# Sitemap

**Career Paths**

**Skill Paths**

**Programming Language Courses**

**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**

**Courses Related to SQL**

SQL Fundamentals

Combining Tables in SQL

**Courses Related to Data Engineering**

**Lessons**

Project: Spark Installation and Jupyter Notebook Integration

Machine Learning Project Walkthrough: Preparing the features

Data Cleaning Walkthrough: Analyzing and Visualizing the Data

Guided Project: Practice Optimizing Dataframes and Processing in Chunks

Guided Project: Analyzing Startup Fundraising Deals from Crunchbase

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

- 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