Introduction to Python and Data Analysis

Delegates (Bring the following)

• WiFi Enabled 64bit Windows Laptop with Admin rights
• USB Portfor memory stick (Training Data)
• Mouse with scroll wheel

Requirments

• Basic knowledge of programming.
• Basic numeracy and statistics

Audience

• Business professionals (Marketers, Finance, Product Managers)
• Students(Business, Economists, Accountants)
• Healthcare, E-Commerce, Social Media, Media and Entertainment.
• Researchers (Academics, Journalists, Policy Analysts)
• Non-technical professionals upskilling to improve and develop their career opportunities
• Recent graduates, graduates with degrees in business, economics, and marketing, as well as researchers looking to gain marketable skills.
• Excel power users, aspiring and existing analyst wishing to automate, process and enhance their data manipulation skills.

Automate slow, manual processes and repetitative tasks.

Benefits and Outcomes

Bring your tabulated data to life, highlighting distribution, clusters, outliers and relationships.

• Python Fundamentals: Learn introductory level Python tailored for data analysis.
• Data Manipulation: Read, clean, process data using Pandas
• Data Visualisation: Create visualisations using Matplotlib.
• Data Analysis: Apply statistical methods and models to extract insights for data driven decisions.

Uncover underlying data segments to enable proactive, tailored initiatives for improved outcomes.

Training Content

Sessions

Session 1 - Course Introduction

• Introduction and Objectives
• Installation of Python Application (IDLE) and Libraries
• Data Analysis Overview

Session 2 - Introduction to Python

• IDLE environment and Commands.
• Programs, Functions and Arguments.
• Data Types (Lists, Strings, Sets, Tuples, Dictionary).
• Operators, Indexing, Slicing and Sorting.
• Conditional Statements and Loops.
• Type Verification and Conversions.
• Debugging.
• Boolean algebra
• Operators (Arithmetic, Comparison, Logical, Identity, Membership)
• Numpy

Lunch

Session 3 - Pandas

• Cleaning and processing
• Information and Description
• Querying and Filtering Data
• Adding and Dropping Columns
• Adding and Dropping Rows of Data
• Renaming Columns
• Identifying and filling missing Data
• Identifying and removing duplicate Data
• Finding near duplicates
• Sorting Data
• Merging DataFrames
• Grouping DataFrames
• Joining DataFrames
• Saving DataFrames to file

Visualisation with Matplotlib

Session 4 - Data Visualisation

• Chart selection.
• Anatomy of a plot (Figure, Plot, Axes, Legend, Titles, Axis).
• Pie, Bar Charts, Line, Scatter Plots, Histograms
• Visualising Data with Pandas, Matplotlib, Seaborn, Plotly, Bokeh and Altair
• Export plots to file and interactive web based visualisations.
• Data: Correlation, Distribution, Statistics and Patterns.

Session 5 - Basic analysis of a data set

• Cleaning and process the data
• Analysing the data for distribution, patterns and clusters.

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