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Career Paths

• Data Analyst in R

• Data Analyst in Python

• Data Scientist in Python

• Data Engineer

Skill Paths

• SQL Fundamentals

• Python Basics for Data Analysis

• R Basics for Data Analysis

• Data Analysis and Visualization with Python

• Data Visualization with R

• APIs and Web Scraping with Python

• APIs and Web Scraping with R

• Machine Learning Introduction with Python

• Machine Learning Intermediate with Python

• Probability and Statistics with Python

• Probability and Statistics with R

• Data Cleaning with Python

Programming Language Courses

• Python for Data Science

• R for Data Science

• SQL for Data Science

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Courses Related to Python

• Python for Data Science: Fundamentals Part I
  
• Python for Data Science: Fundamentals Part II
• Python for Data Science: Intermediate
• Pandas and NumPy Fundamentals
• Data Visualization Fundamentals
• Storytelling Data Visualization and Information Design
• APIs and Web Scraping in Python
• Data Cleaning and Analysis
• Statistics Fundamentals
• Intermediate Statistics: Averages and Variability
• Probability: Fundamentals
• Conditional Probability
• Machine Learning Fundamentals
• Calculus For Machine Learning
• Linear Algebra For Machine Learning
• Linear Regression For Machine Learning
• Decision Trees
• Deep Learning Fundamentals
• Machine Learning Project
• Kaggle Fundamentals
• Machine Learning in Python: Intermediate
• Hypothesis Testing: Fundamentals
• Data Cleaning in Python: Advanced
• Data Cleaning Project Walkthrough
• Python for Data Engineering: Fundamentals Part I
• Python for Data Engineering: Fundamentals Part II
• Python Intermediate for Data Engineering
• Programming Concepts with Python
• Elements of the Command Line
• Text Processing in the Command Line
• Data Analysis in Business
• Functions: Advanced
• Command Line: Intermediate
• Git and Version Control
• Spark and Map-Reduce

