Data Scientist in Python

Learn how to make inferences and predictions from data.

Python

SQL

Machine Learning

This is a Dataquest career path — a sequence of interactive data science courses that’s designed to take you from total beginner to qualified data scientist. You’ll learn to write and run real code, all from the comfort of your browser window.

Made for: Beginners
Prerequisites: None

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Your New Skills

You’ll learn all this and much more in our interactive data science courses:

  • Python programming
  • SQL queries
  • Data analysis and visualization
  • Data mining, web scraping, and APIs
  • Probability and statistics
  • Machine learning
  • Deep learning
  • Jupyter Notebooks
  • Git
  • Command line/bash

Your New Portfolio

Predicting the Stock Market

Use machine learning to predict future stock prices

Predicting Home Sale Prices

Use linear regression to predict real estate prices

Performing Market Analysis

Use statistical analysis to identify market opportunities

Analyzing SAT Fairness

Use demographic data and SAT scores to assess testing bias

Building a Handwritten Digits Classifier

Use deep learning to build a system for reading handwritten numbers

Building a Custom Spam Filter

Filtering spam email messages using Naive Bayes

+ many more projects

Start working toward your new career in Data Science.

Start working toward your new career in Data Science.

Start working toward your new career in Data Science.

Learn Interactively

In this career path, our courses will help you learn Python fundamentals, dig into data analysis and data viz, query databases with SQL, study statistics, and build machine learning models in a thoughtful sequence, with each lesson building on the previous one.

Our courses are designed so that there are no prerequisites and no prior experience required. Everything you need to learn, you'll learn on this path!

As you learn, you'll apply each concept immediately by writing code right in your browser that's automatically checked by our system to give you near-instant feedback on your progress.

Learn more about the Dataquest teaching method, and how we differ from other online learning sites.   

Become a Data Scientist

This course path covers all of the technical skills you’re likely to need to work as a data scientist, and we’re adding new courses all the time!

Dataquest learners like Francisco, Caitlin, Isaac, Adam, Sunishchal, and many more have used this path to go from working in totally unrelated fields to working as full-time data scientists. And it’s not just about getting a job — Francisco, who got his first data science job four years ago after using Dataquest, says he’s still using his Dataquest skills every day.

Don’t take our word for it! Read what real Dataquest learners have to say about how Dataquest has impacted their lives.


Choosing the Right Data Science Courses

There are lots of options out there for learning data science. Online learning platforms  and MOOCs like Coursera, edX, Udemy, etc. tend to be based on "traditional" education, using video lectures to replace in-person ones. Even interactive platforms like Datacamp still haven't fully escaped from the "video lecture" model.

While video lectures work well for some people, Dataquest's no-video approach is based on science that says learners who go hands-on are more likely to succeed than those who learn via lectures.

Another reason we don't teach with videos is that in real-world data science work, you'll frequently be challenged to learn about a new package, function, API, etc. by reading documentation. The vast majority of technical documentation is available in text only, which means that the most effective data scientists need to be well-practiced at reading about these kinds of technical topics. Our platform is designed to help you hone those technical reading skills gradually, rather than teaching you with videos and then tossing you into the documentation deep end when you start your first data science job!

Dive into Data Science and Get Certified!

Sign up for a free account to try any course in this path. To complete courses and earn certificates, you’ll need a Premium subscription, which unlocks all of our course content, practice problems, guided projects, and even access to our Premium-only career forum to help you navigate your path towards becoming a data scientist.

Full List of Data Science Courses:

Python for Data Science: Fundamentals

Learn the basics of Python programming and data science.

Python for Data Science: Intermediate

Learn the basics of Python programming and data science.

Pandas & NumPy Fundamentals

Learn how to analyze data using the pandas and NumPy libraries.

FREE + BASIC

Exploratory Data Visualization

Learn how to explore data by creating and interpreting data graphics. This course is taught using matplotlib and pandas.

Storytelling Through Data Visualization

Learn how to communicate insights and tell stories using data visualization.

Data Cleaning and Analysis

Learn how to clean and combine datasets, then practice your skills.

Data Cleaning in Python: Advanced

Learn advanced techniques for cleaning data in Python.

Data Cleaning Project Walkthrough

Learn how to clean and combine datasets, then practice your skills.

Elements of the Command Line

Learn the basics of the Bash to establish a foundation of working the command line as a springboard to using the command line for data science

Text Processing in the Command Line

Learn more about the command line and how to use it in your data science workflow.

