Codecademy Data Science Career Path: Data Science is one of the fastest growing fields in tech. Get this dream job by mastering the skills you need to analyze data with SQL and Python. Then, go even further by building Machine Learning algorithms.
Become a Data Scientist With Codecademy Data Science Career Path

Codecademy Update Sent To Our Email: Data Science Career Path!

This week, we’ve updated our Data Science Career Path by adding a ton of new lessons, projects, and quizzes. If you’re unfamiliar, Paths are a more guided learning journey than our one-off courses. You choose your goal (to learn a skill, build a thing, transition careers) and we guide you through different languages, frameworks, and foundational concepts to get you to your desired outcome.

In this update we added a whopping 19 new projects to the Path, and we also added milestones focused on teaching you how to scrape and clean your own data sets. Knowing how to scrape and clean data is crucial to becoming a self sufficient data scientist because, in the real world, you’ll often have to find the data yourself and clean it to be usable.

Generally, Paths are a Pro-only feature, but the first milestone in Career Paths is free to all users. So check it out and see what you’re missing in Pro.

Includes:

SQL, Python 3, NumPy, pandas, matplotlib, scikit-learn, and more...

Experience: Beginners welcome

For those who want to:

Become a data scientist
Become a data analyst
Use data in your job

Codecademy is an online interactive platform that offers free coding classes in 12 different programming languages including Python, Java, JavaScript, Ruby, SQL, C++, and Sass, as well as markup languages HTML and CSS.

To start this Career Path, sign up for Codecademy Pro For Free

Bonus Video: 10 Tips for Working on a Dev Team

This is one of the biggest things that trips people up when they first transition from learning to doing, but it's true—coding is a team sport! Now you might be wondering, "How do I go from coding solo to working on a dev team?" Don't worry, Matt has you covered with ten quick tips for working with other people on coding projects.


Want to put this all into practice but haven't got a dev job yet?  Start a group project with your fellow learners!  Here's how:

Starting a Group Project

Group projects are one of the most useful things you can do with learning to code, and a key experience to help you to bridge the gap to coding professionally on teams.  But how to actually work on a group project is a more complex question than it may seem.  In this video, Stephanie shares some tips on how to get started.


Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. Data science is the same concept as data mining and big data: "use the most powerful hardware, the most powerful programming systems, and the most efficient algorithms to solve problems".

Data science is a "concept to unify statistics, data analysis, machine learning and their related methods" in order to "understand and analyze actual phenomena" with data. It employs techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, and information science.

Turing award winner Jim Gray imagined data science as a "fourth paradigm" of science (empirical, theoretical, computational and now data-driven) and asserted that "everything about science is changing because of the impact of information technology" and the data deluge. In 2015, the American Statistical Association identified database management, statistics and machine learning, and distributed and parallel systems as the three emerging foundational professional communities.

In 2012, when Harvard Business Review called it "The Sexiest Job of the 21st Century", the term "data science" became a buzzword. It is now often used interchangeably with earlier concepts like business analytics, business intelligence, predictive modeling, and statistics. Even the suggestion that data science is sexy was paraphrasing Hans Rosling, featured in a 2011 BBC documentary with the quote, "Statistics is now the sexiest subject around." Nate Silver referred to data science as a sexed up term for statistics.

In many cases, earlier approaches and solutions are now simply rebranded as "data science" to be more attractive, which can cause the term to become "dilute[d] beyond usefulness." While many university programs now offer a data science degree, there exists no consensus on a definition or suitable curriculum contents. To its discredit, however, many data-science and big-data projects fail to deliver useful results, often as a result of poor management and utilization of resources.