Data Analytics: Excel, SQL, Power BI & Python

What you will be able to do

• Frame a business question and identify the data needed to answer it
• Clean messy data in spreadsheets and Power Query, and document every change
• Analyse data with formulas, lookups and PivotTables
• Query and combine relational data with SQL, including joins, CTEs and window functions
• Load, clean, summarise and chart data with Python and pandas
• Define KPIs precisely and build an interactive, accessible Power BI dashboard
• Present findings as a clear, honest data story with stated limitations
• Publish a documented portfolio case study that respects data privacy and licences

About this course

Syllabus

This syllabus is a draft awaiting instructor review. Topics, order and schedule may change before the first batch is announced.

1. 01Data thinking and spreadsheet foundations+4 live sessions · 8 hours · How analysis turns questions into evidence, and the spreadsheet skills every analyst relies on.

   Learning objectives

   • Describe the analysis workflow from question to decision
   • Recognise data types, tidy data and common data-quality problems
   • Explain the basics of personal data, consent and fair use of data
   • Use formulas, references, lookups and conditional functions accurately

   Live sessions

   1. 01How data analysis works2 hAsking answerable questions, the analysis workflow, data types and tidy data, where business data comes from, personal data and India’s DPDP Act 2023 in brief, checking a dataset’s licence; touring the course dataset.
   2. 02Spreadsheet essentials2 hTables and structured references, relative and absolute references, arithmetic, text and date functions, number formats, and keeping raw data separate from analysis.
   3. 03Logic, lookups and conditional summaries2 hIF and IFS, AND and OR, XLOOKUP and INDEX/MATCH, COUNTIFS, SUMIFS and AVERAGEIFS, handling lookup errors without hiding real problems.
   4. 04Spreadsheet review and doubt-clearing2 hGuided exercises on lookups and conditional summaries; finding and fixing broken formulas; open questions.

   Practice

   Answer fifteen questions about the stationery chain’s sales using formulas and lookups, checked against expected results.

   Assessment

   Quiz on data types, references and lookups, plus a short spreadsheet assignment.

   You will be able to

   You can explore a dataset in a spreadsheet and answer straightforward business questions accurately.

2. 02Cleaning and analysing data in spreadsheets+4 live sessions · 8 hours · Find and fix data-quality problems repeatably, then summarise and chart the clean data.

   Learning objectives

   • Detect duplicates, inconsistent categories, invalid dates and missing values
   • Build repeatable cleaning steps with Power Query instead of manual edits
   • Summarise data with PivotTables and choose an appropriate chart
   • Document cleaning decisions so others can review them

   Live sessions

   1. 05Data cleaning in spreadsheets2 hProfiling a dataset, removing duplicates, trimming and standardising text, fixing dates and numbers stored as text, deciding what to do with missing values, data validation rules.
   2. 06Repeatable cleaning with Power Query2 hImporting CSV and Excel files, applied steps, splitting and merging columns, unpivoting, appending and merging queries, refreshing when new data arrives.
   3. 07PivotTables and charts2 hPivotTables, grouping by date, calculated fields, slicers, choosing between bar, line and scatter charts, avoiding misleading axes.
   4. 08Cleaning workshop and doubt-clearing2 hCleaning a deliberately messy public dataset end to end and writing a cleaning log; open questions.

   Practice

   Clean a year of the chain’s raw sales exports with Power Query, then build a PivotTable summary by store, category and month.

   Assessment

   Assignment: the cleaned workbook with a cleaning log and three PivotTable-based answers.

   You will be able to

   You can turn messy raw data into a clean, documented dataset and summarise it reliably.

3. 03SQL for analysis+5 live sessions · 10 hours · Pull, combine and summarise data directly from a relational database with SQL, using PostgreSQL.

   Learning objectives

   • Explain tables, keys and relationships in a relational database
   • Filter, sort, join and aggregate data with SQL
   • Use CASE, CTEs and date functions for reporting questions
   • Use window functions for rankings, running totals and period comparisons

   Live sessions

   1. 09Databases and SELECT2 hTables, rows, primary and foreign keys; installing PostgreSQL and a query tool; loading the course dataset; SELECT, WHERE, ORDER BY, LIMIT and NULL handling.
   2. 10Joins and aggregation2 hInner and left joins, avoiding accidental row multiplication, COUNT, SUM and AVG, GROUP BY and HAVING, checking results against a spreadsheet.
   3. 11CASE, CTEs and dates2 hCASE for grouping and flags, WITH clauses for readable multi-step queries, subqueries, date truncation and intervals for monthly and weekly reporting.
   4. 12Window functions for analysis2 hOVER and PARTITION BY, ROW_NUMBER and RANK, running totals, LAG for month-on-month change, a simple customer cohort query.
   5. 13SQL review and doubt-clearing2 hA reporting workshop on the course database; reviewing each other’s queries for correctness and clarity; open questions.

   Practice

   A reporting pack of twelve questions on sales, returns and repeat customers, answered in SQL.

   Assessment

   Timed quiz on joins, aggregation and window functions (auto-marked).

   You will be able to

   You can answer multi-table business questions directly from a database with SQL.

4. 04Python and pandas for analysts+4 live sessions · 8 hours · Make cleaning and analysis repeatable with Python, pandas and notebooks — no prior programming needed.

