Data Analytics Foundations
Level: Beginner → Intermediate
Duration: 1 Month
What You'll Learn
01. Data Science & Analytics Fundamentals
- What is Data?
- Data Science vs Data Analytics
- Types of Data
- Structured & Unstructured Data
- Data Analytics Lifecycle
- Descriptive, Diagnostic, Predictive & Prescriptive Analytics
- Real-world Applications of Data Analytics
02. Excel for Data Analysis
- Excel Interface & Data Entry
- Sorting & Filtering
- Data Formatting
- Data Validation
- Basic & Advanced Formulas
- IF, SUMIF, COUNTIF
- VLOOKUP / XLOOKUP
- INDEX & MATCH
- Conditional Formatting
- Excel Tables
- Pivot Tables
- Pivot Charts
- Basic Excel Dashboards
03. Statistics Fundamentals
- Mean, Median & Mode
- Range
- Variance & Standard Deviation
- Percentiles & Quartiles
- Data Distribution
- Outliers
- Correlation Basics
- Probability Fundamentals
04. Python Fundamentals for Analytics
- Python Basics
- Variables & Data Types
- Operators
- Conditions & Loops
- Functions
- Lists, Tuples & Dictionaries
- Exception Handling
- Working with Files
05. Development Environment
- VS Code
- Jupyter Notebook
- Google Colab
- Python Environment Setup
- Basic Debugging
06. Foundation Projects
- Student Performance
- Sales Report & Dashboard
- Basic Python Data Analysis
Data Analytics & Business Intelligence
Level: Intermediate → Advanced
Duration: 2 Months
What You'll Learn
01. NumPy for Data Analysis
- NumPy Arrays
- Array Creation
- Indexing & Slicing
- Array Operations
- Mathematical Functions
- Statistical Functions
- Vectorized Operations
02. Pandas — Data Manipulation
- Series & DataFrames
- Importing CSV & Excel Files
- Data Inspection
- Selecting & Filtering Data
- Sorting Data
- Handling Missing Values
- Removing Duplicates
- Data Transformation
- GroupBy & Aggregation
- Merge & Join
- Pivot Tables
- Exporting Data
03. Data Cleaning & Preparation
- Identifying Data Quality Issues
- Missing Data
- Duplicate Data
- Incorrect Data Types
- Outlier Detection
- Data Standardization
- Data Transformation
- Preparing Data for Analysis
04. Exploratory Data Analysis — EDA
- Understanding a Dataset
- Univariate Analysis
- Bivariate Analysis
- Multivariate Analysis
- Finding Patterns & Trends
- Correlation Analysis
- Outlier Analysis
- Generating Data Insights
05. Data Visualization
- Matplotlib
- Seaborn
- Bar Charts
- Line Charts
- Pie Charts
- Histograms
- Scatter Plots
- Box Plots
- Heatmaps
- Choosing the Right Chart
- Data Storytelling
6. SQL & Database Analytics
- What is a Database?
- Relational Databases
- Tables, Rows & Columns
- Primary & Foreign Keys
- MySQL
- SQL SELECT
- WHERE & ORDER BY
- GROUP BY & HAVING
- Aggregate Functions
- INNER JOIN
- LEFT JOIN
- RIGHT JOIN
- Subqueries
- CASE Statements
- Common Table Expressions — CTE
- Window Functions
- Real-world Business Queries
07. Power BI — Business Intelligence
- Introduction to Power BI
- Power BI Desktop
- Importing Data
- Excel / CSV / Database Connections
- Power Query
- Data Cleaning in Power Query
- Data Relationships
- Data Modeling
- Calculated Columns
- Measures
- Basic DAX
- KPIs - Key Performance Indicator
- Interactive Charts
- Filters & Slicers
- Drill-down
- Dashboard Design
- Publishing Reports
08. Tableau — Data Visualization
- Tableau Interface
- Connecting Data
- Worksheets
- Charts & Graphs
- Filters
- Calculated Fields
- Dashboards
- Interactive Visualizations
- Basic Data Storytelling
Projects
- E-Commerce Sales Analysis
- Customer Data Analysis
- Business Performance Dashboard
- Sales Dashboard using Power BI
- SQL Business Analysis Project
Advanced Analytics & Predictive Data Science
Level: Advanced → Data Professional
Duration: 2 Months
What You'll Learn
01. Advanced Statistics
- Probability Distributions
- Normal Distribution
- Sampling
- Confidence Intervals
- Central Limit Theorem — Concept
- Hypothesis Testing
- Null & Alternative Hypothesis
- p-value
- T-Test
- Chi-Square Test
- ANOVA
- A/B Testing Fundamentals
02. Advanced SQL Analytics
- Advanced JOINs
- CTEs
- Subqueries
- Window Functions
- Ranking Functions
- ROW_NUMBER
- RANK & DENSE_RANK
- Running Totals
- Moving Averages
- Date & Time Analysis
- Business Case Queries
03. Predictive Analytics
- What is Predictive Analytics?
- Predictive vs Descriptive Analytics
- Regression Fundamentals
- Linear Regression
- Multiple Regression
- Model Training & Testing
- Prediction & Forecasting
- Model Evaluation
- MAE (Mean Absolute Error)
- MSE (Mean Squared Error)
- RMSE (Root Mean Squared Error)
- R² Score (R-Squared / Coefficient of Determination)
04. Machine Learning for Data Analysts
- Introduction to Machine Learning
- Supervised vs Unsupervised Learning
- Train-Test Split
- Feature & Target Variables
- Feature Scaling
- Encoding Categorical Data
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forest
- K-Means Clustering
- Model Evaluation
- Overfitting & Underfitting
05. Business Analytics
- Sales Analytics
- Customer Analytics
- Marketing Analytics
- Financial Analytics
- HR Analytics
- E-Commerce Analytics
- KPI Analysis
- Customer Segmentation
- Churn Analysis
- Revenue & Profit Analysis
- Trend Analysis
06. Time Series & Forecasting — Basics
- Time Series Data
- Trend
- Seasonality
- Moving Average
- Forecasting Concepts
- Sales Forecasting
- Demand Forecasting
07. Data Storytelling & Presentation
- Turning Data into Insights
- Identifying Key Findings
- Creating Executive Reports
- Dashboard Storytelling
- Presenting Data to Non-Technical Users
- Business Recommendations from Data
08. Portfolio & Capstone Projects
- Students complete an end-to-end project:
Capstone Project Options
- E-Commerce Customer Analytics
- Sales & Revenue Intelligence
- Customer Churn Analysis
- Marketing Campaign Analysis
- Retail Business Analytics
- Employee / HR Analytics
- Sales Forecasting Project