Courses Related to R

• Introduction to Data Analysis in R

• Data Structures in R

• Control Flow, Iteration and Functions in R

• Specialized Data Processing in R: Strings and Dates

• Data Visualization in R

• APIs in R

• Web Scraping in R

• Statistics Fundamentals in R

• Statistics Intermediate in R: Averages and Variability

• Probability: Fundamentals in R

• Conditional Probability in R

• Hypothesis Testing in R

• Data Cleaning in R

• Data Cleaning in R: Advanced

• Linear Regression Modeling in R

• Machine Learning Fundamentals in R

• Introduction To Shiny

Courses Related to SQL

• SQL Fundamentals

• Introduction to SQL and Databases

• Intermediate SQL

• Intermediate SQL in R

• Filtering and Sorting Data in SQL

• Summarizing Data in SQL

• Combining Tables in SQL

Courses Related to Data Engineering

• Algorithm Complexity

• Postgres for Data Engineers

• Optimizing Postgres Databases

• Numpy for Data Engineers

• Processing Large Datasets In Pandas

• Parallel Processing

• Data Structures Fundamentals

• Recursion and Trees

• Building a Data Pipeline

Lessons

• Multi category chi-squared tests

• Challenge: Cleaning Data

• Significance Testing

• Challenge: Working with the Reddit API

• Challenge: Working with the Command Line

• Git Remotes

• Git Branches

• Merge Conflicts

• Challenge: Data Munging Using The Command Line

• Data Cleaning and Exploration Using Csvkit

• Project: Spark Installation and Jupyter Notebook Integration

• Guided Project: Git Installation and GitHub Integration

• Overfitting

• Machine Learning Project Walkthrough: Data Cleaning

• Machine Learning Project Walkthrough: Preparing the features

• Machine Learning Project Walkthrough: Making Predictions

• Data Cleaning Walkthrough

• Data Cleaning Walkthrough: Combining the Data

• Data Cleaning Walkthrough: Analyzing and Visualizing the Data

• Introduction to K-Nearest Neighbors

• Multivariate K-Nearest Neighbors

• Hyperparameter Optimization

• Cross Validation

• Guided Project: Predicting Car Prices

• Evaluating Model Performance

• Understanding Linear and Nonlinear Functions

• Understanding Limits

• Finding Extreme Points

• Linear Systems

• Vectors

• Matrix Algebra

• Optimizing Dataframe Memory Footprint

• Processing Dataframes in Chunks

• Guided Project: Practice Optimizing Dataframes and Processing in Chunks

• Augmenting Pandas With SQLite

• Guided Project: Analyzing Startup Fundraising Deals from Crunchbase

• Guided Project: Analyzing Stock Prices

• Solution Sets

• Joining Data in SQL

• Getting Started with Kaggle

• Feature Preparation, Selection, and Engineering

• Model Selection and Tuning

• Guided Project: Creating a Kaggle Workflow

• Intermediate Joins in SQL

• Building and Organizing Complex Queries

• Guided Project: Answering Business Questions using SQL

• Table Relations and Normalization

• Variables and Data Types

• Guided Project: Star Wars Survey

• Guided Project: Winning Jeopardy

• Guided Project: Predicting Bike Rentals

• Guided Project: Analyzing NYC High School Data

• The Linear Regression Model

• Feature Selection

• Gradient Descent

• Ordinary Least Squares

• Processing And Transforming Features

• Guided Project: Predicting House Sale Prices

• Representing Neural Networks

• Nonlinear Activation Functions

• Hidden Layers

• Guided Project: Building A Handwritten Digits Classifier

• Intro to Postgres

• Creating Tables

• Prepared Statements and SQL Injections

• Loading and Extracting Data with Tables

• User and Database Management

• Project: Postgresql Installation

• Guided Project: Storing Storm Data

• Introduction to SQL

• Summary Statistics

• Group Summary Statistics

• Subqueries

• Querying SQLite from Python

• Guided Project: Analyzing CIA Factbook Data Using SQL

• Exploring Postgres Internals

• Debugging Postgres Queries

• Using an Index

• Vacuuming Postgres Databases

• Advanced Indexing

• Functional Programming

• Pipeline Tasks

• Building a Pipeline Class

• Multiple Dependency Pipeline

• Guided Project: Hacker News Pipeline

• Creating Line Graphs

• Creating Multiple Line Graphs

• Bar Charts, Histograms, and Box Plots

• Scatter Plots for Exploratory Analysis

• Guided Project: Analyzing Forest Fire Data

• Guided Project: Answering Business Questions using SQL

• Sampling

• Variables in Statistics

• Frequency Distributions

• Visualizing Frequency Distributions

• Comparing Frequency Distributions

• Guided Project: Investigating Fandango Movie Ratings

• Introduction to NumPy

• Boolean Indexing with NumPy

• Introduction to pandas

• Exploring Data with Pandas Intermediate

• Data Cleaning Basics

• Guided Project: Exploring Ebay Car Sales Data

• The Mean

• The Weighted Mean and the Median

• The Mode

• Measures of Variability

• Z-scores

• Guided Project: Finding the Best Markets to Advertise In

• Programming in Python

• Lists and For Loops

• Conditional Statements

• Dictionaries and Frequency Tables

• Functions – Fundamentals

• Functions – Intermediate

• Data Cleaning With R

• String Manipulation and Relational Data

• Correlations and Reshaping Data

• Dealing With Missing Data

• Guided Project: NYC Schools Perceptions

• Python Data Analysis Basics

• Data Aggregation

• Combining Data With Pandas

• Transforming Data With Pandas

• Working With Strings In Pandas

• Working With Missing And Duplicate Data

• Guided Project: Clean And Analyze Employee Exit Surveys

• Learn and Install Jupyter Notebook

• Guided Project: Profitable App Profiles for the App Store and Google Play Markets

