Data Analytics Professional Programme

Build a strong understanding of data and teach learners how to use Excel to organise, analyse, and communicate information.

Module 1: Introduction to Data Analytics

• 1.1 what is data, structured vs unstructured, qualitative vs quantitative, primary vs secondary data, data analytics vs data science, data analyst vs data scientist vs BI analyst, the analytics lifecycle: business problem to data collection to preparation to analysis to visualisation to insights to business decision

Module 2: Excel Fundamentals

• 2.1 the Excel interface, worksheets and workbooks, rows and columns, data entry and types, formatting, sorting, filtering, tables, named ranges

Module 3: Excel Formulas

• 3.1 SUM, AVERAGE, MIN, MAX, COUNT, COUNTA, IF, IFS, AND, OR, lookup functions: XLOOKUP, VLOOKUP, HLOOKUP, INDEX, MATCH, SUMIF(S), COUNTIF(S), AVERAGEIF(S)

Module 4: Data Cleaning with Excel

• 4.1 duplicate records, missing values, incorrect data types, text cleaning, date formatting, data validation, find and replace, removing unwanted spaces, standardising categories

Module 5: Excel Visualisation

• 5.1 bar, line, pie, scatter, and combination charts, conditional formatting, sparklines, basic dashboards

Module 6: Pivot Tables

• 6.1 creating pivot tables, grouping data, calculated fields, pivot charts, filters, slicers, interactive reports

Capstone: Personal & Household Finance Analytics Dashboard

• 7.1 Learners receive a raw financial dataset and must clean it, categorise transactions, calculate income and expenses, analyse spending patterns, identify major spending categories, calculate savings, build an Excel dashboard, and present three to five key insights.

Teach learners the statistical thinking required to interpret data correctly.

Module 1: Descriptive Statistics

• 1.1 mean, median, mode, range, variance, standard deviation, percentiles, quartiles, interquartile range

Module 2: Data Distribution

• 2.1 normal distribution, skewness, outliers, frequency distributions, histograms, box plots

Module 3: Probability Fundamentals

• 3.1 probability concepts and events, probability distributions, expected value, basic conditional probability

Module 4: Correlation & Relationships

• 4.1 positive, negative, and no correlation, correlation vs causation, scatter plots, trend lines

Module 5: Data Quality

• 5.1 accuracy, completeness, consistency, validity, timeliness, uniqueness

Module 6: Data Preparation

• 6.1 introduction to ETL, data transformation, data integration, data profiling, data validation

Module 7: Data Storytelling

• 7.1 finding patterns and identifying trends, selecting appropriate charts, communicating insights, executive summaries, avoiding misleading visualisations

Capstone: Customer Behaviour Analysis

• 8.1 Learners analyse a customer dataset (demographics, purchases, products, locations, order frequency, customer value) and identify customer trends, the most valuable customer segments, purchasing patterns, product preferences, geographic patterns, and potential business opportunities.

Teach learners to retrieve, transform, and analyse data directly from relational databases.

Module 1: Database Fundamentals

• 1.1 relational databases, tables, records, fields, primary and foreign keys, relationships, normalisation

Module 2: SQL Fundamentals

• 2.1 SELECT, FROM, WHERE, ORDER BY, LIMIT, DISTINCT

Module 3: SQL Functions

• 3.1 aggregate functions: COUNT, SUM, AVG, MIN, MAX

Module 4: Filtering & Grouping

• 4.1 AND, OR, IN, BETWEEN, LIKE, GROUP BY, HAVING

Module 5: SQL Joins

• 5.1 INNER, LEFT, RIGHT, and FULL joins, self joins

Module 6: Advanced SQL

• 6.1 subqueries, Common Table Expressions, CASE statements, window functions, ranking, running totals, date analysis

Module 7: Database Analytics

• 7.1 working with realistic databases containing customers, products, orders, employees, transactions, and locations

Capstone: Retail Database Analytics

• 8.1 Learners query a relational database to answer business questions: total sales, top revenue products, highest-value customers, best-performing regions, monthly sales trends, declining products, average order value, and deliver a SQL script, query documentation, an analytical report, visualisations, and business recommendations.

Teach learners to use Python for data manipulation, analysis, and visualisation. This is the beginning of the four-month major practical project phase.

