze data to derive meaningful insights.

The SELECT statement helps them retrieve data from tables, apply filters, and use groups to narrow down results. They can join tables to combine data from diverse sources and create a comprehensive dataset for analysis.
Transforming data is also a key aspect of BI. SQL's functions allow BI workers to collect, calculate, and format data as needed. Aggregation functions like SUM, AVG, and COUNT are used to derive summary statistics, while math functions support calculations for further analysis.
SQL also helps BI analysts cleanse data with filters. They can remove irrelevant records using WHERE conditions. They can then use SQL to create new tables or views that outline data for reports. This streamlines complex queries.
SQL aids in time-series analysis and finding trends. BI analysts can use SQL's date and time functions to extract relevant time periods and observe data changes over time.
Knowing this language allows BI workers to extract insights from databases. It is the language that lets them turn data into meaning and inform their organization’s decision-making process.
Google Sheets and Excel
These two spreadsheet tools are common among people who work with data. Of course, this includes people in BI. They use Google Sheets and Microsoft Excel for data analysis and visualization. These tools allow BI workers to import, clean, and structure data from varied sources. They can sort, filter, and transform data using built-in functions. 
For instance, pivot tables and charts help them boil down and visualize data trends. People in BI use these features to create lively reports and visuals. They highlight key insights to make it easier for stakeholders to grasp complex data.
Both platforms also support external data sources and APIs. This allows BI analysts to fetch real-time data for reports. This helps them track metrics and KPIs.
For more in-depth analysis, people in BI can use add-ons and scripting languages to automate tasks, create custom functions, and build more complex analytical models.
Both of these tools help BI workers process, analyze, visualize, and share data. They cover a range of analytical needs. Because of this, these two spreadsheet apps are key in BI work.
Data visualization tools
This was touched on above, but there are many ways to visualize data. Here’s a quick run-down of a few tools that people in BI use on a daily basis.
Tableau
This is something you’ll see BI experts use all the time. And it’s easy to see why. Tableau is a leading data visualization tool. Since people in BI are all about making data easy to digest, they often rely on this tool. It can turn complex data into insights that can spur informed actions.
It offers a user-friendly interface that allows BI analysts to connect to varied data sources, from databases to spreadsheets. Then it helps them create interactive and clear dashboards and reports.
With this tool, BI workers can explore data through drag-and-drop functions. This leads to swift analysis without asking that people in BI have advanced coding skills. 
It can make various types of visualizations such as bar charts, line graphs, heat maps, and geographic maps, which aid in presenting patterns and trends. Then, to get into more detail, filters and parameters allow users to customize views based on their own criteria.
Tableau also has a basic formula language. Using it, BI analysts can embed calculations. This lets them perform complex aggregations and derive new insights.
But Tableau can also build interactive dashboards. That means analysts can make dynamic presentations that allow stakeholders to drill down into data to gain deeper insights.

