Start Your Career as a Data Scientist

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.
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?

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.
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?

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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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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