This course aims to extend and solidify your Python experience by exploring advanced techniques and common Python APIs. You’ll learn how to write OOP code. Also implement multithreaded code and automate software testing. You will use popular Python data science libraries, implement Big Data solutions, and more. Machine learning and AI are introduced using Python libraries.
The skills learned can be applied to corporate software to provide significant improvements with an emphasis on best practice.
Duration
3 days
Prerequisites
- Approx. 6 months Python experience
What you’ll learn
- Object-oriented Python programming
- Asynchronous programming
- Python data science techniques
- Python Big Data
- The principles of machine learning
- Making forecasts from training data
- Implementing and automating software testing
Course details
Object-Oriented Programming
- Essential Concepts
- Defining and Using a Class
- Class-Wide Members
Additional Object-Oriented Techniques
- A Closer Look at Attributes
- Implementing Special Methods
- Inheritance
Asynchronous Processing in Python
- Getting Started with Asynchrony in Python
- Creating Tasks to Run in Different Threads
- Additional Task Techniques
Getting Started with Python Data Science and NumPy
- Introduction to Python Data Science
- NumPy Arrays
- Manipulating Array Elements
- Manipulating Array Shape
NumPy Techniques
- NumPy Universal Functions
- Aggregations
- Broadcasting
- Manipulating Arrays using Boolean Logic
- Additional Techniques
Getting Started with Pandas
- Introduction to Pandas
- Creating a Series
- Using a Series
- Creating a DataFrame
- Using a DataFrame
Pandas Techniques
- Universal Functions
- Merging and Joining Datasets
- A Closer Look at Joins
Working with Time Series Data
- Introduction to Time Series Data
- Indexing and Plotting Time Series Data
- Testing Data for Stationarity
- Making Data Stationary
- Forecasting Time Series Data
- Scaling Back the ARIMA Results
Introduction to Machine Learning
- Machine Learning Concepts
- How Learning Can Help Businesses
- Learning From Training Data
- Classification Of Data
- Clustering Data
Getting Started with Scikit-Learn
- Scikit-Learn Essentials
- A Closer Look at Datasets
Understanding the Scikit-Learn API
- Introduction
- Scikit-Learn API Essentials
- Performing Linear Regression
Going Further with Scikit-Learn
- Introduction
- Understanding Naïve Bayes Classification
- Naïve Bayes Example using Scikit-Learn
Automate Testing
- Python test frameworks
- Example class-under-test
- How to write a test
- Running tests
- Arrange / Act / Assert
- Testing for exceptions
- Setup and teardown code
Case Study
- Worked example of a real-world data science problem