In short, Tableau is a robust tool for turning raw data into meaningful insights that drive informed decision-making.
Power BI
If you’ve been looking into BI, you likely saw this coming. Power BI dashboard examples can be seen across the BI space — how to work with them, the best way to set them up, the list goes on.
But this tool is used for good reason. Microsoft Power BI is a robust platform that can transform data into compelling insights. Power BI connects to diverse data sources, from databases to cloud services, and creates interactive reports and dashboards.
With Power BI, BI analysts can also drag and drop data to construct visualizations such as bar charts, line graphs, pie charts, and maps. These visuals help them highlight trends and patterns within data.
Power BI can also model data. This allows BI analysts to link distinct datasets, enhancing the depth of analysis. Then, the Data Analysis Expressions language within Power BI augments what people in BI can do. With it, they can run complex calculations for advanced analysis.
This may sound akin to what Tableau can do, but there is a key difference. Power BI is a Microsoft product. That means it works seamlessly with Excel and Azure out-of-the-box. This powers easy collaboration and data integration. And because so many tools mesh, it is simpler to share interactive dashboards with stakeholders.
Power BI is also mobile-friendly, and it has cloud-based deployment options. This makes it easy to scale for organizations of all sizes.
Other Tools
Self-service business intelligence tends to focus on the tools described above for visualization. But, as you might expect, there are other tools as well. In your BI career, you may also encounter the following:
  • Qlik SenseThis self-service tool allows users to apply associative data analysis to explore relationships dynamically. It’s also a way for BI workers to unearth and visualize data to create purpose-built dashboards.
  • LookerThis is known for its baked-in analytics and focus on data exploration. It can create custom data models and interactive visualizations.
  • DomoThis is a cloud-based platform for BI and data visualization. It offers real-time insights and dashboards. It can also connect to diverse data sources. Domo is easy to use, as it was designed to cater to both tech-savvy and tech-averse users.
  • SisenseThis tool shines when you need to refine complex data into easy-to-grasp insights. Like other tools, you can use it to make dashboards, reports, and embedded analytics for a wide range of business users.
  • Google Data StudioYou might see this in your BI career for one simple reason: it’s free. Like other tools, it lets you create custom reports and dashboards. Unlike other tools, it works seamlessly with Google products such as Google Analytics, Google Ads, and Google Drive.
Want to become a Business Intelligence Analyst?
So you’ve gotten down here, and BI sounds like something you want to pursue. Read on to find out the ins and outs of how to land a BI career.
Learn SQL
Of all the tools listed above, none is more vital than this language. If you want to know more about it, check out our section on it above. We go into the nitty-gritty of it up there. Here, we’ll instead answer why BI analysts should know SQL.
In short, it lets BI analysts access, change, and analyze data directly from databases. In a data profession, this is key. After all, people whose jobs are all about data need to master the tools that let them work with data.
It allows BI techies to craft tailored queries, perform complex aggregations, and transform raw data into actionable insights. Using it, they can independently extract specific information, join data from distinct sources, and create custom reports. This lets them derive meaningful insights that drive informed decision-making within organizations.
And it’s a language that you can learn for free. In fact, in just 15 hours split across unique lessons, TripleTen can help you master its basics. Check out our SQL course here.
Do you need a degree?
This is a common question we come across. It comes in a few forms: is there such a thing as a business intelligence degree? Do I need a master’s degree to land one of these jobs? Can I still get a BI job if I studied something else in college?
Tech, as an industry, looks at people’s skills and know-how as opposed to their background. If you can show that you know how to do the tasks, you can land the job. In fact, according to a recent report, 59% of tech companies are thinking of eliminating college degree requirements entirely.
So if you want to start a career in BI, just gain the knowledge and master the skills. You don’t need any fancy degree to land a great BI job.
Business intelligence analytics bootcamp
To gain knowledge and skills, bootcamps are the way to go. They provide in-depth and up-to-date info on everything a BI expert will need to know. They’re focused on getting grads jobs, and that shapes how they teach. Instead of the theoretical knowledge that university computer science programs tend to prefer, industry experts at bootcamps teach the practical know-how that gets people hired in tech and keeps them thriving. Best of all, instead of demanding an expensive four-year commitment, some can be completed in as little as four months.
These intensive programs are often broken into two- to three-week sprints during which students have a set list of tasks to complete. But how and when they work on the tasks is up to them. This mirrors how the industry at large operates. That means grads come out of bootcamps not only with know-how, but also with baked-in experience with how tech works. 
All of that is what we provide in our Business Analytics Bootcamp. Our beginner-friendly course is focused on getting people the jobs they study for. We have tutors, insightful code reviewers, a community of fellow learners, and industry-seasoned experts who will help you gain the needed knowledge and skills. Then, once your studies are done, people in-the-know from tech will help you craft a catchy resume and a robust portfolio. With all of this, you’re sure to get a job. And we’re so confident in that promise that if you don’t get a job within six months of graduating, you’ll get your money back.
Find out more here.
BI certifications
The first thing to know here is that to start out as a BI analyst, you do not need a certification. Landing an entry-level BI job without one is no problem. But if you want to keep growing in your BI career, certifications might make your resume just that little bit more convincing. As such, here are our top five recommendations for BI certifications.
  • Microsoft Certified Data Analyst AssociateThis confirms skills in Microsoft Power BI and Excel. It covers data visualization, modeling, and preparation techniques.
  • Tableau Desktop SpecialistThis attests to skills in Tableau Desktop. It states that the person holding it knows how to create impactful visualizations.
  • Certified Business Intelligence Professional (CBIP)This is offered by The Data Warehousing Institute. It covers data analysis, data integration, and business performance management.
  • Qlik Sense Business Analyst CertificationThis affirms skills in using Qlik Sense for data visualization and analysis. It attests to the holder’s abilities in creating dynamic dashboards and reports.
  • Google Data Analytics Professional CertificateThe Google Data Analytics certification covers skills with Google tools. This includes data collection, transformation, visualization, and interpretation. It is a great first certification for entry-level BI professionals.
BI analyst job placement
Job placement is a cornerstone of the best data analytics bootcamps. For instance, TripleTen has a wide network of companies that it partners with to provide externships. During these learning experiences, students wrapping up their time at TripleTen join real-world projects. This gives them hands-on experience before they even start looking for jobs.
For instance, in a data-focused externship, you might find yourself cleaning, analyzing, and visualizing data that has real impact on a company’s future just like one of our grads. You could also end up being a key voice in guiding startups as they scale.
This experience is the exact thing hiring managers look for. That is why TripleTen makes sure to offer externships to its students. We’re focused on getting people hired in new, meaningful jobs.
A Producer Switches to Tech to Find Time for Life: AC Slamet’s TripleTen Story
Externship with TripleTen: Prepare4VC
Our pitch to you
Remote data analyst jobs aren’t as hard to get as you might think. The right bootcamp can help you pursue business intelligence jobs or even Google data analytics certification. It will give you skills in BI reporting and Google data analytics and expose you to Power BI dashboard examples.
This in-depth prep is what you’ll find at TripleTen. We’ll teach you business intelligence reporting so you can land that coveted BI analyst job.
Check us out here or dive into our free coding bootcamp. And if you want to see what job is right for you, take our career quiz.
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The most common Software Engineering questions
Software Engineering jobs
Types of software programming
Want to become a Software Engineer?
Our pitch to you
The most common Software Engineering questions
What is coding?
This is a core question for budding software engineers: what is coding, and how does it work?
Coding, also known as computer programming, is all about writing instructions that can be understood by computers. This is done to get them to perform specific tasks. These tasks can be as small as adding two numbers or as significant as powering AI systems for video games.
In either case, software engineers write a series of logical commands using a programming language that bridges the gap between people and machines. Common languages include Python, Java, JavaScript, C#, and C++. Each has its own strengths and weaknesses, and each has its own rules for how to write it. If you want to learn more about programming languages, check out our recent post.
Coding’s applications are vast. Code powers everything from the apps you use to order food to the Mars rover’s navigation systems. As such, it’s a valuable skill. People who know how to write code are consistently in high demand across diverse industries.
But there is one thing to specify here: computer science vs. computer programming. While they may seem similar, they differ in scope. Computer science is broader. It encompasses algorithms, data structures, database development, and much more. Computer programming, on the other hand, focuses on writing code. In fact, people often define coding as the practical application of computer science principles.
How do programs work?
Programs translate code into action. The exact way this is done can differ among languages, but most work along similar lines.
  • Compilation / interpretation
    This step is about the program preparing the code. Some languages such as C++ or Java need to compile to allow the computer to read the code. Languages like Python and JavaScript can be directly interpreted by the computer.
  • Execution
    The computer reads the code line-by-line and does what’s written. This is shaped by discrete systems such as:
  • Flow controlPrograms often include control structures like loops and conditional statements. They allow the program to repeat operations or make decisions.
  • Input and outputMany programs process inputs and produce outputs. For example, a program might need to know if you’ve pressed a button. Then, once that input is received, the program should do something. This output could be the motion of a character in a video game or a change to a file.
  • Memory managementOften, programs store, access, and change data. Good memory management ensures the program can use data without opening it to outside access.
  • Error handlingThis allows programs to safely deal with unexpected situations. For example, error handling enables the program to close down before a mistake in the code can fry your computer. Then, it can say what went wrong so that a coder can fix the issue.
