Python Data Science

Python Data Science

Audience

This course is for all those who want to master the use of Python for data science applications.

Prerequisites

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

Duration

3 days. Hands on.

Course Objectives

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.

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 Content

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

Virtual Courses

ALL of our courses can be delivered virtually. And our Bath public schedule of courses are now available as live virtual sessions, using the popular Zoom Virtual Classroom and remote labs. Delegates can test their access at: www.zoom.us/test

On-Site Courses

Can't attend one of our public classes? Booking for multiple people?

All our courses are available on your site! Delivered for your staff, at your premises.

Contact us to find out more...