Advance Data Analytics Mastery
Comprehensive Course Syllabus (Excel, SQL, Power BI, Python)
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Duration: 6-8 Months
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Level: Beginner to Pro
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π 1. Introduction to Data AnalyticsβΌ
- π― Business Objectives & Classification of Data
- π Industry Relevance & Analytics Methodology
- π Case Studies: How Leading Companies Use Data
- π§° Analytics Tools & Problem-Solving Framework
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π 2. Advanced Excel for Data ScienceβΌ
- π VLOOKUP, XLOOKUP, MATCH, INDEX (Dynamic)
- π Pivot Tables, Slicers & Pivot Charts
- π’ 60+ Advanced Functions (Financial, Logical, Text)
- π― What-If Analysis (Goal Seek, Scenario Manager)
- β Data Validation, Sorting & Advanced Filtering
- π€ AI Tools in Excel & Automation Basics
- π Final Practical Assignment & Assessments
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π’οΈ 3. SQL Server (Database Management)βΌ
- π οΈ SQL Architecture & SSMS Configuration
- π T-SQL: Insert, Update, Delete & Complex Select
- π Primary Key, Composite Key & Constraints
- β‘ Indexing, Query Tuning & Joins in Views
- π¦ Stored Procedures & Dynamic SQL
- π₯ Triggers, Cursors & Transactions (ACID)
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π 4. Power BI (Data Visualization)βΌ
- π₯ Data Connectivity (SQL, Excel, CSV Integration)
- π DAX Functions, Measures & Calculated Columns
- π Power Query: Append, Merge & Unpivot
- π Data Modelling: Star Schema & Cardinality
- π Advanced Visuals: KPIs, Maps, Gauges & Slicers
- π Publishing, Sharing & Row-Level Security
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π 5. Python Programming for AnalyticsβΌ
- βοΈ Basics: Variables, Operators & Control Flow
- π§© Functions, Lambda, Modules & Packages
- π Lists, Tuples, Dictionaries & Comprehensions
- β οΈ Exception Handling & File Handling
- π Managing Workbooks & SQL Connections
- π§° OS Module & Directory Management
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π 6. Libraries & VisualizationβΌ
- πΌ Pandas: Data Cleaning & Manipulation
- π’ NumPy: Numerical Calculations & Arrays
- π Matplotlib & Seaborn: Customizing Charts
- π Statistical Analysis & Insights Generation
- β Final Capstone Project & Project Work