  1. TerminationThis happens when a program reaches the end of its code or hits an instruction that tells it to terminate. During this step, the program gives back the memory it reserved and does other cleanup.
What does a software engineer do?
In a sentence, software engineers write the code that powers everything digital. This means that their work can be seen in almost every aspect of our lives. From your smartphone to your car, and from medical devices to the latest trendy toys, code is everywhere. Behind all of that, there is a software engineer.
But while writing code is the core of what software engineers do, it is not everything. Like with any job, it comes with other tasks.
Here’s a typical flow for software engineer responsibilities:
  • Figure out requirementsBefore a piece of software is made, the engineer needs to know what it should do. Because of this, developers spend a good amount of time sitting down and talking to teammates and customers. This ensures that everyone’s clear on what exactly the project’s all about and what it should accomplish.
  • Design the approachWhen an engineer knows what the code’s supposed to do, they create a plan. In this phase, they map out what will go where and how the elements should interact.
  • Write and test codeAfter figuring out what to write, the programmers start coding. Using their skills in their chosen language, they create the instructions that tell the software what to do. Once that’s done, it’s time to test. With the assistance of quality assurance (QA) engineers, they find and fix bugs.
  • Present projectsOnce the software is polished, they present it. They show it to their colleagues and customers and demonstrate how it accomplishes what they wanted it to. And if the initial requirements changed, they explain what changed and why.
  • Maintain softwarePrograms are dynamic. Often, they need to be updated. For example, engineers need to tweak smartphone apps when new phone operating systems are released. Because of this, software engineers spend time tending to their code and making sure it is up to date.
What languages do programmers use?
There are hundreds of languages that coders use. Instead of listing them all, we’ll focus on the top five most popular from StackOverflow’s recent survey.
  • JavaScriptThis is the language of the internet. JavaScript is the backbone of online apps, websites, games, and even some servers. It is what makes webpages interactive. In addition, it is a fairly easy language to learn.
  • HTML/CSSWhile they don’t contain programming logic, these two still combine to give websites their content and styling. Along with JavaScript, they are crucial for web developers. Devs use HTML and CSS to give sites structure and style, and they use JavaScript to make pages dynamic.
  • SQL (Structured Query Language)This language has a narrow scope, but when a piece of software needs to save, view, or change data from a database, SQL is key.
  • PythonWidely considered the easiest language to learn, Python is also very powerful. It is the language of choice for machine learning and artificial intelligence. But it also has a wide range of applications — from self-driving cars to video recommendation algorithms.
  • TypeScriptAs indicated by the name, this language is an extension of JavaScript. However, it adds static typing, which makes code easier to maintain and helps with readability. Like JavaScript, it is often used in web development, mobile apps, and some back end services.
What are daily activities for a coder?
Every coding job is different. But here are some things a coder can expect to do day-to-day:
  • Join meetingsGood communication and teamwork are crucial for a smooth development process. That makes regular meetings vital. In fact, coders often start their days with stand-up meetings. These are shorter check-ins that make sure the whole team stays in-the-know about what’s going on.
  • PlanBefore getting to the code, programmers go over requirements. They break down complex tasks into smaller steps and work out the best way to do what needs to be done.
  • Write codeThis is the core of the job. Coders use languages and development tools to translate their plans into instructions for computers. They write clean code that is efficient and readable.
  • Debug and testUsing debugging tools and testing frameworks, coders work with QA engineers to ensure their programs work as intended. If they find a problem, they go back into the code and fix it.
  • Document codeEngineers write comments and notes explaining their code. This will make it easier to maintain down the road. In addition, it will make the code’s logic clearer to other engineers.
  • Participate in code reviewsDevelopers come together to give and get feedback on the code they’ve written. This helps improve quality, reveals bugs early in the process, and fosters teamwork.
  • Research and learnTechnology evolves rapidly. Because of that, many software engineers spend time updating their knowledge of tools and techniques. They explore new tech, read the latest documentation, and participate in online forums.
Do developers work alone or in teams?
It depends on the project. Here are some ways coders work:
  • Solo workOn smaller projects, software engineers might indeed work alone. This gives them freedom and autonomy. This may be how you first start coding if you begin as a freelancer.
  • Pair programmingIt is what it sounds like — two coders working in tandem. Often, one writes code while the other gives feedback and helps make design decisions.
  • Agile / Scrum teamsThis is a way for larger groups to collaborate. Organizing teams this way is popular among tech companies. This approach involves sprints, two- to three-week chunks during which specific tasks need to be completed. During these periods, team members work on the bits of the larger project they’ve been assigned and regularly link up.
  • Distributed collaborationDistributed workforces and remote jobs are now quite prevalent. That means coders might have team members on the other side of the globe. While they still might use an Agile / Scrum approach, more far-flung teams will incorporate additional systems and software to make it easier for people to cooperate no matter where they are.
  • Open-source developmentOpen-source development might be the most collaborative way coders work. Within this approach, a global community comes together to contribute to a project bit-by-bit. Resources are free for anyone to access. Any programmer can submit changes, review code, and join discussions.
Where can I get a job as a software engineer?
Almost anywhere. When people think of tech jobs, they often think of Silicon Valley giants. But software engineers are in demand across the board. For example, TripleTen grads use their skills to help people find therapy, to design bespoke webpages, and to improve the lives of special-needs students.
Outside of tech companies, coders can find jobs in:
  • Financial institutionsSoftware now shapes banking, investment, and insurance firms. In fact, there’s likely a banking app on your phone. A coder wrote that.
  • E-commerce and retailSoftware engineers write the code that keeps online markets, e-commerce platforms, and retail companies running in our digital era.
  • HealthcareCoders write and maintain electronic health record trackers, lab management systems, and medical imaging applications.
  • EducationEver since school went virtual in 2020, EdTech has seen a boom. Coders build new tools that augment what teachers can do.
  • GamingVideo game firms always need coders. They write the code that makes this medium so engaging.
  • EntertainmentCoders write the algorithms that suggest TV shows on online platforms, the editing software that helps tell the shows’ stories, and much more.
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Software Engineering jobs
Software engineer career path
There is no one be-all end-all way to get this job and grow. Some coders go to four-year universities to get degrees. Some never even set foot inside a college. Some teach themselves code, and others join bootcamps.
But all coders start out learning. They choose a language or a focus and get hands-on to apply what they’re studying. Once they feel sure in their skills, they begin with entry-level positions to gain on-the-job knowledge. This intro to the field also shows them how a coding job operates day-to-day.
From there, they can take on more responsibility. Over time, this can lead to a senior role such as project manager, engineering manager, or technical lead. They can even become chief technical officers if they show a knack for inspiring others.
If you pursue the latest knowledge in your field and stay at the top of your game, software engineering can take you far.
Software engineer vs. developer
When we talk about software developer vs. software engineer, it might be hard to know quite what we mean. The terms can be used interchangeably. Still, there are general ways in which the two differ.
  • Software engineerThis job often entails a broader scope. Engineers can be present at various stages of development. They may have a say in requirements analysis, system design, coding, testing, and deployment.
  • Software developerThis role tends to be more specific. Developers are involved in coding and programming tasks. They translate requirements and designs into actual software solutions. They focus on writing code, implementing features, and debugging issues.
Web developer vs. app developer
Here we get into another distinction akin to software engineer vs. web developer or web developer vs. software developer. These two roles differ in focus.
  • Web developerAs the name suggests, this job is all about the web. It focuses on creating websites and web applications that run in browsers. These coders typically use HTML, CSS, and JavaScript.
  • App developerIn this role, people write applications that run on your phone or desktop. That means their programs don’t need web browsers to work. App developers tend to use languages such as Swift, Java, and Kotlin.
What is a full stack engineer?
To grasp what full-stack engineer jobs are about, two concepts need to be explained: front end and back end. Simply, “front end” covers the elements a user interacts with and sees. “Back end” handles what the user doesn’t see. Front end development is about crafting responsive designs and user interfaces. Back end development is about building systems that support those user-facing components.
The tech behind both the front and back end combine to make up what’s known as a “stack”. Hence, someone who can work in both aspects is known as a full stack developer.
For the front end, coders typically use languages such as HTML, CSS, and JavaScript as well as libraries such as React, Angular, or Vue.js. These add-ons make it easier to build dynamic user experiences.
For the back end, developers usually use Python, Java, Ruby, and even JavaScript. They augment this with frameworks such as Django, Spring, Ruby on Rails, or Express.js.
Overall, a full stack engineer is a well-rounded coder with a diverse range of skills in tech. These skills allow them to work on all layers of web application development. Their versatility and grasp of both front end and back end technologies make them valuable assets in modern software development teams. Because of this, they are highly employable.
Types of software programming
Website development
Making a website involves more than just one task. For example, tech workers build the user interface and write code for both the front and back end. For the front-end, they focus on creating pleasant and user-friendly interfaces using HTML, CSS, and JavaScript. For the back-end, coders craft server-side logic and handle data storage using Python, Java, Ruby, or JavaScript.
Full-stack coders do it all, but often, people specialize. Let’s go a little deeper.
Web app programming
If you’ve ever used the internet, you’ve used a web app. They are the programs you interact with in a browser. Just think of the things you tend to use on a daily basis — social media sites, booking platforms, weather forecast apps, etc. They are all powered by web apps.

