Markovdata
Advanced Python for Data Science & Data Analysis
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Course sections

Section 1
NumPy Basics: Arrays and Vectorized Computation
1
1. NumPy ndarray: A Multidimensional Array Object
2
2. Universal Functions: Fast Element-Wise Array Functions
3
3. Array-Oriented Programming with Arrays
4
4. File Input and Output with Arrays
5
5. Linear Algebra
6
6. Pseudorandom Number Generation
7
7. Example: Random Walks
Section 2
Getting Started with pandas.
1
1. Introduction to pandas Data Structures
2
2. Essential Functionality
3
3. Summarizing and Computing Descriptive Statistics Correlation and Covariance Unique Values, Value Counts, and Membership
Section 3
Data Loading and Storage and File Formats.
1
1. Reading and Writing Data in Text Format Reading Text Files in Pieces
2
2. Binary Data Formats
3
3. Interacting with Web APIs
4
4. Interacting with Databases
Section 4
Data Cleaning and Preparation.
1
1. Handling Missing Data
2
2. String Manipulation
Section 5
Data Wrangling: Join and Combine and Reshape.
1
1. Hierarchical Indexing
2
2. Combining and Merging Datasets
3
3. Reshaping and Pivoting
Section 6
Plotting and Visualization
1
1. A Brief matplotlib API Primer
2
2. Plotting with pandas and seaborn
3
3. Other Python Visualization Tools
Section 7
Data Aggregation and Group Operations.
1
1. GroupBy Mechanics
2
2. Data Aggregation
3
3. Apply: General split-apply-combine
4
4. Pivot Tables and Cross-Tabulation
Section 8
Time Series.
1
1. Date and Time Data Types and Tools
2
2. Time Series Basics
3
3. Date Ranges, Frequencies, and Shifting
4
4. Time Zone Handling
5
5. Periods and Period Arithmetic
6
6. Resampling and Frequency Conversion

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