Python Data Science

Python has emerged as a popular and effective language in the world of data science. The dynamic nature of the language, the relative simplicity of the syntax, and the abundance of fast and powerful libraries have all been important contributory factors in this growth.

This course takes a detailed look at the most popular Python libraries for numeric processing, statistical analysis, machine learning, and visualization. We also show how to make use of common Python data types and algorithms to achieve real-world tasks.


3 days



  • Some familiarity with Python or another contemporary language would be beneficial

What you'll learn

  • Using NumPy and Pandas for efficient data manipulation
  • Using Matplotlib and Seaborn for visualization
  • Working with time series data
  • Machine learning concepts
  • Using Scikit-Learn for machine learning

Course details

Python Quick Start

  • Python Essentials
  • Language Fundamentals
  • Functions
  • Data Structures

Getting Started with NumPy

  • Setting the Scene
  • 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
  • Classification
  • Clustering

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

Case Study

  • Worked example of a real-world data science problem