That’s what web app programming is all about. Within this field, coders make interactive apps that run on web browsers. They use languages such as JavaScript, Python, Ruby, or PHP. These are then enhanced with frameworks like React, Angular, Django, or Ruby on Rails.

These programs handle user inputs, process data, implement security measures, and manage user sessions. They also engage with data storage, retrieval, and management, and apply APIs that connect front end and back end components.

While web app programming may seem weighted to the front end, it also involves the back end. These apps must mesh with servers, databases, and external APIs.
Back end engineering
As stated above, back end engineering is about the things the end user doesn’t see. The database that stores your login details? That’s part of the back end. The server hosting the site you’re trying to log in to? It’s also the back end. Think of the back end as the scaffolding on which the front end is built.

When coders make the back end, they build the logic, functionality, and infrastructure that support the front end. They also enable the app to interact with databases, external services, and other resources. They use languages such as Python, Java, Ruby, PHP, or Node.js, and frameworks like Django, Spring, Ruby on Rails, Laravel, or Express.js.

Back end engineers work with front end developers, database administrators, and other team members to build robust and scalable apps. Their code is key to powering the core functionality of an app and ensuring its smooth operation, security, and efficiency.
Want to become a Software Engineer?
So you’ve read all about what it’s like to be a software engineer. Now you want to try it out, but you don’t know where to start. That’s where TripleTen comes in. Read on to see how you can start pursuing this great job.
Learn to code
The core skill all software engineers have is coding. If you want to know what language is best or which has the highest salary, we go into that in detail in our previous post. Here, we’re going to show you a few approaches to learning to code.
Computer science degrees
When people consider learning to code, they often think they need a software programmer degree from a four-year university. These schools do indeed give students strong foundations in algorithms, coding languages, and other fundamental concepts. In addition, four-year programs also include classes that provide theoretical knowledge. Combined, this can give grads a good basis for a wide range of technical roles. That’s not to mention the other perks that come with a college education — the degree, the on-campus experience, and the weird classes in unexpected knowledge that can refresh how you view the world.

This does come with downsides, though. The average college education costs $35,551 per year. The average student debt is $28,950. That’s not to mention that many people take on these high costs and do not even graduate. That means they have debt without a degree — the worst of both worlds.

In addition, as computer science changes so rapidly, college curricula struggle to stay relevant. Not only that, but the information taught is often too theoretical. It tends to be detached from real-world uses. Then, once grads start looking for jobs, they realize they have no practical skills or industry experience to put on their resumes.

