Exaltitude Data Roadmap: based on MIT Applied Data Science Program
Master the concepts and skills from the MIT Applied Data Science Certificate Program, entirely for FREE!
Hi, I’m Jean,
I’m the Founder and host of Exaltitude on YouTube. I was an early engineer at WhatsApp before its $19B acquisition by Meta, then led engineering teams at Meta, where I experienced firsthand how technology evolves and shapes the world.
Now, I’m on a mission to break down how AI is changing our careers, industries, and the future of work. AI will transform how we work, learn, and live, and many are overwhelmed by what this means, especially those without access or context.
My goal is to simplify these concepts and make them accessible to everyone, whether you’re starting in tech, pivoting careers, or just curious about AI’s impact. I’m here to bring credible, practical insights into the conversation, helping you not just keep up, but take advantage of AI opportunities.
No hype. No jargon. Just real-world insights for navigating AI and what’s next.
Stay connected for updates, industry insights, and career advice on LinkedIn and YouTube. Have questions? Reach out on my website!
Companion Video
Expected Timeline
While most students complete the MIT AI Graduate Certificate program in 12 weeks, 15 to 18 hours per week. When studying part-time, self-study can take significantly longer.
The amount of time it takes depends on various factors, including:
Your experience level: If you have a strong foundation in math, programming, and related fields, you may be able to progress faster.
Your dedication and time commitment: The more time you devote to studying, the quicker you can complete the program.
The depth of your learning: If you want to gain a deep understanding of each topic, you may need to spend more time.
Set realistic expectations and be patient with yourself. Remember that the goal is to learn and understand the material, not just to finish the program quickly.
Who is this for?
Individuals seeking a career transition into Data Science and Machine Learning
Professionals aiming to advance their Data Science and ML leadership skills
Entrepreneurs looking to leverage Data Science and ML for innovative solutions
Prerequisites
No prior programming or math experience is required. We’ll start from the basics and guide you through the entire journey, from understanding data to building complex machine-learning models.
Career Preparation and Guidance
This section is important because learning to code and getting a job are two different skills. Coding is about building your technical knowledge and creating projects, while getting a job is about writing a strong resume, networking, and preparing for interviews. Balancing both skill sets is key to achieving your career goals.
Included in the tuition of the MIT program is career coaching support. However, there are also free alternatives available to support your career journey!
Resume Writing:
The Ultimate Resume Handbook by Jean (paid): A comprehensive guide to crafting standout resumes tailored for tech roles. Also, download the free Ultimate Resume Template.
Developer Resume with ChatGPT for ATS Success by Jean on YouTube: Learn how to use ChatGPT effectively to optimize your resume for applicant tracking systems.
Engineering Resume Hack (from Big Tech Hiring Manager) by Jean on YouTube: Insider tips from Jean, a former hiring manager, to make your resume stand out in big tech.
Interviews:
Cracking the Coding Interview by Gayle L. McDowell (paid book)
Blind 75 Leet code questions by Leetcode
Python cheat sheet by Leetcode
DSA study guide by Leetcode
System Design Interview Survival Guide (2024): Strategies and Tips (blog)
Join FREE Monthly LinkedIn Live Q&A with Jean:
What it is: An open session where Jean answers your career-related questions live, completely free.
Why it matters: Gain expert insights on navigating the tech industry, job search strategies, and career development.
Next session: Follow Jean on LinkedIn to get updates on the newest events.
Job Market Insights:
Tech Salaries Trends for 2025 by Jean on YouTube: Stay informed about the latest salary trends in the tech industry.
Top Machine Learning Engineer Salary by Jean on YouTube: Explore the earning potential of ML roles in various industries.
Career Development:
What Color Is Your Parachute? By Richard N. Bolles (Paid book)
Zero to AI ML Engineer: Get Hired Without Experience by Jean on YouTube: A roadmap for breaking into AI/ML engineering without prior experience.
7 Mistakes that Ruin Your Career as a Junior Software Engineer by Jean on YouTube: Avoid common pitfalls that could derail your early career.
Study Guide
Module 1: Foundations
The first module in the program for applied Data Science begins with the foundations, which cover Python and Statistics foundations.
Part 1: Python
Python is a versatile programming language used for various applications, from web development to data science and machine learning.
Must learn topics: Arrays and Matrix
An array is a data structure that stores various elements or items at contiguous memory locations.
A matrix is a two-dimensional (2D) array where data (elements/items) is stored in the format of rows and columns.
Free Classes:
Jean’s Python Roadmap on Substack
Recommended Book: Automate the Boring Stuff - Chapter 4
Pandas is a commonly used library in Python that is used to analyze and manipulate data.
Free Classes/Resources: Pandas Tutorial
NumPy is a package in the Python library where you can use this package for scientific computing to work with arrays.
Classes/Resources: Jean’s NumPy Tutorial for Beginners on YouTube
Part 2: Probability and Statistics
Descriptive Statistics is a method that helps you study data analysis using multiple data sets by describing and summarizing them. For example, the data set can either be a collection of the population in a neighborhood or the marks a sample of 100 students achieved.
A Distribution is a statistical function used to report all the probable values that a random variable takes within a certain range.
Bayes Theorem is a mathematical formula that is named after Thomas Bayes. This theorem helps you determine conditional probability.
Inferential statistics is a method that lets you explore basic concepts of using data for estimation and assess theories with the help of Python.Free Classes:
Khan Academy’s Probability and Statistics
Jean’s Mathematics for Machine Learning video on YouTube
Recommended Book: A First Course in Probability, by Sheldon Ross, Pearson (paid resource)
Module 2: Data Analysis and Visualization
This module includes the essential topics on data analysis and visualization.
Visualization is the process of representing data and information in a graphical form.
Exploratory Data Analysis (EDA) enables you to uncover patterns and insights frequently with visual methods within some data.
💡Advanced Topics: Module 3-5
The next few modules cover advanced topics. While they’re not essential for every data science role, they can be valuable for those who want to specialize in machine learning. If you’re aiming to become a machine learning engineer or data scientist who builds complex models, then these modules are definitely for you.
If you’re more interested in roles like data analyst or data engineer, you might not need to dive deep into these topics. To learn more about different data roles and their requirements, check out my newsletter, “Demystifying Data Careers: Your Guide to Data Analyst vs Scientist vs Data Engineer vs ML Engineer,” where I’ve outlined the various career paths in data science.
