Data Analytics Course
Job-ready Data Analytics course in Tamil & English — Advanced Excel, SQL, Python, Power BI and Tableau with real business projects, capstone and 100% placement assistance.
Course overview
Data Analytics End to End: Turning Raw Business Data into Decisions with Excel, SQL, Python, Power BI and Tableau
Course Description:
Become a job-ready Data Analyst with GeekBase Technology. This course starts from absolute basics — no coding background needed — and walks you through the exact toolkit hiring companies ask for: advanced Excel, SQL, Python with Pandas, statistics for decision making, and business dashboards in Power BI and Tableau. Every module is taught through real business datasets from sales, marketing, HR and finance, so you finish with a portfolio of dashboards and a capstone project you can show in interviews, not just notes.
Key Highlights:
Target Audience:
Freshers and final-year students from any degree — engineering, commerce, arts or science — who want a analytics career without a heavy programming background.
Working professionals in operations, sales, finance, HR or support who already work with data in Excel and want to move into a dedicated Data Analyst role.
Anyone planning to grow into Data Science or Machine Learning later — this course builds the Python, SQL and statistics foundation those roles assume you already have.
Mentor Support:
Learners will have access to an experienced instructor who will provide support through one on one meeting, live Q&A sessions, and email to answer questions and provide guidance throughout the course.
Curriculum
12 modulesModule 1: Foundations of Data Analytics
- What a Data Analyst actually does: role, deliverables, and a day in the job.
- The analytics lifecycle: question, collect, clean, analyse, visualise, decide.
- Types of analytics: descriptive, diagnostic, predictive, prescriptive.
- Data types, data sources, and how business data is structured.
- Defining KPIs and metrics that a business actually acts on.
- How Data Analytics differs from Data Science, ML and Business Analysis.
Module 2: Advanced Excel for Analysts
- Spreadsheet essentials: referencing, formatting, and data validation.
- Core formulas: logical, text, date and statistical functions.
- Lookups that matter: VLOOKUP, XLOOKUP, INDEX-MATCH, and nested lookups.
- PivotTables, PivotCharts, slicers and grouped summaries.
- Data cleaning in Excel: duplicates, text-to-columns, Power Query basics.
- What-if analysis, Goal Seek, and Solver for business scenarios.
- Building a self-updating Excel dashboard from raw data.
Module 3: SQL Fundamentals for Data Analysis
- Relational databases, tables, keys and schema design basics.
- SELECT, WHERE, ORDER BY, LIMIT and filtering with operators.
- Aggregate functions: COUNT, SUM, AVG, MIN, MAX.
- GROUP BY and HAVING for segment-level analysis.
- String, date and numeric functions in SQL.
- CASE expressions for bucketing and conditional reporting.
Module 4: Advanced SQL — Joins, CTEs and Window Functions
- INNER, LEFT, RIGHT, FULL and SELF joins with real business tables.
- Subqueries and correlated subqueries.
- Common Table Expressions (CTEs) for readable, layered queries.
- Window functions: ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD.
- Running totals, moving averages and month-over-month growth in SQL.
- Query performance, indexes, and writing SQL that reviewers accept.
- SQL interview problem-solving drills.
Module 5: Python Programming for Analysts
- Python setup, Jupyter Notebook, and the analyst workflow.
- Variables, data types, operators and input/output.
- Control flow: conditionals, loops and comprehensions.
- Data structures: lists, tuples, dictionaries and sets.
- Functions, modules and error handling.
- Reading and writing files: CSV, Excel and JSON.
Module 6: Data Wrangling with NumPy and Pandas
- NumPy arrays: creation, indexing, slicing, broadcasting and vectorised math.
- Pandas Series and DataFrames: loading, inspecting and selecting data.
- Filtering, sorting, and conditional selection at scale.
- Handling missing data, duplicates, outliers and inconsistent types.
- GroupBy, aggregation, and pivot tables in Pandas.
- Merging, joining, concatenating and reshaping datasets.
- Working with dates, time series and rolling windows.
Module 7: Exploratory Data Analysis and Visualisation in Python
- The EDA workflow: profiling a dataset you have never seen before.
- Univariate, bivariate and multivariate analysis.
- Matplotlib: line, bar, scatter, histogram and subplot customisation.
- Seaborn: distribution, categorical, correlation and heatmap plots.
- Detecting outliers, skew and relationships that change the conclusion.
- Choosing the right chart for the question being asked.
Module 8: Statistics for Decision Making
- Descriptive statistics: mean, median, mode, variance, standard deviation.
- Probability basics, distributions, and the normal distribution.
- Sampling, sampling error and the central limit theorem.
- Confidence intervals and margin of error.
- Hypothesis testing: t-test, chi-square and p-value interpretation.
- A/B testing: designing an experiment and reading the result honestly.
- Correlation vs causation, and an introduction to linear regression.
Module 9: Business Intelligence with Power BI
- Power BI Desktop tour: reports, data and model views.
- Connecting to Excel, CSV and SQL data sources.
- Power Query: transforming, appending and merging data before load.
- Data modelling: relationships, star schema and cardinality.
- DAX essentials: calculated columns, measures, and time intelligence.
- Visuals, filters, slicers, bookmarks and drill-through.
- Publishing to Power BI Service, scheduled refresh and sharing.
Module 10: Dashboarding and Storytelling with Tableau
- Tableau Desktop basics: connections, dimensions and measures.
- Building core charts, maps, and dual-axis visuals.
- Calculated fields, parameters and table calculations.
- Level of Detail (LOD) expressions for advanced aggregation.
- Assembling interactive dashboards and guided story points.
- Dashboard design principles: layout, colour, hierarchy and accessibility.
- Presenting insights to non-technical stakeholders.
Module 11: Applied Business Analytics
- Sales and revenue analytics: funnel, cohort and retention analysis.
- Marketing analytics: campaign performance, CAC, ROI and attribution.
- HR analytics: attrition, headcount and workforce reporting.
- Finance analytics: budget variance, profitability and forecasting basics.
- Building a reporting pack that a manager can read in five minutes.
- Data ethics, privacy and responsible handling of business data.
Module 12: Capstone Projects and Career Launch
- End-to-end capstone: raw dataset to cleaned model to published dashboard.
- Three portfolio projects across different business domains.
- Publishing your work to GitHub and a shareable portfolio.
- Analyst resume and LinkedIn profile built around measurable outcomes.
- SQL, Excel and case-study interview preparation.
- Mock interviews with feedback, plus guidance on certification paths.
Certification
Course Certification:
Upon successful completion of the course, there will be cumulative test conducted and students who scored above 60% marks will receive a certificate of completion from GeekBase Technology, which can be used to showcase their newly acquired Data Analytics skills.
Note: Test will be a MCQ pattern and maximum two attempts allowed.
Why become a certified Data Analyst ?
Data Analyst is one of the fastest-growing entry points into the tech industry in India. Freshers typically start between ₹3.5 and ₹6 LPA, and analysts with two to four years of experience in SQL and business intelligence commonly move into the ₹8 to ₹15 LPA range. Because almost every sector — IT services, banking, e-commerce, healthcare, manufacturing and startups — now runs on reporting and dashboards, the skills transfer across industries instead of locking you into one.
It is also the most practical stepping stone into Data Science and Machine Learning. The SQL, Python and statistics you build here are the exact prerequisites those roles expect, so you can start earning as an analyst and upgrade later without starting over.
