Data Analytics
Course Level
Beginner to Advanced
Duration
4 Months
Chapter Overview
1.1 What is Data Analytics?
1.2 Importance of Data-Driven Decision Making
1.3 Types of Data: Structured, Semi-structured, Unstructured
1.4 Data Analyst Roles and Responsibilities
1.5 Overview of the Data Lifecycle
1.6 Real-World Applications of Data Analytics
1.2 Importance of Data-Driven Decision Making
1.3 Types of Data: Structured, Semi-structured, Unstructured
1.4 Data Analyst Roles and Responsibilities
1.5 Overview of the Data Lifecycle
1.6 Real-World Applications of Data Analytics
2.1 Introduction to Excel for Data Analysis
2.2 Sorting, Filtering, and Conditional Formatting
2.3 Advanced Formulas (VLOOKUP, HLOOKUP, INDEX-MATCH)
2.4 Pivot Tables and Pivot Charts
2.5 Data Visualization in Excel
2.6 Hands-On Project: Analyzing Sales Data
2.2 Sorting, Filtering, and Conditional Formatting
2.3 Advanced Formulas (VLOOKUP, HLOOKUP, INDEX-MATCH)
2.4 Pivot Tables and Pivot Charts
2.5 Data Visualization in Excel
2.6 Hands-On Project: Analyzing Sales Data
3.1 Introduction to Databases and RDBMS
3.2 Basic SQL Commands (SELECT, WHERE, ORDER BY)
3.3 Joins (INNER, LEFT, RIGHT, FULL)
3.4 Aggregate Functions (COUNT, AVG, SUM)
3.5 Subqueries and Nested Queries
3.6 Window Functions and CTEs
3.7 Real-World Use Case: Querying a Customer Database
3.2 Basic SQL Commands (SELECT, WHERE, ORDER BY)
3.3 Joins (INNER, LEFT, RIGHT, FULL)
3.4 Aggregate Functions (COUNT, AVG, SUM)
3.5 Subqueries and Nested Queries
3.6 Window Functions and CTEs
3.7 Real-World Use Case: Querying a Customer Database
4.1 Introduction to Python and Its Importance in Data Analysis
4.2 Essential Libraries: NumPy, Pandas, Matplotlib, Seaborn
4.3 Data Manipulation with Pandas (DataFrames, Series, Indexing)
4.4 Exploratory Data Analysis (EDA)
4.5 Data Visualization with Matplotlib and Seaborn
4.6 Handling Missing Data and Outliers
4.7 Project: Analyzing a Dataset Using Python
4.2 Essential Libraries: NumPy, Pandas, Matplotlib, Seaborn
4.3 Data Manipulation with Pandas (DataFrames, Series, Indexing)
4.4 Exploratory Data Analysis (EDA)
4.5 Data Visualization with Matplotlib and Seaborn
4.6 Handling Missing Data and Outliers
4.7 Project: Analyzing a Dataset Using Python
5.1 Introduction to Data Visualization
5.2 Tools: Tableau, Power BI, Google Data Studio
5.3 Creating Dashboards and Interactive Reports
5.4 Storytelling with Data: Best Practices
5.5 Real-World Project: Building a Business Dashboard
5.2 Tools: Tableau, Power BI, Google Data Studio
5.3 Creating Dashboards and Interactive Reports
5.4 Storytelling with Data: Best Practices
5.5 Real-World Project: Building a Business Dashboard
6.1 Descriptive Statistics: Mean, Median, Mode, Variance
6.2 Inferential Statistics: Hypothesis Testing, Confidence Intervals
6.3 Probability Concepts: Conditional Probability, Bayes’ Theorem
6.4 Correlation vs. Causation
6.5 A/B Testing and Experiment Analysis
6.2 Inferential Statistics: Hypothesis Testing, Confidence Intervals
6.3 Probability Concepts: Conditional Probability, Bayes’ Theorem
6.4 Correlation vs. Causation
6.5 A/B Testing and Experiment Analysis
7.1 Importance of Data Cleaning
7.2 Handling Missing Data, Duplicates, and Errors
7.3 Data Transformation and Feature Engineering
7.4 Scaling and Normalization
7.5 Building Data Pipelines
7.2 Handling Missing Data, Duplicates, and Errors
7.3 Data Transformation and Feature Engineering
7.4 Scaling and Normalization
7.5 Building Data Pipelines
8.1 Introduction to Business Analytics
8.2 Understanding Business Metrics (KPIs, ROI)
8.3 Case Studies Across Industries: Finance, Healthcare, Retail
8.4 Translating Business Problems into Data Problems
8.5 Developing Data-Driven Strategies
8.2 Understanding Business Metrics (KPIs, ROI)
8.3 Case Studies Across Industries: Finance, Healthcare, Retail
8.4 Translating Business Problems into Data Problems
8.5 Developing Data-Driven Strategies
9.1 Sourcing Real-World Datasets (Kaggle, Google BigQuery)
9.2 End-to-End Project: Data Extraction, Cleaning, Analysis, and Reporting
9.3 Creating an Impressive Project Portfolio
9.4 Presenting Insights to Non-Technical Stakeholders
9.2 End-to-End Project: Data Extraction, Cleaning, Analysis, and Reporting
9.3 Creating an Impressive Project Portfolio
9.4 Presenting Insights to Non-Technical Stakeholders
10.1 Resume Writing for Data Analyst Roles
10.2 Building a Data Analyst Portfolio
10.3 Mock Interviews (Technical and Behavioral)
10.4 Networking and LinkedIn Optimization
10.2 Building a Data Analyst Portfolio
10.3 Mock Interviews (Technical and Behavioral)
10.4 Networking and LinkedIn Optimization
