Learning Path: From Python Programming to Data Science

Course Curriculum

Chapter 1: Mastering Python – Second Edition

Lecture 1: The Course Overview

Lecture 2: Python Basic Syntax and Block Structure

Lecture 3: Built-in Data Structures and Comprehensions

Lecture 4: First-Class Functions and Classes

Lecture 5: Extensive Standard Library

Lecture 6: New in Python 3.5

Lecture 7: Downloading and Installing Python

Lecture 8: Using the Command-Line and the Interactive Shell

Lecture 9: Installing Packages with pip

Lecture 10: Finding Packages in the Python Package Index

Lecture 11: Creating an Empty Package

Lecture 12: Adding Modules to the Package

Lecture 13: Importing One of the Package's Modules from Another

Lecture 14: Adding Static Data Files to the Package

Lecture 15: PEP 8 and Writing Readable Code

Lecture 16: Using Version Control

Lecture 17: Using venv to Create a Stable and Isolated Work Area

Lecture 18: Getting the Most Out of docstrings 1: PEP 257 and docutils

Lecture 19: Getting the Most Out of docstrings 2: doctest

Lecture 20: Making a Package Executable via python -m

Lecture 21: Handling Command-Line Arguments with argparse

Lecture 22: Interacting with the User

Lecture 23: Executing Other Programs with Subprocess

Lecture 24: Using Shell Scripts or Batch Files to Run Our Programs

Lecture 25: Using concurrent.futures

Lecture 26: Using Multiprocessing

Lecture 27: Understanding Why This Isn't Like Parallel Processing

Lecture 28: Using the asyncio Event Loop and Coroutine Scheduler

Lecture 29: Waiting for Data to Become Available

Lecture 30: Synchronizing Multiple Tasks

Lecture 31: Communicating Across the Network

Lecture 32: Using Function Decorators

Lecture 33: Function Annotations

Lecture 34: Class Decorators

Lecture 35: Metaclasses

Lecture 36: Context Managers

Lecture 37: Descriptors

Lecture 38: Understanding the Principles of Unit Testing

Lecture 39: Using the unittest Package

Lecture 40: Using unittest.mock

Lecture 41: Using unittest's Test Discovery

Lecture 42: Using Nose for Unified Test Discover and Reporting

Lecture 43: What Does Reactive Programming Mean?

Lecture 44: Building a Simple Reactive Programming Framework

Lecture 45: Using the Reactive Extensions for Python (RxPY)

Lecture 46: Microservices and the Advantages of Process Isolation

Lecture 47: Building a High-Level Microservice with Flask

Lecture 48: Building a Low-Level Microservice with nameko

Lecture 49: Advantages and Disadvantages of Compiled Code

Lecture 50: Accessing a Dynamic Library Using ctypes

Lecture 51: Interfacing with C Code Using Cython

Chapter 2: Learning Python Data Analysis

Lecture 1: The Course Overview

Lecture 2: Getting started with Python

Lecture 3: Getting Data using the Twitter API

Lecture 4: Collecting and Storing Tweets

Lecture 5: Database Design

Lecture 6: Pandas and Databases

Lecture 7: Panda Series, Dataframes, and Columnar Operations

Lecture 8: Grouping Operations and Working with Date Columns

Lecture 9: Merging Operations and Exporting data to JSON/CSV

Lecture 10: Array Features, Bucketting Arrays and Histogram Functions

Lecture 11: Simple Aggregations

Lecture 12: Linear Algebra

Lecture 13: Introducting PyQT and MatplotLib

Lecture 14: Creating Charts

Lecture 15: Simple XY Plots with Axis Scales

Lecture 16: Introduction to the NTLK Package

Lecture 17: Bag of Words

Lecture 18: Classification of Words

Lecture 19: Stemming

Lecture 20: Simple Sentiment Analysis

Lecture 21: Grouping By Dimensions and Classification of Data Types

Lecture 22: Trend Analysis and Deriving New Metrics

Lecture 23: Correlation Analysis

Lecture 24: Course Summary

Chapter 3: Python Data Visualization Solutions

Lecture 1: The Course Overview

Lecture 2: Importing Data from CSV

Lecture 3: Importing Data from Microsoft Excel Files

Lecture 4: Importing Data from Fix-Width Files

Lecture 5: Importing Data from Tab Delimited Files

Lecture 6: Importing Data from a JSON Resource

Lecture 7: Importing Data from a Database

Lecture 8: Cleaning Up Data from Outliers

Lecture 9: Importing Image Data into NumPy Arrays

Lecture 10: Generating Controlled Random Datasets

Lecture 11: Smoothing Noise in Real-World Data

Lecture 12: Defining Plot Types and Drawing Sine and Cosine Plots

Lecture 13: Defining Axis Lengths and Limits

Lecture 14: Defining Plot Line Styles, Properties, and Format Strings

Lecture 15: Setting Ticks, Labels, and Grids

Lecture 16: Adding Legends and Annotations

Lecture 17: Moving Spines to Center

Lecture 18: Making Histograms

Lecture 19: Making Bar Charts with Error Bars

Lecture 20: Making Pie Charts Count

Lecture 21: Plotting with Filled Areas

Lecture 22: Drawing Scatter Plots with Colored Markers