Module 1: Python Fundamentals for Analysts

• 1.1 variables, data types, lists, dictionaries, functions, loops, conditions, modules, exception handling

Module 2: NumPy

• 2.1 arrays, array operations, mathematical functions, indexing, filtering, aggregation

Module 3: Pandas

• 3.1 Series and DataFrames, importing and exporting data, filtering, sorting, grouping, aggregation, merging, joining

Module 4: Data Cleaning with Python

• 4.1 missing values, duplicates, incorrect formats, outliers, data type conversion, string cleaning, date processing

Module 5: Data Visualisation

• 5.1 using Matplotlib and/or Plotly for bar, line, histogram, scatter, box, and heatmap charts, interactive visualisations

Module 6: Exploratory Data Analysis

• 6.1 asking analytical questions, exploring distributions, identifying relationships and anomalies, identifying trends, generating hypotheses

Capstone: Sales & Customer Analytics Project

• 7.1 Learners receive a multi-table business dataset (customers, products, orders, transactions, locations, marketing campaigns) and analyse sales/revenue/profit trends, customer segmentation/value/churn indicators, product performance, and regional performance, delivering a Jupyter Notebook, a clean dataset, SQL queries, an analytical report, visualisations, and an executive presentation.

Transform analytical results into professional, interactive business intelligence dashboards.

Module 1: Power BI Fundamentals

• 1.1 the Power BI ecosystem and Power BI Desktop, data sources, importing data, data modelling, reports, dashboards

Module 2: Power Query

• 2.1 data extraction, transformation, cleaning, merging, appending, data types, query parameters

Module 3: Data Modelling

• 3.1 fact and dimension tables, star schema, relationships, cardinality, date tables

Module 4: DAX

• 4.1 fundamentals, measures, calculated columns and tables, CALCULATE, SUM, AVERAGE, COUNT, DISTINCTCOUNT, FILTER, ALL, time intelligence functions

Module 5: Dashboard Design

• 5.1 KPI cards, tables, charts, slicers, drill-down, drill-through, tooltips, bookmarks, navigation

Module 6: Business Intelligence

• 6.1 KPI development, performance analysis, management, executive, and operational reporting

Capstone: Business Intelligence Dashboard

• 7.1 Learners build an interactive Power BI solution for a fictional organisation with Executive Overview, Sales Analysis, Customer Analysis, and Product Analysis pages, delivering a Power BI .pbix file, a data model, DAX measures, a dashboard, a data dictionary, and an executive report.

Move beyond descriptive analytics into predictive and automated analytics.

Module 1: Advanced Statistics

• 1.1 sampling, confidence intervals, hypothesis testing, p-values, statistical significance, A/B testing, regression fundamentals

Module 2: Predictive Analytics

• 2.1 predictive analytics concepts: regression, classification, time-series fundamentals, model evaluation, train/test datasets

Module 3: Forecasting

• 3.1 time-series data, trends, seasonality, moving averages, forecasting models, forecast evaluation

Module 4: Machine Learning for Analysts

• 4.1 introduction to Scikit-learn: linear regression, logistic regression, decision trees, random forests, clustering (used appropriately for analytics, not to turn this into a full data-science programme)

Module 5: Data Automation

• 5.1 using Python for automated data collection, cleaning, and reporting, scheduled analysis, API data collection, Excel report generation

Module 6: APIs & Data Sources

• 6.1 REST APIs, JSON, API authentication, extracting data, working with external datasets

Capstone: Business Forecasting & Predictive Analytics Project

• 7.1 A retail company wants to understand future sales and identify factors associated with customer churn. Learners collect, clean, and explore historical data, analyse relationships, build and evaluate a predictive model, produce forecasts, visualise results, explain limitations, and present business insights, delivering a Python notebook, statistical analysis, a forecasting model, visualisations, a Power BI dashboard, and an executive presentation.

This is the final graduation project, bringing Excel, SQL, Python, statistics, Power BI, data engineering concepts, and business intelligence together into one enterprise-level analytics project.

Module 1: Enterprise Analytics

• 1.1 data analytics strategy, data governance, data ownership, data quality, data catalogues, data lineage

Module 2: Data Warehousing Fundamentals

• 2.1 OLTP vs OLAP, data warehouses, data marts, ETL/ELT, star schema, fact and dimension tables

Module 3: Advanced Power BI

• 3.1 advanced DAX, time intelligence, dynamic measures, advanced filtering, performance optimisation, dashboard UX, row-level security

Module 4: Advanced SQL

• 4.1 CTEs, window functions, query optimisation, complex joins, analytical queries, views, stored procedures introduction

Module 5: Python Analytics Automation

• 5.1 automated ETL, API integration, data pipelines, scheduled analytics, automated reports, database integration

Module 6: Professional Data Analytics

• 6.1 requirements gathering, stakeholder interviews, KPI definition, data storytelling, executive presentations, business reporting, communicating uncertainty, writing analytical recommendations

Capstone: Enterprise Business Intelligence & Analytics Platform

• 7.1 Learners work as a professional analytics team supporting a fictional organisation with multiple data sources (CRM, sales system, inventory, marketing), building a pipeline through SQL and Python analytics into a data warehouse and Power BI executive dashboard covering sales, customer, marketing, and inventory analytics plus sales forecasting and churn indicators. Each learner/team presents the project as though presenting to a company's management team.