CPD Certified Data Analytics with Python

✓  Understand what data analytics is & its real-world applications

Explore the data pipeline and how data-driven decisions are made across industries.

✓  Set up Python & Power BI environments

Install Anaconda and get started with Jupyter Notebook for data work.

✓  Python syntax, variables & data types

Write your first Python programs using comments, variables, and core data types.

✓  Input/output & arithmetic operations

Handle user input and perform calculations using Python's arithmetic operators.

✓  Run basic Python commands in Jupyter

Get hands-on in the Jupyter environment running real Python code from day one.

✓  Understand what SQL is & why it matters in data analytics

Learn about databases, tables, rows, columns, and how relational data is structured.

✓  Retrieve, filter & sort data using SQL queries

Use SELECT, FROM, WHERE, and ORDER BY to query and organise data.

✓  Group & aggregate data with GROUP BY, SUM, AVG & COUNT

Summarise data to produce meaningful totals, averages, and counts across categories.

✓  Perform simple joins between related tables

Use INNER JOIN to combine data across two tables and answer multi-table questions.

✓  Work with a real database using DB Browser for SQLite

Explore a customers and orders database with hands-on queries from day one.

✓  Understand lists, tuples, sets & dictionaries

Work with Python's core data structures and understand when to use each one.

✓  Lists, indexing & data access

Create and navigate lists to store and retrieve ordered collections of data.

✓  For & while loops with if/else conditions

Use control structures to process and filter data with logic and repetition.

✓  Compute averages & apply conditions to real data

Build programs that calculate results from lists — such as average grades or pass rates.

✓  Practice nested loops & conditional logic

Write more complex programs using nested if and for loops to solve data problems.

✓  Write reusable functions with parameters & return values

Define and call functions to make your code cleaner, modular, and reusable.

✓  Handle errors gracefully using try/except

Prevent programs from crashing by catching and managing errors effectively.

✓  Use built-in libraries: math & random

Leverage Python's built-in libraries to perform calculations and simulate data.

✓  Simulate random data using random.randint()

Generate test datasets to practise analysis before working with real-world data.

✓  Build a data cleaner function to remove null values

Apply functions in a practical context to prepare raw data for analysis.

✓  Read & write text and CSV files

Load and save data from files — the foundation of real-world data workflows.

✓  Introduction to pandas DataFrames

Get started with pandas — the industry-standard library for working with structured data.

✓  View & summarise data with pandas

Display rows, column names, and calculate averages to quickly understand a dataset.

✓  Load real CSV data & extract key statistics

Write a script to output row/column counts, column names, and numeric averages.

✓  Select, filter & sort data with pandas

Manipulate DataFrames to isolate and organise the data you need for analysis.

✓  Add & remove columns from a DataFrame

Shape your dataset by creating new columns or dropping those you no longer need.

✓  Handle missing values & remove duplicates

Clean real-world data by identifying gaps, filling nulls, and eliminating duplicates.

✓  String operations & data type conversion

Format and transform text columns and convert data types for consistent, analysis-ready data.

✓  Produce a clean CSV dataset ready for analysis

Apply all cleaning techniques to a real customer dataset and export the result.

✓  Create line, bar, pie & scatter charts with Matplotlib

Build the most commonly used chart types to visualise and communicate data.

✓  Customise labels, titles & colours

Make charts clear and professional by adding formatting that guides the reader.

✓  Introduction to Seaborn for enhanced visualisations

Use Seaborn to produce polished, publication-quality charts with less code.

✓  Plot trends & compare variables using real datasets

Apply visual storytelling by charting sales trends and multi-variable comparisons.

✓  Navigate Power BI Desktop & understand its workflow

Get familiar with the interface including data view, report view, and model view.

✓  Import & explore datasets from CSV & Excel

Bring your cleaned data into Power BI and begin exploring it visually.

✓  Create tables, charts & simple dashboards

Build bar charts, pie charts, and your first Power BI report layout.

✓  Build a report showing sales by region & product category

Apply your skills to create a meaningful, real-world Power BI business report.

✓  Create relationships between tables in Power BI

Connect multiple data tables — such as sales and customers — through data modelling.

✓  Write DAX formulas: SUM, AVERAGE, COUNTROWS & CALCULATE

Use DAX to build calculated columns and measures that power your reports.

✓  Add slicers, filters & drill-through for interactivity

Let users explore data dynamically by filtering visuals by region, year, and more.

✓  Build an interactive dashboard filterable by year & region

Deliver a fully interactive Power BI dashboard that responds to user selections.

✓  Integrate Python scripts within Power BI

Connect Python directly to Power BI to extend its capabilities with custom visuals and logic.

✓  Export Python-cleaned data for use in Power BI

Build a seamless end-to-end workflow: clean data in Python, visualise it in Power BI.

✓  Plot Python visuals inside Power BI reports

Use Python visuals to plot histograms and Matplotlib charts directly within a Power BI report.

✓  Build a hybrid dashboard combining Power BI & Python charts

Deliver a complete dashboard that blends native Power BI visuals with a Python-based chart.

✓  Apply all learned skills to a real-world dataset

Work on a dataset of your choice — sales, HR, or environmental data — and apply the full analytics workflow.

✓  Combine Python analysis with Power BI visualisation

Bring together everything learned to produce a professional, end-to-end analytics project.

✓  Deliver a Jupyter Notebook with cleaning, analysis & charts

Document your full analytical process from raw data to meaningful insight.

✓  Build an interactive Power BI dashboard summarising insights

Present your findings through a polished, fully interactive Power BI report.

✓  Present findings & recommendations to the group

Communicate your analysis clearly and confidently with a short presentation of insights.