Flatiron Data Science Bootcamp - Curriculum

M1: Exploratory Data Analysis and Descriptive Statistics (Python for Data Science)

• M1S1: Getting Started with Data Science
• M1S2: Importing and Statistical Analysis of Data
• M1S3: Working with Pandas
• M1S4: Data Cleaning in Pandas
• M1S5: SQL and Relational Databases
• M1S6: Object Oriented Programming
• M1S7: OOP Continued
• M1S8: Numpy and Foundations of Probability and Combinatorics
• M1S9: Statistical Distributions
• M1S10: Introduction to Linear Regression
• M1S11: Multiple Regression and Model Validation
• M1S12: A Complete Data Science Project Using Multiple Regression

M2: Data Engineering for Data Science

• M2S11: JSON and XML
• M2S12: Accessing Data through APIs
• M2S13: HTML, CSS and Web Scraping
• M2S15: Other Database structures
• M2S16: Scraping and Storing your Data

M3: Probability, Sampling and AB Testing

• M3S17: Combinatorics and Probability
• M3S18: Statistical Distributions
• M3S19: Central Limit Theorem and Confidence Intervals

M4: Statistical Modelling

• M5S37: Principal Component Analysis

M5: Machine Learning and Big Data

• M4S31: Working with Time Series Data
• M4S32: Time Series Modelling

Jupyter Notebook cheatsheet

• shift enter: run cell, select below
• ctrl enter: run cell
• alt enter: run cell, insert below
• a: new cell above
• b: new cell below
• x: delete cell
• z: undo delete cell
• y: code cell
• m: markdown cell
• h: view shortcuts
• ctrl /: comment lines

Git cheatsheet

• git clone URL
• git commit -m MESSAGE
• git push
• git log