What is data analytics? The role of a data analyst in organisations · Types of analytics: descriptive, diagnostic, predictive, and prescriptive · The data analytics workflow: collect, clean, analyse, visualise, communicate · Types of data: quantitative vs qualitative, structured vs unstructured · Data sources: primary data, secondary data, open data, and databases · Data ethics: privacy, consent, confidentiality, and responsible data use · Introduction to all tools: Excel, Power BI, Tableau, SQL, Google Sheets, STATA, SPSS, and AI · Setting up your analytics environment: installing and configuring all tools

Data organisation: tables, named ranges, and structured data in Excel · Advanced formulas: VLOOKUP, HLOOKUP, INDEX-MATCH, XLOOKUP, IF, IFS, SUMIFS, COUNTIFS · PivotTables: creating, customising, filtering, grouping, and slicers · PivotCharts: building dynamic charts linked to PivotTables · Data cleaning in Excel: removing duplicates, blanks, text-to-columns, TRIM, and CLEAN · Conditional formatting: highlighting patterns, heat maps, and data bars · Power Query: importing, transforming, and combining data from multiple sources · What-if analysis: Goal Seek, Scenario Manager, and Data Tables · Excel dashboards: combining charts, slicers, and KPIs into a single executive view · Hands-on lab: build a complete sales performance dashboard from a raw dataset

Google Sheets vs Excel: key differences and when to use each · Importing data: CSV, Excel, Google Forms, and connected data sources · Advanced functions: QUERY function, ARRAYFORMULA, IMPORTRANGE, and FILTER · Collaborative features: sharing, commenting, version history, and permissions · Charts and visualisations: building and customising charts in Google Sheets · Google Sheets dashboards: linking multiple sheets into summary views · Data validation and dropdown lists: ensuring data quality in shared sheets · Automating tasks with Google Apps Script: introduction and basic macros · Connecting Google Sheets to Looker Studio (Google Data Studio) for reporting · Hands-on lab: build a collaborative project tracker with automated summary dashboard

What is SQL and why every data analyst must know it · Database concepts: tables, rows, columns, primary keys, and foreign keys · Basic SQL: SELECT, FROM, WHERE, ORDER BY, LIMIT, and DISTINCT · Filtering and conditions: AND, OR, NOT, IN, BETWEEN, LIKE, and IS NULL · Aggregate functions: COUNT, SUM, AVG, MIN, MAX, GROUP BY, and HAVING · Joining tables: INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL OUTER JOIN · Subqueries and CTEs (Common Table Expressions): readable, layered queries · Window functions: ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, and running totals · Data cleaning with SQL: handling NULLs, deduplication, and type casting · Hands-on lab: analyse a real-world business database — answer 20 analytical questions using SQL

Descriptive statistics: mean, median, mode, range, variance, and standard deviation · Data distributions: normal, skewed, and bimodal — identifying and interpreting · Percentiles, quartiles, and the interquartile range (IQR) · Correlation: Pearson and Spearman correlation coefficients — interpretation and limitations · Inferential statistics: samples, populations, and sampling bias · Hypothesis testing: null hypothesis, p-values, and significance levels · Chi-square test: testing relationships between categorical variables · t-tests: comparing means between two groups (independent and paired samples) · Regression analysis: simple and multiple linear regression — interpreting coefficients · Probability basics: rules, conditional probability, and Bayes’ theorem · Hands-on lab: conduct a full statistical analysis on a real dataset — test a hypothesis and interpret results

Introduction to SPSS: interface, variable view, data view, and output viewer · Importing data into SPSS: CSV, Excel, and text files · Defining variables: variable names, labels, value labels, missing values, and measurement levels · Data cleaning in SPSS: finding and handling missing values, outliers, and errors using Explore · Descriptive statistics in SPSS: Frequencies, Descriptives, and Explore procedures · Cross-tabulations and chi-square test: Crosstabs procedure — interpretation of output · Comparing means: Independent samples t-test, paired samples t-test, and one-sample t-test · One-way ANOVA: testing differences across three or more groups with post-hoc tests · Correlation in SPSS: Pearson and Spearman correlation — interpreting the output table · Linear regression in SPSS: simple and multiple regression — R², coefficients, and significance · Non-parametric tests: Mann-Whitney U, Kruskal-Wallis, and Wilcoxon signed-rank tests · Reliability analysis: Cronbach’s Alpha for scale and survey data validation · Factor analysis: introduction to exploratory factor analysis (EFA) for survey data · Exporting SPSS output: tables and charts to Word, Excel, and PDF · Hands-on lab: complete a full research data analysis in SPSS — from import to final output tables

