📘 1. Introduction to Data Analytics

  • What is Data Analytics?

  • Types of Data Analytics: Descriptive, Diagnostic, Predictive, Prescriptive

  • Applications of Data Analytics across Industries

  • The Data Analysis Process

🧠 2. Fundamentals of Data

  • Types of Data: Structured vs Unstructured

  • Data Types: Quantitative vs Qualitative

  • Data Collection Methods

  • Understanding Databases and Data Warehouses

🧹 3. Data Cleaning & Preparation

  • Importance of Data Quality

  • Handling Missing Values & Duplicates

  • Data Transformation: Normalization, Encoding

  • Tools: Microsoft Excel, Google Sheets, Python (Pandas)

📊 4. Data Visualization

  • Principles of Effective Data Visualization

  • Charts & Graphs (Bar, Line, Scatter, Pie, Heatmaps)

  • Dashboards and Storytelling with Data

  • Tools: Tableau, Power BI, Excel, Google Data Studio

🔢 5. Statistics for Data Analysis

  • Descriptive Statistics (Mean, Median, Mode, Variance, Standard Deviation)

  • Probability Basics

  • Distributions (Normal, Binomial)

  • Hypothesis Testing & Confidence Intervals

  • Correlation vs Causation

🐍 6. Excel & SQL for Data Analysis

Excel:

  • Functions (VLOOKUP, INDEX/MATCH, IF, SUMIFS)

  • Pivot Tables

  • Data Filtering & Validation

SQL:

  • Basics of Databases & RDBMS

  • SELECT, WHERE, GROUP BY, JOIN

  • Subqueries and Window Functions

  • SQL for Data Cleaning and Aggregation

🐍 7. Python for Data Analysis

  • Python Basics: Variables, Data Types, Loops

  • Libraries: NumPy, Pandas, Matplotlib, Seaborn

  • Data Manipulation with Pandas

  • Data Visualization with Matplotlib & Seaborn

🔮 8. Introduction to Predictive Analytics & Machine Learning

  • What is Predictive Modeling?

  • Overview of Regression, Classification

  • Introduction to Scikit-learn

  • Model Evaluation (Accuracy, Precision, Recall)

📈 9. Business Intelligence & Reporting

  • BI vs Data Analytics

  • Building Interactive Dashboards

  • Real-Time Reporting

  • KPI Tracking & Business Decision-Making

  • Tools: Power BI, Tableau, Looker Studio

🔐 10. Data Ethics & Governance

  • Data Privacy and Protection (GDPR, HIPAA Basics)

  • Ethical Use of Data

  • Data Governance Frameworks

🛠️ 11. Tools & Technologies Overview

  • Jupyter Notebooks

  • Google Colab

  • Cloud Platforms: Google BigQuery, AWS (Basics)

  • ETL Tools: Alteryx, Apache NiFi (Intro)

🧾 12. Final Project / Capstone

  • Real-World Dataset Analysis

  • Define Problem Statement

  • Clean, Analyze, Visualize & Present Data

  • Deliver Business Insights and Recommendations

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