Data science is the practical science of organizing data to extract valuable business insights. With the help of math, statistics, programming, and machine learning, data scientists operate with large amounts of data and make conclusions that could influence different groups of people.
There are many examples of when businesses did major overhauls with the help of data science. One is Ford, which took a $12.6 billion loss in 2006. After inviting in a chief data scientist, they examined real customer data and made quite the transformation —they sold over 2.3 million cars in three years (with profit!)
This profession is not only rewarding, but it is also currently in top demand. U.S. Bureau of Labor Statistics names data science among the fastest-growing job markets and estimates 11.5 million new data science jobs by 2026. (Guess what? This prediction was made using data science.)
Studying data science takes dedication: we recommend reserving at least 20 hours weekly for a comfortable learning process. This specialization is open to anyone with some prior coding experience.
TripleTen’s goal: After nine months, you will be a data scientist with fundamental knowledge of the field, skills in Python, Pandas, machine learning, and neural networks, and a median salary of $89,300.
Is data science really for me?
Good news: you don’t need to be a genius in calculus to break into DS. According to TripleTen career specialists, data science is a perfect field for those who have patience and a desire to discover hidden patterns. Strongly independent thinkers will enjoy this profession for learning strategies to ask the right questions that lead to insightful answers.
Being critical and having an eye for detail are amongst other essential skills. One of the primary tasks of a data scientist is to avoid bias or misleading conclusions due to incorrect data collection.
As a data scientist, you will have clear tasks and shaped outcomes to achieve. It’s a profession for those who enjoy independence and self-management, so being both motivated and disciplined are crucial.
Anybody who appreciates uncovering parallels and patterns, studying trends, making forecasts, automating tasks, and teaching computers to learn from their results may find data science appealing.
In my everyday life, whenever I watch Netflix and get movie recommendations, I tell my wife - this is data science in action! Chuks Okoli, TripleTen grad and Machine Learning Engineer at Leidos
How is the data science bootcamp organized?
The program is arranged in modules that run in two-week sprints with projects at the end of each module. Upon graduation, you will have 17 projects in your portfolio to show to recruiters.
Studies are held on an online platform built to train your practical skills as you learn. You can also rely on instant feedback from tutors and code reviewers.
Course duration: 35 weeks, or 17 sprints.
The final project takes up to two weeks or 40 hours.
After day one, you will know the strengths and potential of data science and be able to see real cases of work that are optimized, automated, and calculated with the help of data scientists.
After one month, you will be familiar with the fundamentals of Python. You will know data types and feel comfortable with arithmetic operations and functions. You will use Pandas library for data analysis and Jupyter Notebook for code writing and graphs.
After three months, you will know the difference between exploratory and statistical data analysis and the best cases to use them. You will be able to set testing hypotheses and use data visualizations to present your findings.
After six months, your skills in data analysis will become advanced. You will be familiar with SQL language for data collection and storage, and look into the basics of machine learning. By creating your first training model and improving it over the course, you will learn to integrate business metrics and implement a project for a mining company.
After eight months, you will be ready to work with neural networks. You will master time series, make your model learn from data via unsupervised learning, understand speech with natural language processing, and many more amazing things.
Once you are ready to work on the final project, you will aggregate all the knowledge from the bootcamp to work on a real-life task with a fixed deadline and requirements.
Example of a task for the final project: A telecom operator would like to be able to forecast the churn of clients. If users plan to leave, they will be offered promotional codes and special plan options. The marketing team has collected some of their clientele’s data, including their plans and contracts. Your role is to analyze the churn rate and set predictions.
How do we support your future data science career?
TripleTen helps you enter a new career with all available resources. Each student undergoes a Career Prep course dedicated to the best practices of job searching: preparing a resume, writing cover letters, assembling a portfolio, networking, preparing for a technical interview, etc.
“Our mock interviews are a series of test interviews where we mimic a real job search environment,” says Ana Mineeva, TripleTen Career Product Lead. “These interviews are split into stages. The first one is the screening when you talk to the HR manager. The second one is the interview with the hiring manager. Next comes the technical interview, and at last, the soft skills interview.”
Career Acceleration is an additional course available to U.S. residents after graduation. It’s a support program for those who haven’t landed a job in data science yet. Relying on a career coach’s guidance, you will be able to determine your strengths, highlight your experience, shortlist companies, and prepare for conversations with your future employer.
Career acceleration is a more targeted approach to job finding. There’s a session with the career coach where they examine student's cover letter and hiring funnel.
One of the requirements to start the Career acceleration is to have a funnel: a google spreadsheet, or a report in Notion, saying how many proposals you’ve sent, to which companies, who the contact was, and which response you got. It helps us understand if the student is considering the right group of companies. Anna Mineeva, TripleTen Career Product Lead
Externships are also available to all Data Science Bootcamp students. The team gives you real-life work cases to accomplish and enrich your portfolio. Externships create an actual setting you’ll encounter when working for a company, and employers value this type of experience.
Why should I choose TripleTen?
A realistic and fundamental approach. You cannot learn data science in three months, no matter what some courses claim.
The bootcamp was a challenge -- eight months. But I’d rather be a data scientist in eight months than do a bootcamp in six weeks and try to be a data scientist without even knowing what I’m doing. Chuks Okoli, TripleTen grad
Study with industry professionals. All TripleTen tutors are real data scientists. Their curiosity about the latest trends and innovations makes their classes highly practical and focused on valuable results.
Career support that no one else provides. With the help of the TripleTen Career Team, you can start your job search and count on maximum support in preparing your portfolio and resume. If you still haven’t decided if data science is for you, book a call with our advisor to resolve all doubts before enrolling.