SQL Fundamentals

Learn the basics of working with SQL databases.

Intermediate SQL for Data Analysis

Learn to work with multi-table databases.

APIs and Web Scraping in Python

Learn how to acquire data from APIs and the web.

Statistics: Fundamentals

Learn about sampling, variables and distributions.

Statistics Intermediate: Averages & Variability

Learn to summarize distributions, measure variability using variance or standard deviation, and compare values using z-scores.

Probability Fundamentals

Learn the fundamentals of probability theory using Python

Conditional Probability

Learn about conditional probability, Bayes' theorem, and Naive Bayes.

Hypothesis Testing: Fundamentals

Learn more advanced statistical concepts including A/B tests and chi-squared tests for more powerful data analysis.

Machine Learning Fundamentals

Learn the fundamentals of machine learning using k-nearest neighbors.

Calculus for Machine Learning

Learn the calculus necessary for intermediate machine learning techniques like linear regression.

Linear Algebra for Machine Learning

Learn the linear algebra necessary for intermediate machine learning techniques like linear regression.

Linear Regression for Machine Learning

Learn how to use the linear regression machine learning model.

Machine Learning in Python: Intermediate

Dive more into Machine learning.

Decision Trees

Learn how to construct and interpret decision trees.

Deep Learning: Fundamentals

Learn the basics of deep neural networks. Includes graph representation, activation functions, multiple hidden layers, and image classification.

Machine Learning Project

Learn what a complete data science project looks like, from data cleaning to machine learning.

Kaggle Fundamentals

Learn how to get started with and participate in Kaggle competitions with Kaggle's 'Titanic' competition.

Functions: Advanced

Learn how to write high-quality functions.

Command Line: Intermediate

Learn more about the command line and how to use it in your data analysis workflow.

Git & Version Control

Learn the basics of Python programming and data science.

Spark & Map-Reduce

Learn how to use Apache Spark and the map-reduce technique to clean and analyze large datasets.

Our Data Science Course Curriculum

As you can see above, our data science curriculum includes all of the required skills for data analysis and data science, starting from scratch. You'll learn Python programming, including mastering key libraries like pandas, numpy, matplotlib, and scikit-learn, as well as key concepts like both object-oriented and functional programming. You'll learn to master writing SQL queries including a variety of joins, union, and much more, including complex queries. You'll master web scraping with Python and BeautifulSoup, and learn to access new data sets using APIs.

You'll also learn the math and statistics required to perform accurate data analysis and run meaningful tests, as well as the calculus needed to understand what's happening under the hood with machine learning algorithms. By the end of the path, you'll be able to do things like explain Bayes' theorem, or use a chi-squared test. And of course, you'll learn how to use all of the most popular machine learning algorithms in scikit-learn to perform ML tasks including linear regression, deep learning, image recognition, predictive analytics, and much more.

But you'll learn much more than just those mandatory technical skills. You'll learn how to use the UNIX command line to streamline your workflow, and how to use Git and Github to enable collaboration between and across teams — skills that'll help set you a part as an effective and efficient data scientist.

You Don't Need a Degree to Succeed!

Earning an undergraduate or Masters degree in data science is a laudable accomplishment. But it is not required to get a job in data science!

And thank goodness it isn't, because degree programs are tremendously expensive. For example:

  • A Purdue University Masters in Data Science will cost you more than $47,000 (out of state)
  • A University of Rochester MS in Data Science costs more than $51,000
  • Carnegie Mellon's Data Science Masters degree costs $25,000 per semester
  • Columbia University's MS in Data Science costs over $60,000

And of course, these programs also cost time — typically at least a full year of full-time study.

Some degree programs provide an excellent education. But even if you can afford to take on substantial debt and go a full year without any sort of income, is this the best way to invest your money and time?

If your goal is to work as a data scientist, probably not.

With such high demand for data scientists, hiring managers tend not to care much where or how you learned your skills. All they care about is that you actually have the skills required to do the job.

Thankfully, you'll finish the Dataquest data science course path with a portfolio projects that prove you do have those skills, and more!

So why spend $25,000 to $50,000 per year on a university degree when you could learn the same skills faster and more effectively on your own time with Dataquest, and still end up with the job in data science you're looking for?

Here, there's no application process. There's nothing you need to wait for. You don't even need a credit card to get started. Sign up now and start learning data science for free!