• Cleaning and Preparing Data in Python

• Object-Oriented Python

• Working with Dates and Times in Python

• Regular Expressions Basics

• List Comprehensions and Lambda Functions

• Guided Project: Exploring Hacker News Posts

• Table Relations and Normalization

• Querying SQLite from R

• Advanced Regular Expressions

• Working with Missing Data

• Joining Data in SQL

• Intermediate Joins in SQL

• Building and Organizing Complex Queries

• Guided Project: Answering Business Questions using SQL

• Table Relations and Normalization

• Guided Project: Designing and Creating a Database

• Estimating Probabilities

• Probability Rules

• Solving Complex Probability Problems

• Permutations and Combinations

• Exploring Data With pandas: Fundamentals

• Mobile App for Lottery Addiction

• Introduction to the Command Line

• The Filesystem

• Glob Patterns and Wildcards

• Users and Permissions

• Getting Help and Reading Documentation
• File Inspection
• Text Processing
• Redirection and Pipelines
• Standard Streams and File Descriptors
• Simple Random Sampling
• Stratified Sampling
• Variables in Statistics
• Frequency Distributions
• Visualizing Frequency Distributions
• Comparing Frequency Distributions
• Regular Expressions Basics
• Advanced Regular Expressions
• Map and Anonymous Functions
• Working with Missing Data
• Working with Dates and Times in Python
• Guided Project: Exploring Hacker News Posts
• Estimating Probabilities
• Probability Rules
• Probabilities of Multiple Random Experiments
• Permutations and Combinations
• Mobile App for Lottery Addiction
• Guided Project: Investigating Fandango Movie Ratings
• Best Practices for Writing Functions
• Context Managers
• Introduction to Decorators
• Decorators: Advanced
• Programming in Python
• Variables and Data Types
• Lists and For Loops
• Conditional Statements
• Dictionaries
• Functions: Fundamentals
• Functions: Intermediate
• Project: Learn and Install Jupyter Notebook
• Guided Project: Profitable App Profiles for the App Store and Google Play Markets
• Conditional Probability: Fundamentals
• Conditional Probability: Intermediate
• Bayes Theorem
• The Naive Bayes Algorithm
• Guided Project: Building A Spam Filter With Naive Bayes
• Python Data Analysis Basics
• Object-Oriented Python
• Probability Distributions
• Hypothesis Testing
• Categorical Data and The Chi-Squared Test
• Multi category chi-squared tests
• Guided Project: Winning Jeopardy
• The Mean
• The Weighted Mean and the Median
• The Mode
• Measures of Variability
• Z-scores
• Guided Project: Finding the Best Markets to Advertise In
• Binary And Positional Number Systems
• Encodings and Representing Text In A Computer
• Reading And Writing To Files
• Memory and Disk Usage
• Fundamentals of Modeling in R
• Bivariate Relationships — Correlation and Scatterplots
• Estimating the Coefficients and Fitting Linear Models
• Assessing the Accuracy of the Model
• Fitting Many Linear Models
• Guided Project: Predicting Condominium Sale Prices
• Joining Data in SQL
• Intermediate Joins in SQL
• Building and Organizing Complex Queries
• Fuzzy Language in Data Science
• Communicating Results
• Business Metrics
• Guided Project: Popular Data Science Questions
• Conditional Probability: Fundamentals
• Conditional Probability Continued
• Bayes’ Theorem
• The Naive Bayes Algorithm
• Guided Project: Building A Spam Filter With Naive Bayes in R
• Time Complexity of Algorithms
• Constant Time Complexity
• Logarithmic Time Complexity
• Sorting Algorithms
• Space Complexity
• Building Fast Queries on a CSV
• Introduction to Machine Learning Concepts
• Evaluating Model Performance
• Multivariate K-Nearest Neighbors in R
• Cross Validation in R
• Hyperparameter Optimization in R
• Guided Project: Predicting Car Prices
• Dataframes in R
• Control Flow in R
• Functions in R
• String Manipulation in R: Fundamentals
• Date and Time Manipulation in R: Fundamentals
• Guided Project: Creating An Efficient Data Analysis Workflow
• Introduction to Programming in R
• Data Manipulation with R: Basics
• Guided Project: Installing RStudio
• Vectors in R
• Matrices in R
• Lists in R
• Guided Project: Investigating COVID-19 Virus Trends
• Introduction to NumPy
• Arithmetic with NumPy Arrays
• Broadcasting NumPy Arrays
• Datasets and Boolean Indexing
• NumPy Datatypes
• Arithmetic Expressions and Variables in R
• Iterations in R
• Map Function in R
• Guided Project: Creating An Efficient Data Analysis Workflow (Part 2)
• Logical Expressions in R
• Working with APIs
• Line Charts
• Multiple plots
• Bar Plots And Scatter Plots
• Guided Project: Visualizing Earnings Based On College Majors
• Improving Plot Aesthetics
• Color, Layout, and Annotations
• Guided Project: Visualizing The Gender Gap In College Degrees
• Conditional Plots
• Intermediate APIs
• Guided Project: Implementing a Key-Value Database
• Web Scraping
• Processing Tasks With Stacks and Queues
• Effectively Using Arrays and Lists
• Sorting Arrays And Lists
• Searching Arrays And Lists
• Hash Tables
• CPU Bound Programs
• I/O Bound Programs
• Overcoming The Limitation of Threads
• Quickly Analyzing Data With Parallel Processing
• Guided Project: Analyzing Wikipedia Pages
• Histograms And Box Plots
• Overview of Recursion
• Introduction to Binary Trees
• Working with Binary Search Trees
• Implementing a Binary Heap
• Performance Boosts of Using a B-Tree
• Performance Boosts of Using a B-Tree II
• Introduction to Spark
• Transformations and Actions
• Challenge: Transforming Hamlet into a Data Set
• Guided Project: Predicting the stock market
• Working with Jupyter console
• Piping and redirecting output
• Introduction to Decision Trees
• Building a Decision Tree
• Spark DataFrames
• Applying Decision Trees
• Spark SQL
• Introduction to Random Forests
• Working with Programs
• Command Line Python Scripting
• Introduction to Git
• Chi-squared tests