   Learning objectives

   • Write basic Python: variables, types, lists, dictionaries, conditions, loops and functions
   • Load, inspect, select and filter data with pandas
   • Clean, group, merge and reshape data in pandas
   • Create clear charts with pandas and matplotlib

   Live sessions

   1. 14Python essentials for analysts2 hInstalling Python and Jupyter, variables and types, lists and dictionaries, conditions, loops and functions, reading error messages.
   2. 15Loading and exploring data with pandas2 hDataFrames and Series, reading CSV and Excel files, info and describe, selecting columns and rows, filtering with conditions, sorting.
   3. 16Cleaning, grouping and merging with pandas2 hData types and conversion, missing values, duplicates, string and date handling, groupby and aggregation, merge, pivot_table and melt.
   4. 17Charts in Python and doubt-clearing2 hLine, bar, histogram and scatter charts with pandas and matplotlib, labelling and accessible colours, exporting results; open questions.

   Practice

   Repeat the Power Query cleaning of the sales exports in a pandas notebook and compare the results row for row.

   Assessment

   Assignment: a documented notebook that cleans a new month of data and produces three charts.

   You will be able to

   You can use Python and pandas to clean and analyse data in a repeatable, reviewable way.

5. 05KPIs and dashboards with Power BI+4 live sessions · 8 hours · Define the right measures, model data properly and build dashboards that people can trust and use.

   Learning objectives

   • Define KPIs precisely, with a formula, owner, data source and known limitations
   • Build a star-schema data model in Power BI from cleaned data
   • Write DAX measures, including simple time comparisons
   • Design an accessible dashboard for a specific audience

   Live sessions

   1. 18KPIs and metric design2 hGoals versus metrics, leading and lagging indicators, ratios and averages done correctly, metric definitions, how metrics get gamed, choosing a small set of KPIs for the case business.
   2. 19Power BI data modelling2 hPower BI Desktop interface, Power Query in Power BI, fact and dimension tables, star schemas, relationships and filter direction, a date table.
   3. 20DAX measures2 hCalculated columns versus measures, SUM, DIVIDE and CALCULATE, filter context in plain language, year-to-date and month-on-month comparisons.
   4. 21Dashboard design and doubt-clearing2 hLayout for an audience, choosing visuals, slicers and drill-through, accessible colour and alt text, what publishing and sharing involve; open questions.

   Practice

   Build a sales and returns dashboard for the stationery chain with a written definition for every KPI.

   Assessment

   Assignment: the Power BI file and a one-page KPI definition sheet, reviewed with feedback.

   You will be able to

   You can define meaningful KPIs and build a well-modelled, accessible Power BI dashboard.

6. 06Data storytelling and portfolio case study+3 live sessions · 6 hours · Turn analysis into a clear, honest recommendation, then complete and present the portfolio case study.

   Learning objectives

   • Structure findings as a story with a question, evidence and recommendation
   • Choose honest charts and state uncertainty and limitations
   • Present a case study live and respond to questions
   • Publish a documented, privacy-respecting portfolio project

   Live sessions

   1. 22Data storytelling and honest presentation2 hKnowing the audience, one message per chart, annotations, misleading charts and how to avoid them, correlation versus causation, writing an executive summary with limitations.
   2. 23Case study build lab and review2 hGuided time on the case study with individual check-ins on cleaning, queries, dashboard and story; preparing the GitHub README; doubt-clearing.
   3. 24Final assessment and case study presentations2 hWritten and practical assessment across the course (pass mark 60%); live case study presentations with feedback; next steps for continued learning.

   Practice

   Complete the case study and rehearse the five-minute presentation with a peer before the final session.

   Assessment

   Portfolio case study submission and live presentation, and the final assessment (pass mark 60%).

   You will be able to

   You can take a business question from raw data to a clear, defensible recommendation and show the work in a portfolio.

Teaching formats and fees

Every format teaches the full syllabus live, with the same projects, assessments and certificate rules. The fee is per student for the whole course.

Launch fee — GST treatment to be confirmed.

Fees are set in Indian rupees. Amounts in other currencies are estimates; the amount and currency you would pay are shown before payment once online enrolment opens.

Schedules and batches

Choose the schedule that fits your week. Both teach the same syllabus and the same 48 live hours; only the number of weeks differs.

• Weekday Intensive classes are one hour, so each 2 h syllabus session is taught across consecutive weekday classes.
• Weekend Evening classes run on Saturday and Sunday evenings; the exact time and time zone are set before a batch is confirmed.
• Public holidays are not class days unless the instructor confirms a class. Holidays, instructor leave and cancelled classes move the end date later so no live hours are lost.
• Plan about 5 hours a week of exercises, assignments and project work outside class.
• Group, Duo and One-to-One courses all follow one of these two schedules.

Final project

Certificate

Tools and skills

Where these skills apply

• Business and operations reporting
• Sales, marketing and customer analysis
• Finance and budget tracking with data
• Building and maintaining KPI dashboards
• Data preparation for data science and machine-learning work
• Bringing evidence into decisions in any role that handles data

We help you prepare for work; we do not guarantee jobs, placements or salaries. Career resources

Questions about Data Analytics: Excel, SQL, Power BI & Python