Data Analytics Course Syllabus – Best Data Analytics Training in Trichy
Looking to learn Data Analytics from scratch or upgrade your skills? Our Data Analytics training in Trichy is divided into well-structured, industry-relevant modules covering everything from Excel fundamentals to advanced dashboard development and Python-based analytics.

Module 1: Introduction to Data Analytics
• What is Data Analytics? Types: Descriptive, Diagnostic, Predictive, Prescriptive
• Data Analytics Lifecycle: Collection → Cleaning → Analysis → Visualisation → Insights
• Key Tools Overview: Excel, SQL, Python, Power BI, Tableau — when to use which
• Understanding Data Types: Structured, Unstructured, Semi-Structured
• Real-world use cases: How top companies (Amazon, Flipkart, HDFC) use analytics
• Setting Up the Analytics Environment (Python, Jupyter Notebook, Anaconda)
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Module 2: Microsoft Excel for Data Analytics
• Advanced Excel Formulas: VLOOKUP, XLOOKUP, INDEX-MATCH, IF, SUMIFS, COUNTIFS
• Pivot Tables and Pivot Charts for instant data summarisation
• Data Cleaning in Excel: Remove duplicates, handle blanks, text functions
• Conditional Formatting and dynamic dashboards in Excel
• What-If Analysis: Goal Seek, Scenario Manager, Data Tables
• Excel Power Query – Import and transform large datasets
• Excel Power Pivot – Build basic data models
• Mini-Project: Build a Sales Performance Dashboard in Excel
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Module 3: SQL for Data Analytics
• SQL Fundamentals: SELECT, WHERE, ORDER BY, GROUP BY, HAVING
• Joins: INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN, SELF JOIN
• Subqueries, CTEs (WITH clause), and Nested Queries
• Window Functions: ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, PARTITION BY
• Aggregate Functions: SUM, COUNT, AVG, MIN, MAX with GROUP BY
• Data Cleaning with SQL: TRIM, REPLACE, CAST, NULLIF, COALESCE
• Stored Procedures, Views, and Indexes for performance
• Mini-Project: Analyse an E-Commerce sales database using SQL
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Module 4: Python for Data Analytics
• Python Basics: Variables, Lists, Dictionaries, Loops, Functions
• NumPy – Array operations, mathematical computations, broadcasting
• Pandas – DataFrames, Series, merging, groupby, pivot tables
• Data Cleaning with Pandas: Handle missing values, duplicates, outliers
• Exploratory Data Analysis (EDA) – summarise and understand datasets
• Matplotlib & Seaborn – Create line, bar, scatter, heatmap, box plots
• Reading/Writing CSV, Excel, JSON, and SQL data in Python
• Mini-Project: Full EDA on a real-world dataset (HR / Sales / Finance)
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Module 5: Statistics & Probability for Data Analytics
• Descriptive Statistics: Mean, Median, Mode, Variance, Standard Deviation
• Probability Basics: Events, Distributions, Bayes Theorem
• Normal Distribution, Z-Score, and Confidence Intervals
• Hypothesis Testing: T-Test, Chi-Square Test, ANOVA
• Correlation and Regression Analysis (Simple & Multiple Linear Regression)
• Sampling Techniques and Survey Design
• Statistical Analysis using Python (SciPy, StatsModels)
• Mini-Project: Statistical Report on a real business dataset
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Module 6: Power BI – Business Intelligence & Dashboards
• Power BI Desktop: Interface, Data Sources, Import vs DirectQuery
• Power Query Editor – Data transformation and shaping
• Data Modelling: Relationships, Star Schema, Snowflake Schema
• DAX (Data Analysis Expressions): Calculated Columns, Measures, KPIs
• DAX Functions: CALCULATE, SUMX, FILTER, RELATED, TIME INTELLIGENCE
• Building Interactive Dashboards: Slicers, Filters, Drill-Through, Bookmarks
• Power BI Service – Publish, Share, Schedule Refresh, Row-Level Security
• Mini-Project: Build a complete Sales Analytics Dashboard in Power BI
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Module 7: Tableau – Data Visualisation & Storytelling
• Tableau Desktop: Interface, Data Connection, Extract vs Live
• Building Charts: Bar, Line, Pie, Scatter, Treemap, Heatmap, Waterfall
• Calculated Fields, Table Calculations, Level of Detail (LOD) Expressions
• Filters: Quick Filters, Context Filters, Extract Filters, Data Source Filters
• Parameters and Dynamic Dashboards in Tableau
• Tableau Dashboard Design: Layout Containers, Actions, Stories
• Tableau Public – Publish and share your dashboards online
• Mini-Project: Build an interactive HR/Sales Analytics Story in Tableau
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Module 8: Real-World Projects, Case Studies & Career Prep
• End-to-End Analytics Project: Collect → Clean → Analyse → Visualise → Present
• Domain Projects: Finance dashboard, Healthcare KPI report, Retail sales analysis
• Storytelling with Data – How to present insights to business stakeholders
• GitHub Portfolio – Push all projects and create a professional analytics portfolio
• Resume Writing for Data Analyst roles: keywords, structure, and ATS optimisation
• LinkedIn Profile Optimisation for Data Analytics job search
• Mock Interviews: SQL queries, Python pandas tasks, Power BI scenario questions
• Data Analytics Certification Guidance: Microsoft PL-300 (Power BI), Google Data Analytics

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