Learn the fundamentals of data science in our 10-week, part-time course

Offered on nights and weekends, our 60-hour course provides a thorough introduction to data science skills — programming in Python, SQL, data visualization, and statistical analysis.

Advance Your Skills

Our intro course is perfect for those looking to bring the combination of advanced data analysis and programming into their everyday skill set.

Part-Time Flexibility, Full-Time Rigor

Our three-hour data science classes meet twice a week at Financial District campus and are designed for busy professionals on the move. Benefit from a robust curriculum without the commitment of a full-time course.

Uncompromising Education

Our 10-week course is the most thorough and accessible you’ll find teaching the foundational skills all data scientists use to do their jobs. Benefit by learning in-person with a dedicated and skilled instructor provided for every 12 students in the course.

Exceptional Community

Our students are surrounded by instructors and colleagues who support, challenge, and energize them. They push each other to do more, think bigger, and go further than they ever could on their own.

Who is this course for?

Who is this course for?

More and more companies are relying on data scientists to uncover insights and opportunities crucial to setting and achieving business goals. The profound need for individuals who understand how to identify a problem; pull, analyze, and model data; plus, communicate its results has never been greater.

Individuals or roles that will benefit from the course are:

  • Business/Data Analysts looking to leverage coding in Python & SQL to gather more data and automate their workflow
  • Developers or Engineers looking to develop statistical analysis skills or learn Python and data visualization
  • Anyone interested in:
    • Advancing their existing skill set for career growth opportunities
    • Using data to make informed decisions
    • Learning the fundamentals of programming and statistics for pleasure
    • Exploring data science as a potential career path

Know how to apply data science principles to real-world situations in less than three months

Over 10 weeks, students learn how to code in Python and SQL, use statistics to analyze data and take action against insights, create data visualisation libraries, learn advanced regression techniques, and show process and insights in a Jupyter notebook — a growing collaboration tool among data scientists for sharing code.

Additionally, students will complete two portfolio projects that will be available in GitHub in order to demonstrate technical proficiencies gained in the course.

Students considering or already exploring Python or Machine Learning will find this course to be a launching point towards a career in data science or continued studies in Flatiron School’s full-time, Immersive Data Science Bootcamp.


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Week 1: Intro to Programming for Data Science

Students begin the course with a dive into Python, one of the most popular programming languages utilized by data scientists. This weeklong introduction to data science in Python focuses on fundamental data types, and how they are used — including strings, booleans, variables, lists, and dictionaries.

Week 2: Basic Data Visualization + Collections

In week 2, students learn how to collect and visualize data using bar charts, histograms, scatter plots, and more. Students will also learn how to write code that repeats similar tasks with loops.

Week 3: Functions + Conditionals

Functions with and without arguments are examined in week 3 as students learn how to make them more flexible and predictable. Most importantly, students begin learning how to make decisions with data using the conditional statements if/else.

Week 4: Working with APIs + Scraping Data

In week 4, students explore working with APIs and how to pull data from an open source web library using JSON. Students then learn how to manipulate the data using the skills learned in weeks 1 through 3.

Week 5: Python Project

Students complete their first project in order to demonstrate proficiency with Python. Students will be posed with a problem that will need to be answered with a data visualization response that leverages all the skills learned in the first four weeks.

Week 6: Introduction to SQL + Relational Databases

Students are introduced to SQL — a common programming language used to communicate with databases. Basic terms, files, and data types are explored. Students also examine relational databases and various join statements when making an analysis across different tables of data.

Week 7: Python + SQL

It’s coming together — in week 7, students begin learning how to combine programming in Python with SQL through sorting/grouping data and complex join statements. Statistical analysis and terms are also introduced.

Week 8: Statistics: Distributions + Variables

In week 8, students take a deeper dive into statistics by exploring distributions, z-scores, and the central limit theorem. Students also examine the relationship between variables, correlation vs. causation, and how to detect and address multicollinearity.