This is not to say that college is a bad choice. If you are interested in higher education for reasons beyond future employability, the degree is likely still worth it. If you’re focused on learning to code to improve your career prospects, there are other ways that are cheaper and more employment-focused.
Coding tutors
Often, people choose to learn with online coding tutors. Programming tutors can be found on freelancing websites, and often their lessons can be very affordable. Sessions can be arranged according to the learner’s schedule, and different tutors can cover different languages or technologies. This can make tech knowledge very accessible for learners. In fact, if you’re looking for a good resource, here are the best YouTube channels for Software Engineering students.

However, tutors tend to only focus on one aspect of getting a job in tech: the hard skills. Other key things such as resume and interview prep are not in their purview. In addition, when students finish their time with a tutor, they may not have anything to show for it in their portfolios. That’s not to mention that tutors can vary wildly in quality and competence. Sifting through a sea of freelance coding teachers can be a job in and of itself.
Software Engineering Bootcamps
Software engineering bootcamps are the way to learn coding for career-minded people. They focus on giving students useful knowledge and experience to get them the new career they’re searching for. Because they are staffed by industry experts, these programs can teach the latest versions of the most in-demand coding languages.

These bootcamps are shaped into two- to three-week sprints. During this time, students are given tasks to complete in cohorts, but how they approach the tasks is up to them. This reflects how work is done in the tech industry, meaning grads leave bootcamps more prepared for a new career. In addition, they are placed among a robust community that can support them in whatever way they might need.

But the education at bootcamps extends further. In addition to the hard skills in coding, these programs often include sections dedicated to career prep. This includes guidance on building a strong resume, advice on negotiation, mock interviews for practice, and more. Not only do grads leave bootcamps with knowledge of code. They also gain the ability to clearly communicate their skills to hiring managers. This is why many bootcamps feel confident enough to offer guarantees — if students don’t find a job in their new field within six months of finishing the program, they get their money back. Sound too good to be true? Check it out for yourself.

In fact, there are even free coding bootcamps out there. For example, there is one all about SQL, the language techies use to get programs to talk to databases. It’s a skill in high demand, and you can start your journey in tech with it without paying a cent. Find out more about it here.

And if you want more insight on getting a tech career, check out our previous post here.
Software engineering certifications
There are many certifications for software engineers. Often, these programming certifications center on one technology or one field. They are not crucial for coders just starting out. However, software engineer certifications can give techies a leg up later in their careers. There are tons of coding certifications out there, but we’re going to focus on five.
Here’s a list of what we think are the best certifications for software engineers:
  • AWS Certified Developer - AssociateOffered by Amazon Web Services (AWS), this certification validates expertise in developing and deploying applications on the popular AWS cloud platform.
  • Microsoft Certified: Azure Developer AssociateThis certification demonstrates proficiency in designing, building, and deploying applications on Microsoft Azure, a widely used cloud computing platform.
  • Google Certified Professional Cloud DeveloperThis software engineering certification confirms skills in developing, deploying, and managing applications on the Google Cloud Platform.
  • Certified Scrum DeveloperThis computer programming certificate offered by the Scrum Alliance validates knowledge of Agile methodologies and software development practices. It demonstrates the holder’s proficiency in Scrum principles, collaboration, and delivering high-quality software.
  • Oracle Certified Professional, Java SE DeveloperThis software engineering certificate showcases expertise in Java development, including core programming concepts, object-oriented design, and proficiency in Java SE technologies.
Depending on how your coding career pans out, one of these may be more relevant than another. It's worth noting that software developer certifications alone do not guarantee career success. Practical experience, continuous learning, and staying informed on industry trends are all vital.
Software engineering job placement
Learning to code is crucial. But on-the-job experience is equally as important if you want a tech career. That is where coding job placement comes in.
At TripleTen, this takes the form of externships. During externships, learners at the end of their studies join real-world projects. This gives them the chance to get hands-on experience before they even finish the program. Students see how tech works day-to-day as they develop websites for companies battling plastic pollution or even help a company make key high-level marketing decisions.
The skills gained are invaluable. For example, students engage in real-world code reviews. This teaches them how to give and get feedback in a professional environment. Down the line, communication skills like these make them stand out to hiring managers.
Whatever shape it comes in, job placement should be at the top of your list when looking for a good bootcamp. After all, you’re most likely not joining a bootcamp just for fun. You want the program to help revitalize your career. So choose one that is career-focused. Find out more in our post here.
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Our pitch to you
So what is coding? More than you might think. Software engineers write the computer programming that defines our lives. They are the ones coding games and building apps.
Our software engineering bootcamp can make you one of them. Software developer vs software engineer? The choice is yours. You’ll gain hands-on experience that can get you employed in no time, and you’ll be ready to pursue later programming certifications.
Check us out here or dive into our free coding bootcamp. And if you want to see what job is right for you, take our career quiz.
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The most common data science questions
Data scientist skills
Data scientist roles
Data science salaries
How to become a data scientist
Our pitch to you
The most common data science questions
What does a data scientist do?
A data science job can change based on numerous factors. There will be wide variations in day-to-day work among different positions. But there are certain commonalities. And they revolve around training machine learning models on good data to build intelligent systems.
Like many data professionals, data scientists start their process by cleaning their data. They remove anomalies, cut out repeat entries, and delete corrupted information. But the next step makes data scientists unique. They then use this data to construct models that make predictions.
For a concrete example, data scientists build the music app recommendation algorithms that guess what songs to play next. Think about the time one of your playlists was supposed to end but kept on going with similar songs. A data scientist built the model that made that possible.
As you might be able to guess, these tasks make data science a fairly coding-heavy role. Sometimes, people in DS might appear to have jobs akin to those of software engineers. After all, they seem to spend so much of their time building and tweaking machine learning models.
And it’s true; data scientists do spend a lot of their time coding in Python. This language is easy to learn, powerful, and has libraries that are tailor-made for machine learning.
But while the bulk of the job is about building predictive algorithms, it isn’t the only thing these professionals do. They also work with software engineers to build tools to process data. And day-to-day, they still have meetings to make sure their projects are in line with company goals and to make sure they’re up-to-date with everything their teams are working on.
Why is data science important?
Data science is about so much more than the music recommendation algorithm mentioned in the previous section. It can make companies more competitive by allowing them to foresee and prepare for changing market conditions. It can help businesses understand their customers better so they can tailor their products to uncovered preferences or needs. It can make organizations more efficient by streamlining supply chains and logistics.
That’s all vital to how things work throughout the economy. For example, better logistics and supply chains lead to less waste, as the right products will be where they need to be when they need to be there.
Think about fruit; data science will lead to more of it arriving fresher. In addition to this shoring up profits, data science will also help ensure that more people have more nutrition and that less food will be lost to rot.
And data science is crucial in healthcare, too. Consider LYNA (Lymph Node Assistant), Google’s data science solution. It can spot tumors that might metastasize to the patient’s lymph nodes. People can have trouble distinguishing these cells, but in one trial, LYNA caught these cancers 99% of the time thanks to machine learning.
And it goes so much further. Data science can aid in the fight against climate change. It can make video games more fun. It can help people find love on dating apps.
Its applications are myriad and profound; that’s why it’s important.
Is data science a good career?
Yes.

