Data Analysis Training Program

1. Excel & Google Sheets (1 Month)

You'll learn data cleaning, formatting, complex formulas, and interactive dashboards that decision-makers actually use.

• Formulas & Functions: IF/IFS, VLOOKUP/XLOOKUP, HLOOKUP, INDEX-MATCH, TEXT/DATE functions for automated reporting.
• Data Cleaning: Remove duplicates, split/merge columns, handle blanks, standardise text—so datasets are "analysis-ready."
• Pivot Tables & Charts: Summarise large tables into KPIs; build slicer-driven dashboards for management reviews.
• Automation with VBA: Write macros to refresh reports, reshape files, and email summaries—cut manual work to minutes.
• Google Sheets for Collaboration: Real-time sharing, version history, app-scripts/add-ons for lightweight automation.

Project: Executive Sales Dashboard (auto-refresh + PDF export + KPI alerts).

2. SQL, MySQL & NoSQL (MongoDB) (1.5 Months)

You'll learn to query, transform, and optimise data from real databases used in apps and BI tools.

Relational SQL (MySQL):

• Core Querying: SELECT/FROM/WHERE, ORDER BY/LIMIT, GROUP BY with COUNT/SUM/AVG/MIN/MAX.
• Joins: INNER/LEFT/RIGHT/FULL to stitch customer, order, product, and transaction tables.
• Logic & Subqueries: CASE expressions; subqueries/derived tables for complex filters.
• Window Functions: ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD to build cohort analyses and trends.
• CTEs & Performance: CTEs for readable analytics; indexes & EXPLAIN plans for faster queries.
• DML & Governance: INSERT/UPDATE/DELETE/MERGE; transactions (BEGIN/COMMIT/ROLLBACK) for data integrity.

NoSQL (MongoDB):

• JSON Documents & CRUD: Flexible schemas for clickstreams/catalogues.
• Aggregation Pipeline: Grouping, lookups, projections for semi-structured analytics at scale.
• Indexes: Speed up reads on high-traffic collections.

Tools (with purpose): MySQL Workbench/SSMS for query IDEs; pandas-sqlalchemy to pull data into Python; Power BI/Looker to visualise SQL outputs.

Project: Customer Segmentation using SQL joins + window functions; product funnel in MongoDB Aggregation Pipeline.

3. Python for Data Analysis (2 Months)

You'll learn to clean, analyse, visualise and automate using production-grade Python.

• Core Python → OOP: Data types, loops, functions, error handling, OOP for reusable analytics utilities; file I/O (CSV/JSON/Excel).
• NumPy: Vectorised calculations, broadcasting, matrix ops for fast number-crunching.
• Pandas: Data wrangling, joins/merge, groupby, missing-value treatment, feature creation; time-series resampling.
• Visualisation: Matplotlib/Seaborn for trends, distributions, correlations; export to images/PDF for stakeholders.
• Intro ML (scikit-learn): Train/test split, scaling/encoding, linear/logistic regression, k-means for first predictive insights.
• Automation: Batch jobs to clean files, refresh KPIs, and email dashboards.

Project: E-commerce EDA + churn-risk early-warning report (Python notebooks → HTML executive brief).

4. Business Intelligence & Data Visualisation (1 Month)

You'll learn to convert raw tables into C-suite dashboards with Power BI & Looker Studio.

• Power BI Desktop → Service → Mobile: Data modelling, relationships, DAX measures, drilldowns, Row-Level Security, scheduled refresh, mobile views.
• Looker Studio: Live connectors (Sheets/BigQuery/MySQL), interactive scorecards, shareable links for stakeholders.

Project: Company-wide KPI Command Center (Sales, Inventory, Marketing, Finance) with scheduled refresh & RLS.