Week 9: Linear Regression

Linear and advanced regression techniques are taught in week 9 as students go from learning how to perform a simple regression to regressions with multiple variables. How to interpret and clean regression outputs from standard industry tools such as Excel is explored.

Week 10: Advanced Regression Project

    In the final course project, students are encouraged to pose a question they would like to answer with data. Using open source data from the web, students will be required to clean the data, perform an advanced regression, draw a prediction, and communicate their analysis visually to their instructors and classmates.

    This final project will draw upon all the skills learned over the previous 9 weeks and be accessible in GitHub, providing students with the ability to share their process and insights with future and current hiring managers as a demonstration of acquired skills.

    “The best way to learn to code? Start building something today. There’s only so much you can get out of reading and watching videos about these concepts without actually doing some coding yourself.”

    Avi Flombaum

    Avi Flombaum

    Co-Founder & Dean
    Projects & Portfolio

    Start building your portfolio

    At Flatiron School, students learn data science by developing the infrastructure to store and organize data, and then providing analysis of that data. Students come away from this course with two portfolio projects to demonstrate their technical proficiency to current or future job managers, hiring leads, and coding bootcamp admissions teams.

    Both projects will be built and shareable via the student’s GitHub page (standard for technical interviews) and will leverage the popular version control system, “git.” Students push every line of code they write at Flatiron School to GitHub through our proprietary platform, Learn.co, giving them an extensive profile to show employers, co-workers, and fellow engineers.

    Project 1

    Students retrieve data from web either via scraping a JSON library or a web API. They then use their working knowledge of Python to organize data and deliver valuable insights with the Plotly visualization library.

    Project 2

    Students gather data from the web using Python and store and retrieve data using SQL. They then  use real-world statistical and visualization tools to deliver insights.


    Meet your teachers

    We take teaching seriously. Great teachers inspire us to connect with topics on a profound level. With experience in the field and the classroom, our data science instructors are unmatched. Simply put: students learn from the best.

    The Flatiron School Experience

    Part-time flexibility, full-time rigor

    For our Intro to Data Science course, we’ve applied the same rigorous coursework and discipline as with our flagship software engineering course, which has helped over 1,000 students get jobs. Benefit from a robust data science curriculum without the commitment of a full-time course.

    Real-Time Feedback

    Our class sizes are capped at 24 students with two teachers assigned to ensure students get the attention they need to answer the tough questions and maximize time while learning.

    Real Tools

    You can’t learn real skills without real tools. Students work in our Learn.co environment, the proprietary learning platform all Flatiron School students use to practice coding, which enables students to work with a git-based workflow.

    Ask a Question

    Confused? Our Ask a Question button within Learn.co allows students to crowdsource support from the entire student community. Even when not in class, you have support to find the answer you need.


    Begin your data science journey

    We don’t just admit individual students to our program; we curate a community, cultivating a diversity of thought and experience.

    1. Apply

    Submit a written application. Tell us about yourself and why you want to join our community.

    2. Admissions Review

    An Admissions Advisor will review your application and reach out with any questions and/or upon your acceptance to the course.

    3. Confirm Enrollment

    Expect to receive a response from our Admissions Team within 48 business hours after completing your application with instructions on how to complete enrollment in the course.

    Program Dates

    Note: Monday/Wednesday cohorts: November, February. Tuesday/Thursday cohorts: January, March. All classes meet from 6:00pm–9:00pm local time.
    Tuition & Logistics

    Rigorous education at an accessible tuition

    Pay Upfront
    Growth Scholarship
    Introducing the Growth Scholarship
    For a limited time — students who enroll are eligible to apply for our Growth Scholarship, which reduces the cost of tuition by over 50%. Eligibility granted to students who apply for the first 15 spots available in each cohort. To be considered, complete your application today and mark “yes” on the Growth Scholarship submission.

    You have questions; we have answers

    Apply Now

    Learn the fundamentals of data science in our 10-week, part-time course.

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