Introduction to Python and Data Analysis

Who will the Course Benefit?

This course is designed for anyone who wants to acquire basic proficiency in Python and its data analysis tools for use in their own work. It is for numerate people who are familiar with programming constructs but are not necessarily programmers who want to be able to do some data manipulation and visualization using Python.

Course Objectives

This course aims to develop delegates skills in Python and its main data analysis libraries. On completion of the course they will have gained enough proficiency to allow them to apply these tools in their day to day data analysis activities.

Introduction to Python and Data Analysis Training Course

Course Contents - DAY 1

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 Training Course

Course Contents - DAY 2

Session 5: CONDITIONS & 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 Training Course

Course Contents - 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 Training Course

Course Contents - DAY 4

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

Requirements

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.

Further Learning

• Python Programming 1
• Data Analysis Python