Introduction to STATA: interface, do-files, log files, and basic navigation · Importing data into STATA: CSV, Excel, and other formats · Data management: labelling variables, recoding, generating new variables, and sorting · Descriptive statistics in STATA: summarize, tabulate, codebook, and inspect commands · Data cleaning in STATA: missing values, outliers, and duplicate observations · Cross-tabulations: tabulate and tab2 commands · Correlation and regression in STATA: correlate, regress, and output interpretation · t-tests and ANOVA in STATA: ttest, oneway, and interpreting p-values · Graphs in STATA: histogram, scatter plot, bar chart, box plot, and twoway graphs · Exporting STATA outputs: tables and graphs to Word and Excel · Panel data in STATA: xtset, xtreg — fixed effects and random effects models · Introduction to EViews: interface, workfile structure, and importing time-series data · Descriptive statistics and graphing in EViews: series, groups, and basic plots · Unit root tests in EViews: ADF, PP, and KPSS tests for stationarity · Ordinary Least Squares (OLS) regression in EViews: estimation and diagnostic tests · Time series analysis in EViews: ARIMA, ACF, PACF, and Box-Jenkins methodology · VAR (Vector Autoregression) in EViews: model specification and Granger causality test · Cointegration in EViews: Engle-Granger and Johansen cointegration tests · GARCH models in EViews: volatility modelling for financial and economic data · Forecasting in EViews: in-sample fit, out-of-sample forecast, and forecast evaluation · Hands-on lab: complete a time-series analysis in EViews — unit root, OLS, ARIMA, and forecast

Data visualisation principles: choosing the right chart for the right message · Colour theory, typography, and layout for professional dashboards · Power BI fundamentals: Power BI Desktop, data sources, and the Power BI service · Connecting data in Power BI: Excel, SQL, CSV, web, and SharePoint sources · Data modelling in Power BI: relationships, star schema, and calculated columns · DAX basics: SUM, AVERAGE, CALCULATE, FILTER, RELATED, and time intelligence functions · Building Power BI reports: visuals, slicers, drill-through, and bookmarks · Power BI dashboards: pinning visuals, setting alerts, and sharing with stakeholders · Tableau fundamentals: Tableau Desktop, connecting data, and the interface · Building Tableau visualisations: bar, line, scatter, map, heat map, and tree map · Tableau calculated fields: basic formulas and level-of-detail (LOD) expressions · Tableau dashboards and stories: layout, actions, filters, and Tableau Public publishing · Power BI vs Tableau: when to use each tool and industry context · Hands-on lab: build identical dashboards in both Power BI and Tableau from the same dataset

How Generative AI is transforming the data analytics profession · Using ChatGPT, Claude, and Gemini to accelerate data analysis workflows · AI-assisted data cleaning: prompting AI to identify errors, suggest fixes, and generate cleaning scripts · Writing and debugging SQL with AI: generating queries, explaining errors, and optimising performance · Using AI to generate Excel formulas and Power Query transformations from plain English descriptions · AI-powered exploratory data analysis: asking AI to suggest analytical approaches for a dataset · Generating data visualisation code and chart recommendations using AI tools · Using AI to interpret statistical outputs: explaining regression results, p-values, and ANOVA tables in plain language · Building AI-assisted data reports: drafting insights, narratives, and executive summaries from raw findings · Microsoft Copilot in Excel and Power BI: using built-in AI features for analytics · Google Duet AI in Google Sheets and Looker Studio: AI-powered analysis and reporting · Prompt engineering for data analytics: writing effective prompts to get accurate, usable analytical outputs · Ethics and limitations of AI in analytics: hallucinations, data privacy, and over-reliance on AI outputs · Hands-on lab: complete a full data analysis project using AI tools at every stage — cleaning, analysis, visualisation, and reporting

Complete all 10 modules, pass a proctored online examination (minimum 75%), and submit a complete data analysis project including a cleaned dataset, an interactive dashboard, statistical outputs from at least two tools (SPSS, STATA, R, or EViews), and a 1,500-word written report of findings.