Data Analytics Mastery: From Beginner to Job-Ready

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 Understand what data analytics is, its importance, and career opportunities.

 

Lessons:

1. What is Data Analytics?
    
2. Types of Data Analytics: Descriptive, Diagnostic, Predictive, Prescriptive
    
3. Career Paths in Data Analytics
    
4. Tools Used in Data Analytics (Excel, SQL, Python, Power BI, Tableau)
    

Assignment:

• Write a 300-word report on “How data analytics can transform a business.”

  Learn Excel fundamentals and data handling skills.

 

Lessons:

1. Basics of Excel: Interface, Formulas, and Functions
    
2. Data Cleaning & Formatting
    
3. Pivot Tables and Charts
    
4. Conditional Formatting & Logical Functions (IF, AND, OR)
    
5. Advanced Functions (VLOOKUP, HLOOKUP, INDEX-MATCH)
    

Assignment:

• Analyze a sample dataset, clean it, and create a dashboard of KPIs.

 Extract, manipulate, and query data from databases.
 

Lessons:

1. Introduction to Databases & SQL
    
2. SELECT, WHERE, GROUP BY, ORDER BY
    
3. JOINs (INNER, LEFT, RIGHT, FULL)
    
4. Aggregation & Subqueries
    
5. SQL Best Practices
    

Assignment:

• Query a sample database to generate reports like sales per region, top-selling products, etc.

 Present insights using visualizations.

 

Lessons:

1. Introduction to Data Visualization
    
2. Charts & Graphs in Excel
    
3. Introduction to Tableau / Power BI
    
4. Creating Interactive Dashboards
    
5. Storytelling with Data
    

Assignment:

• Build an interactive dashboard showing sales trends, top products, and customer behavior

 Learn statistical concepts and basic analytics techniques.

 

Lessons:

1. Descriptive Statistics (Mean, Median, Mode, Standard Deviation)
    
2. Probability & Distributions
    
3. Correlation & Regression
    
4. Hypothesis Testing Basics
    
5. Business Analytics Use Cases
    

Assignment:

• Perform statistical analysis on a dataset and derive insights for business decisions.

 Use Python for data manipulation and analysis.

 

Lessons:

1. Python Basics: Variables, Data Types, Loops
    
2. Libraries for Data Analytics: Pandas, NumPy
    
3. Data Cleaning and Manipulation
    
4. Data Visualization with Matplotlib & Seaborn
    
5. Mini Project: Sales Data Analysis
    

Assignment:

• Analyze a dataset in Python and generate visual insights.

 Apply all skills in a real-world project and prepare for interviews.
 

Lessons:

1. Capstone Project: End-to-End Data Analysis (Choose dataset, clean, analyze, visualize, and present insights)
    
2. Resume Building for Data Analytics Jobs
    
3. Common Data Analytics Interview Questions
    
4. How to Crack Case Studies in Interviews
    
5. Portfolio Creation
    

Assignment:

• Complete the capstone project and create a professional portfolio.