Even if you don’t land one of the several Google data scientist jobs, you can still thrive as a data scientist. It’s one of the most secure and well-paid tech careers out there.
Let’s start with job security. As you can see from the section above, it’s hard to find a field in which data science is not in high demand. From online streaming services to government bureaucracies, organizations need people with expertise in data science. In fact, according to the U.S. Bureau of Labor Statistics, data scientist jobs are expected to grow by 35% in the next 10 years. For context, the average growth rate for all occupations in that same period is only 3%.
And we can return to the U.S. Bureau of Labor Statistics to prove the point about how lucrative the career is. As of May 2022, the median salary for data scientists across the United States was $103,500. Again, compare that to the median wage for all workers: $46,310.
That’s not even to mention the perks that come with the career: flexibility, working on rewarding tasks, options for remote work, and more.
What is the difference between a data analyst and a data scientist?
The responsibilities of both these positions can overlap. After all, they both handle data, so similarities are inevitable. Depending on the position, people in both of these roles query databases and load, clean, and explore data.
However, there is a clear delineation between them. Data analysts focus on using basic technical skills for hypothesis testing, exploratory data analysis, and predictive analysis. Once they have their findings, they present their insights to help humans make better decisions.
This is where the two diverge. Data scientists are more tech-oriented. They have a much more robust set of technical skills, specifically in Python. They use this know-how to collaborate with software engineers and build scalable predictive systems. So, instead of data scientists extracting data to help a person make a decision, they use data to build models that make decisions themselves.
How long does it take to become a data scientist?
Here’s your favorite answer: it depends. There are undergraduate data science programs at many colleges, meaning you could spend four years studying for the job. You can then get a master’s degree in the field, which would add two more to that. In fact, if you wanted to, you could even get a PhD. Altogether, that could add up to eight years of study to become a data scientist.
That’s not really the answer that a lot of people are looking for, though. Many people want a career change fast. In that case, a bootcamp is the best bet.
With TripleTen, you can gain all the knowledge and skills you need for your first data science job in only eight months. And then, once you graduate, 87% of our students land jobs within six months. In fact, 54% of our students land jobs even before they finish the program.
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Data scientist skills
Data preparation
Of the skills required for data scientist roles, these are the most fundamental, and the ones that data scientists spend the bulk of their time using. These skills include loading, cleaning, integrating, and exploring data.
This is all about the first stages of the data science process. Data science starts with mountains of data. It’s messy, noisy, and inconsistent, and in its raw state, is unusable. It needs refining.
This is where these skills come in. The data needs to be loaded to be manipulable. Then, it needs to be cleansed of duplicate or erroneous information, merged from diverse formats into one unified dataset, and examined for patterns to inform the models that will be built from it. And that is just the most superficial description of data preparation. There are numerous other skills that dovetail with one another within this category.
We’ll underline it here: Data preparation know-how is fundamental for data scientists. These skills make raw data usable, leading to more accurate and better-trained models.
Probability theory and statistics
As we touch on the most math-informed skills a data scientist needs, we’ll let you in on a secret. The predictive algorithms don’t know what show you’ll like, what song to play next, or the exact way a supply chain will function. But they are quite adept at making remarkably accurate guesses. This is where your knowledge of statistics, probability, and queueing theory come in.
Skills in probability theory, descriptive statistics, regression analysis, and time series analysis allow data scientists to build models that turn data into actual insight. Applying these mathematical approaches uncovers correlations, patterns, and trends. It reduces the uncertainty inherent to data.
These skills are crucial for data scientists. In fact, a love for statistics was even the impetus for one of our grads to join TripleTen’s Data Science program in the first place.
Machine learning
Now we get to what makes people excited about data science. Along with natural language processing (which we’ll cover in the next section), machine learning data analysis and other machine learning applications grab the most headlines. It’s easy to see why — you can train a computer to do a thing! It learns!
Of course, you can’t just tell a computer to learn in your most commanding voice and expect it to comply. You’ve got to have some skills. Typically, for machine learning, a data scientist should be well-versed in:
  • Interpreting modelsYou’ve got the model — congrats! The only problem is that, if you can’t figure out what it’s telling you, the model is useless. So the capability to understand the model’s output is crucial.
  • Reinforcement learningIf the model is starting to follow the right path, you need to be able to make sure it keeps going. Enter reinforcement learning, which provides positive feedback to make sure the model works in the real world.
  • Anomaly detectionA good data scientist will use stats to weed out unhelpful data before the model runs. Then, they’ll apply the same techniques to make sure the outcomes aren’t skewed by hidden outliers.
  • Deep learningBasically, this is all about neural networks. Data scientists will be using them regularly, so an understanding of how to initiate and use them is paramount. This means that data scientists should also be familiar with deep learning frameworks such as TensorFlow and PyTorch.
  • Model evaluationOnce again, congrats on the model! Only — did it actually do what you wanted it to do? Knowing how to evaluate the model for accuracy, precision, recall, and more is vital. In addition, data scientists should know how to balance the value of these different metrics when assessing the efficacy of their models.
Algorithms
An argument can be made for algorithm skills to fall under the previous heading. But when we thought about it, we felt like this needed its own section. Algorithms are essential to the work that data scientists do, so instead of putting them as a subcategory, they deserve highlighting.
The problem is that, since algorithms skills are so technical, we get into terms that might make people’s eyes glaze over. Hyperparameter tuning with Bayesian optimization! Dimensionality reduction!
So, to make this section useful to someone just beginning to explore data science, we’ll keep it out of the weeds. In short, algorithms form the basis of a wide swath of the technical work that data scientists do on a daily basis. They’re really, really important.
Depending on your position, you might apply one type of algorithm more often than another. But no matter what, a good bootcamp will give you a knowledge base that will let you get started with algorithms.
Natural language processing
Here’s the second place where data science seems to be pure alchemy. Natural language processing (NLP) tools can extract surprising insights from text. In fact, that’s basically its entire point. That’s why data scientists need to be familiar with natural language processing techniques. These can include:
  • Text preprocessingConsider this like the data cleaning we mentioned earlier. Only, instead of homogenizing data so it can be useful, this skill is about knowing how to unify the formatting so models can easily intake and use text.
  • Sentiment analysisSkills in sentiment analysis are key in NLP, as they help data scientists not only extract data from text, but also the context for the data. A model might be able to see that social media is buzzing with mentions of a product, but without sentiment analysis, it can’t say whether that’s a good or a bad thing.
  • Domain knowledgeAlthough this is not a technical skill, it can still give applicants a boost when looking for a job. Data scientists need to know something about the area they’re working in to be able to elicit valuable, meaningful insights. It allows data scientists to ask good questions and understand the answers.
  • NLP libraries and frameworksThis may seem obvious, but natural language processing with Python is becoming more widespread, meaning that knowledge of Python NLP libraries and frameworks is becoming more important. That means that a mastery of scikit-learn, spaCy, and NLTK, for example, will give you a leg up in your applications.
  • Text vectorizationThis is a fancy term for taking text data and giving it numerical values. This allows models to more easily parse text and extract insight from it. Having these skills will help you make your models more efficient and less prone to errors.
A/B testing experiments
This is what it sounds like. A/B testing compares two versions of something such as a website, marketing strategy, or new product feature. This shows which iteration is better in terms of clicks, revenue, subscriptions, or any other metric. But, because these changes can be minute and still generate huge amounts of data, skills here are vital.
For example, data scientists should have knowledge of experimental design. Only a clear and well-controlled comparison will result in useful data. Once data scientists have this information, they can then untangle the data using the techniques mentioned above.
R and Python
Learn these languages. Seriously.


