Foundational Knowledge & Skills
The Building Blocks of Data Science Excellence
Overview
This section covers the fundamental concepts and skills required for a Data Science (Analytical) role at Meta. These building blocks form the foundation for all technical and analytical work you'll do.
At Meta, Data Scientists play a crucial role in driving product development and business strategy through rigorous data analysis and statistical reasoning. Working at scale with billions of users and petabytes of data, statistical rigor and technical proficiency are paramount.
1. Statistics & Probability
Master the statistical foundations essential for data-driven decision making at Meta. Learn descriptive statistics, probability distributions, hypothesis testing, regression analysis, and experimental design.
Key Topics: Mean, median, mode, normal distribution, A/B testing, p-values, confidence intervals, statistical power
2. SQL & Data Manipulation
Develop proficiency in SQL for querying, joining, and aggregating data. Master window functions and query optimization techniques.
Key Topics: JOINs, GROUP BY, window functions, CTEs, subqueries, query optimization
2a. Analytical Engineering
Turn raw data into trustworthy analytics tables with versioned SQL models, tests, and clear definitions.
Key Topics: facts/dims, incremental loads, SCD2, QA checks, semantic definitions
3. Programming (Python/R) for Data Analysis
Learn to use Python and R for data manipulation, analysis, and visualization. Master core libraries like Pandas, NumPy, Matplotlib, and Seaborn.
Key Topics: Pandas DataFrames, NumPy arrays, data cleaning, visualization, statistical modeling