This online course is an introduction to Python and its main data analysis libraries, Pandas and Matplotlib for delegates with some understanding of programming concepts.

Introduction to Python and Data Analysis – Course Objectives

It is a two-part course, the first is an introduction to Python programming, the second introduces Python’s data analysis tools. For the programming environment we use JupyterLab on the Anaconda platform. Anaconda is one of the most, if not the most, popular Data Science platforms. Please note, this course is not meant for Data Analysts or Scientists who should instead consider our Data Analysis Python course.

We believe in learning by doing and take a hands-on approach to training. Delegates are provided with all required resources, including data, and are expected to code along with the instructor. The objective is for delegates to reproduce the analysis in our manuals as well as gain a conceptual understanding of the methods.

Exercises and examples are used throughout the course to give practical hands-on experience with the techniques covered.

Introduction to Python and Data Analysis- Online Course Contents

Course Introduction

• Administration and Course Materials
• Course Structure and Agenda
• Delegate and Trainer Introductions

Session 1: INTRODUCTION

• Python as an interpreted language
• Script mode by example
• Interactive mode
• Statements
• Comments
• Whitespace and Indentation

Session 2: PYTHON: VARIABLES & SCALAR TYPES

• Numerical types
• Text
• Boolean
• Variables as references
• The type() function

Session 3: OPERATORS & EXPRESSIONS

• Arithmetic Operators
• Assignment Operators
• Comparison Operators
• Logical Operators
• Membership Operators

Session 4: CONTAINERS

• Lists
• Tuples
• Sets
• Dictionary

Introduction to Python and Data Analysis – Day 2

Session 5: CONDITIONS AND LOOPS

• Basic if statement
• Else clause
• For loop
• While loop
• The range function
• Iterating over a list
• Break
• Continue

Session 6: FUNCTIONS

• inbuilt functions (len(), sum(), min(), max(), sorted())
• defining functions
• positional arguments
• names arguments
• default value arguments

Session 7: OBJECTS

• What is a Class?
• Data Attributes and Methods
• A simple example
• Some methods of inbuilt containers

Introduction to Python and Data Analysis -DAY 3

Session 8: INTRODUCTION TO DATAFRAMES

• What is a DataFrame?
• DataFrame attributes
• Loading and writing DataFrames
• Exploratory functions
• Subsetting
• Conditional subsetting
• Adding and dropping columns
• Inbuilt aggregating functions
• Missing values

Introduction to Python and Data Analysis -Day4

Session 9: GROUPBY AND AGGREGATION: SPLIT-APPLY-COMBINE

• Groupby one column and aggregate using single inbuilt function
• Groupby two columns and aggregate using single inbuilt function
• Groupby one column and aggregate using separate function for each column

Session 10: PLOTTING WITH MATPLOTLIB

• Bar chart
• Histogram
• Line plot

Introduction to Python and Data Analysis – Online Course Details

This is a four-day online course running on consecutive days from 9:30 am – 4:30 pm.

Prerequisites

Programming:

• Experience coding small programs that use variables, arrays or lists, conditional statements, loops and functions in some language. Skills and knowledge that can be acquired by attending our Introduction to Programming – Python course.

Numeracy:

• Able to calculate and interpret averages, standard deviations and similar basic statistics.
• Ability to read and understand charts and graphs.
• Mathematics: GCSE or equivalent.

Online Course

This course is delivered live online. Course delegates will need a reliable internet connection and a PC or laptop with audio and video capability with the relevant software installed for the training.  Delegates see, hear and interact with the tutor.

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