Python for Data Analysis

Build the Python foundation you need for analysis. This module focuses on the language features that come up constantly when working with data: core data types, collections such as list and dict, control flow, and writing reusable functions.

• 1.1 Variables, Numbers, Strings, and Booleans
  Preview 28:40
• 1.2 Lists, Tuples, Sets, and Dictionaries
  44:15
• 1.3 Conditionals and Loops
  38:22
• 1.4 Functions and Comprehensions
  41:05
• 1.5 Working with Files and Modules
  26:18
• 1.6 Practice: Summarizing a List of Records
  52:30

Set up a professional, reproducible workflow. You'll install the stack with Anaconda, learn to work efficiently in Jupyter notebooks, manage environments, and understand how NumPy, pandas, Matplotlib, and Seaborn fit together.

• 2.1 Installing Anaconda and Managing Environments
  31:50
• 2.2 Working in Jupyter Notebooks and JupyterLab
  36:14
• 2.3 Cells, Markdown, and Magic Commands
  27:09
• 2.4 Tour of the Stack: NumPy, pandas, Matplotlib, Seaborn
  24:33
• 2.5 Practice: Your First Analysis Notebook
  45:20

Learn the array, the building block beneath pandas. You'll create and reshape ndarray objects, slice and index them, and replace slow Python loops with fast vectorized operations and broadcasting.

• 3.1 Creating and Inspecting ndarrays
  33:42
• 3.2 Indexing, Slicing, and Reshaping
  39:55
• 3.3 Vectorized Operations and Broadcasting
  42:11
• 3.4 Aggregations and Boolean Masking
  35:28
• 3.5 Random Numbers and Simulation Basics
  29:47
• 3.6 Practice: Vectorizing a Calculation
  48:05

Meet the workhorse of Python data analysis. You'll build Series and DataFrame objects, select rows and columns with loc and iloc, filter with boolean conditions, and add or rename columns.

• 4.1 Series, DataFrames, and the Index
  40:33
• 4.2 Selecting Data with loc and iloc
  46:50
• 4.3 Boolean Filtering and Conditional Selection
  38:17
• 4.4 Adding, Renaming, and Dropping Columns
  34:09
• 4.5 Sorting and Summary Statistics
  30:25
• 4.6 Practice: Exploring a DataFrame
  55:40

Get data into pandas from wherever it lives and make it usable. You'll read CSV and Excel files, query SQL databases, pull JSON from REST APIs, and then handle missing values, duplicates, and inconsistent types with fillna, dropna, and type conversion.

• 5.1 Reading CSV and Excel Files
  37:12
• 5.2 Querying SQL Databases with pandas
  41:30
• 5.3 Fetching Data from REST APIs (JSON)
  39:48
• 5.4 Handling Missing Values and Duplicates
  43:55
• 5.5 Fixing Data Types and Text Cleanup
  32:20
• 5.6 Practice: Cleaning a Messy Dataset
  58:10

Reshape and summarize data to answer questions. You'll apply functions across columns, split-apply-combine with groupby, build pivot tables, and join multiple datasets with merge and concat.

• 6.1 Transforming Columns with apply and map
  36:40
• 6.2 Grouping and Aggregating with groupby
  44:22
• 6.3 Pivot Tables and Crosstabs
  38:15
• 6.4 Joining Data with merge and concat
  42:08
• 6.5 Practice: Summarizing Sales by Category
  52:35

Turn analysis into clear visuals. You'll build line, bar, scatter, and histogram plots with matplotlib.pyplot, create statistical charts with Seaborn, and learn to label, style, and export figures that communicate findings.

• 7.1 Matplotlib Basics: Figures and Axes
  39:30
• 7.2 Line, Bar, Scatter, and Histogram Plots
  45:18
• 7.3 Statistical Plots with Seaborn
  41:52
• 7.4 Styling, Labeling, and Exporting Figures
  30:44
• 7.5 Practice: Visualizing a Dataset
  54:15

Bring it all together in a guided, end-to-end analysis. You'll frame a question, load and clean a real dataset, explore it with grouping and visualization, and present your findings in a polished notebook you can add to your portfolio.

• 8.1 Framing the Question and Getting the Data
  28:55
• 8.2 Cleaning and Preparing the Dataset
  42:30
• 8.3 Exploratory Analysis and Visualization
  47:40
• 8.4 Communicating Findings in a Notebook
  26:18
• 8.5 Final Project Submission and Next Steps
  35:05