Training

Harness the full potential of your data using Python. Explore and refine your data analysis skills to turn insights into compelling narratives that enhance decision-making.

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

• Basic knowledge of programming.
• Basic numeracy and statistics

• 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.

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.    

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

Session 3 - Pandas

• Read/Write CSV and Excel (xlsx) using 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

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
• Analysis of the data