These are the two data science programming languages that will be key to you landing a data science job. If you take nothing else from this section, remember this one thing: R and Python are the bedrock skills you’ll need in your data science career.
Online, R is less emphasized than Python, but it still has applications. It focuses on statistical analysis and data visualization. And while that means its usefulness as a data science language is perhaps more limited than Python, it’s still valuable. After all, if you look at the sections above, you can see how valuable stats are.
Python is the language in which the majority of machine learning algorithms are written. It’s robust, one of the easiest languages to learn, and has a wide community behind it. Every day, someone seems to come out with a piece of tech augmenting this language’s data science capabilities.
A good bootcamp will emphasize machine-learning-focused Python libraries. These are add-ons to the language that make writing algorithms smoother, more efficient, and more intuitive. Common examples include NumPy, Pandas, and scikit-learn.
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Data scientist roles
So we’ve got the skills cleared up, and now you’re curious about careers in data science. As you may have gleaned by now, there’s more than one data scientist career path and approach to work. In fact, data scientist remote jobs abound. But we’re getting ahead of ourselves. Let’s dive into the jobs you can get after specializing in data science.
Data scientist
Surprise! If you study data science, you can then look for data scientist jobs. Shocker, we know. Data scientist jobs, remote or on-site, revolve around data. For a more detailed description, check out ‘What does a data scientist do?’ above.
Research scientist
This category covers a wide range of different specialties. You can pursue computer and information research scientist roles, but there’s so much more. Research scientist jobs vary based on your specific industry and company. Still, in any research scientist role, a strong background in data science will be key.
These experts unite scientific inquiry and data-driven decision-making. Because of this, knowing how to wrangle data is vital. After all, every step of the data science process is present in the daily work of research scientists.
The thing that makes these jobs unique is their focus. Instead of using machine learning to streamline supply chains, research scientists use data to expand our understanding of the world. For example, they use data science to improve healthcare, combat climate change, or even answer societal questions.
Machine learning engineer
This is for the people who really love making algorithms. Day-to-day, they’re spending the bulk of their time creating, deploying, and optimizing machine learning models. That means they have to determine what these algorithms should do, train them on data, and then make sure there are no bugs.
Naturally, they don’t work alone. They collaborate with data scientists, software engineers, and project managers to ensure the models are doing what they’re supposed to. This includes integrating the model into the final production environment and scaling it later on.
This skillset is flexible, so it is in demand across nearly all industries. Still, domain-specific knowledge will help machine learning engineers craft more effective models.
Artificial intelligence engineer
It might feel strange to have this come after a section about machine learning engineers. After all — is there a difference?
Artificial intelligence jobs and machine learning roles often overlap. Natural language processing jobs, for example, can blur the line between ML and AI. In fact, many companies might use the terms interchangeably. But, in general, AI engineers tend to have a broader scope.
Machine learning focuses on developing algorithms that handle data-driven tasks. Think about that system surfacing new songs in your music app, for example. 
AI engineers use machine learning but take digital cognition a step further. They build systems that understand, reason, and make decisions. These systems focus on solving problems that demand near-human levels of perception and reflection. Think chatbots or autonomous vehicles.
Entry-level data science roles
Entry-level data scientist jobs can be found in nearly every industry. Healthcare data scientist jobs abound, and even in product development, data science expertise is in high demand. This means that this knowledge makes it easy to get a foothold in tech.
You can choose your path based on your interests or the knowledge you already have. For example, if you’ve always been curious about finance, you can find an entry-level job as a quantitative analyst, otherwise known as a quant. These people handle statistical analysis, financial modeling, and engage in data-driven research.
But instead of listing out and describing each entry-level position out there, we’ll get to the point. Each of the jobs above offers an entry-level version. Each industry that is clamoring for data scientists will be more than happy to welcome a newly-minted data scientist into its ranks. In fact, many companies will then actively cultivate you by covering training, certification, and continuous learning.
So, if you have an industry you’re passionate about and you have data science in your background, you’ll most likely find a way to align your interests and skills.
Data science salaries
How much does a data scientist make?
In most situations, a data scientist salary will keep you fairly comfortable. But let’s get more specific: how much do data scientists make? Keep reading to find out.
Data scientist average salary
Data scientist salaries are, on average, quite good. In fact, according to Indeed at the time of this writing, the average salary for data scientists was $124,987. We can break that down just a bit more, though. The site also says that entry-level roles in the field earn, on average, $105,701. And then, once people have 3 to 5 years of experience, their earnings rise to $144,025.
This six-figure salary is backed up by Glassdoor, which estimates an average salary of $113,394. Likely, the slight discrepancy comes down to the data sets analyzed. In either case, though, you can see that this is a field that offers great compensation.
And this doesn’t even count the additional benefits that companies often offer to entice top-notch data scientists to join their teams.
Salaries by data science role
But let’s take this one step more granular. Check out the chart below to find out what you can expect from a data scientist, machine learning engineer, AI engineer, or research scientist salary. All data is sourced from Glassdoor as of November 3rd, 2023.
How to become a data scientist
Learn data science skills
Every data science career path starts somewhere. And, like with any specialization, the first thing to do is to get the skills down. There are a few ways to go about this, though, so let’s check them out.
Do you need a degree?
No.


