CPD Certified Data Analytics with Python

Learning Objectives

• Understand what data analytics is and its real-world applications.
• Set up Python and Power BI environments.
• Write simple Python programs.

Topics Covered

• What is data analytics? The data pipeline.
• Installing Anaconda and using Jupyter Notebook.
• Python syntax, comments, variables, and data types.
• Input/output, arithmetic operations.

Activities

• Run a few basic Python commands in Jupyter.
• Explore real-life examples of data-driven decisions.

Assignment

• Write a program that takes three numbers as input and prints their sum, average, and maximum.

Learning Objectives

• Understand what SQL is and why it matters in data analytics.
• Learn to retrieve, filter, sort, and group data using basic SQL queries.
• Work with a simple relational database (SQLite or MySQL).
• Connect SQL concepts to what students already learned in Python and Power BI.

Topics Covered

• What is a database? Tables, rows, columns, and relationships.
• SQL basics: SELECT, FROM, WHERE.
• Filtering with comparison and logical operators.
• Sorting with ORDER BY.
• Grouping and aggregations with GROUP BY, SUM, AVG, COUNT.
• Simple joins (intro-only level): INNER JOIN on two related tables.
• Using DB Browser for SQLite (or a cloud SQL sandbox).

Activities

• Explore a small database (e.g., customers and orders).
• Run queries to fetch:
  • all customers
  • orders above a certain value
  • customers from a specific region
• Write a grouped query showing total sales per region.
• Perform a simple join between two tables to match customers and their orders.

Assignment
Write a set of SQL queries that answer the following:

1. List all products with a price above a chosen threshold.
2. Show total sales per product category using GROUP BY.
3. Join the customers and orders tables to show each customer and their total spending.

Learning Objectives

• Understand lists, tuples, sets, and dictionaries.
• Use control structures to process data.

Topics Covered

• Lists and indexing.
• Tuples, sets, and dictionaries.
• For and while loops.
• If/else conditions.

Activities

• Create a list of student names and grades, and compute the average grade.
• Practice nested if and for loops.

💡 Assignment

• Write a Python program that counts how many students scored above 70% using a list of scores.

Learning Objectives

• Write reusable functions.
• Handle errors gracefully.
• Use built-in and external libraries.

Topics Covered

• Defining and calling functions.
• Parameters and return values.
• try/except for handling errors.
• Using math and random libraries.

Activities

• Create a function that calculates the average of a list.
• Simulate random data using random.randint().

Assignment

• Build a small “data cleaner” function that removes null or empty values from a list.

Learning Objectives

• Load and process data from files.
• Understand basic file operations.
• Get introduced to the pandas library.

Topics Covered

• Reading and writing text and CSV files.
• Introduction to pandas DataFrames.
• Viewing and summarizing data with pandas.

Activities

• Load a CSV file (e.g., simple sales data) using pandas.
• Display the first 5 rows and calculate column averages.

Assignment

• Write a Python script that loads a CSV file and outputs:
  • Number of rows and columns
  • Column names
  • Average of numeric columns

Learning Objectives

• Manipulate and clean data using pandas.
• Handle missing values and duplicates.

Topics Covered

• Selecting, filtering, and sorting data.
• Adding and removing columns.
• Handling missing and duplicate values.
• String operations and data type conversion.

Activities

• Clean a dataset by dropping duplicates and filling missing values.
• Extract and format text columns.

Assignment

• Clean a real dataset (e.g., customer data). Produce a clean CSV for analysis.

Learning Objectives

• Create basic visualizations with Matplotlib and Seaborn.
• Understand visual storytelling in data analytics.

Topics Covered

• Line charts, bar charts, pie charts, scatter plots.
• Customizing labels, titles, and colors.
• Introduction to Seaborn for prettier charts.

Activities

• Plot trends in sales or temperature data.
• Compare multiple variables using scatter plots.

Assignment

• Create three charts showing trends and comparisons in your cleaned dataset (from Week 5).

Learning Objectives

• Understand Power BI’s interface and workflow.
• Import and explore datasets.
• Create basic reports and visuals.

Topics Covered

• Power BI Desktop overview.
• Data import (CSV, Excel).
• Data view, report view, and model view.
• Creating tables, charts, and simple dashboards.

Activities

• Load the cleaned dataset into Power BI.
• Create bar and pie charts.

Assignment

• Build a Power BI report showing total sales per region and product category.

Learning Objectives

• Create relationships between tables.
• Write basic DAX formulas for calculations.
• Enhance dashboards with interactivity.

Topics Covered

• Data modeling and relationships.
• Calculated columns vs. measures.
• DAX basics: SUM, AVERAGE, COUNTROWS, CALCULATE.
• Slicers, filters, and drill-through.

Activities

• Connect multiple tables (sales and customers).
• Use slicers to filter visual data.

Assignment

• Create an interactive Power BI dashboard that allows users to filter by year and region

Learning Objectives

• Integrate Python scripts within Power BI.
• Compare Power BI visuals with Python charts.

Topics Covered

• Exporting Python-cleaned data for Power BI.
• Using Python visuals in Power BI.
• End-to-end workflow: Python → Power BI.

Activities

• Use a Python visual to plot a histogram in Power BI.
• Compare Matplotlib and Power BI outputs for the same dataset.

Assignment

• Create a hybrid Power BI dashboard combining native Power BI visuals and one Python-based chart.

Learning Objectives

• Apply all learned skills to a real dataset.
• Produce a professional analytics report.

Topics Covered

• Project structure and documentation.
• Presentation of insights and recommendations.

Activities

• Work individually or in pairs on a dataset of choice (e.g., sales, HR, or environmental data).
• Combine Python analysis and Power BI visualization.

Final Project Deliverables

• Jupyter Notebook: Data cleaning, analysis, and charts.
• Power BI Dashboard: Interactive report summarizing insights.
• Short Presentation: Findings and recommendations.