As in most professions in tech, you don’t need an undergraduate education in data science to land a job in the field. Data science online degree programs have proliferated, but there are more ways to get into the field than by spending years studying.
For example, free resources abound. Many universities have uploaded free data science courses, and you can find fairly in-depth data science tutorials on YouTube. If these resources work for you, go for them. If you find good ones, they can give you a proper introduction to the field.
If you can build confidence, consistently review your progress to refine your skills, launch portfolio-building projects, and network all on your own, then this is a great way to start a career in data science. If that seems unrealistic or if you’d like to find another degree-free way to gain the skills and knowledge that can land you a job in data science, check out the next section.
Data scientist bootcamp
If you’re interested in data science, the quickest and most reliable way to launch a career is through a data science bootcamp. These big data courses will guide you through the essentials that will set you up to land your first job. 
Experts in the field curate lessons at data camps so that you know the education you’re getting is useful, relevant, and up-to-date. They’ll give you the precise skills you’ll find yourself using when you land a job in tech.
And education at a bootcamp goes further than just the tech skills. A good program will also help you hone your resume, build out a portfolio, and practice interviewing. They’ll make sure that you have tech know-how as well as job-hunting skills as you meet and interact with hiring managers.
It goes beyond that, too. You’ll learn within sprints, two- to three-week periods during which you can approach tasks as you see fit. This perfectly mirrors how work is done in tech at large. Further, bootcamps have networking baked in — you’ll meet a huge range of people and even gain access to job placement opportunities.
Data science certifications
Once you have the basics down, you can augment your career growth by going for a data science certificate. A data science certification course will both boost and validate your skills.
Take the Microsoft Certified: Azure Data Scientist Associate exam, for example. By preparing for it, you will deepen and lock in your knowledge of Azure machine learning and MLflow. Then, once you pass it, you’ll have a shiny new entry in your resume.
But this isn’t the only certification out there. There’s also the Certified Data Science Professional program from the United States Data Science Institute. And there are also data analytics certifications that you might also find relevant.
In any case, once you find yourself more involved in data science, you can choose the certification to pursue. Do you want to explore a specific machine learning setup in more depth?
Do you want to emphasize your skills in a specific area? Whatever certifications you decide on, you can be sure they’ll help you in your career.
Data scientist job placement
This is usually where you can find the secret ingredient to a successful application. See, in addition to education and certification, hands-on experience is vital. Hiring managers want to bring on people who have proven that they know how to work in the field. And that means more than just having skills in Python or machine learning. There are approaches to work, soft skills, and nuances that are really best learned on-the-job.
That’s why we place so much emphasis on our externships. These are work placement opportunities in which, at the end of a student’s time at TripleTen, they get real-world experience at tech companies. For example, our students have worked on data analysis for video game localization, a landing page for a company tackling unrecyclable plastic, and a support widget for a teaching management system.
And all of these projects become part of our students’ portfolios. That means that, when your next boss is sorting through resumes of people who, like you, are fresh to the industry, you’ll already have a project and valuable know-how gained first-hand from the industry. You’ll have actual experience that you’ll be able to draw on when you begin your interviewing process. Then, once you land the job, you’ll already have an understanding of what’s expected of you and how to fulfill those expectations.
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Externship with TripleTen: Allcorrect Games
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Externship with TripleTen: EdTonomy
Our pitch to you
Our data science bootcamp will show you how to become a data scientist. We’ll set you up for data scientist jobs, research scientist jobs, artificial intelligence jobs, and more. In fact, we’re so confident in our data camp that if you don’t find yourself earning a data scientist salary within six months of graduation, you’ll get your money back.
So dive in. Check out our Data Science bootcamp, where you’ll master the data science skills that will get you the tech job you want. A rewarding, well-paid, and exciting career awaits.
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