Data Analytics Program
Master data collection, cleaning, analysis, visualization, and storytelling to make data-driven business decisions.
In today’s business world, data is the new oil. This program builds strong foundations in data handling, visualization, and analysis, enabling learners to extract insights, design reports, and support strategic decisions across industries.
₹4,000 + 18% GST
Week 1: Foundations of Data Analytics
- Introduction to Data Analytics & business importance
- Types of Data: Structured, Unstructured, Semi-structured
- Data Analytics lifecycle
- Overview of roles: Data Analyst, Data Scientist, Data Engineer
👉 Hands-on: Setup Python, Jupyter Notebook, Excel basics
- Types of Data: Structured, Unstructured, Semi-structured
- Data Analytics lifecycle
- Overview of roles: Data Analyst, Data Scientist, Data Engineer
👉 Hands-on: Setup Python, Jupyter Notebook, Excel basics
Week 2: Data Wrangling and Exploration
- Data collection methods (CSV, APIs, Databases)
- Data cleaning: missing values, outliers, formatting
- Exploratory Data Analysis (EDA) with Pandas, Matplotlib, Seaborn
- Descriptive statistics & data distributions
👉 Hands-on: Clean & analyze a public dataset (COVID, Sales, Iris)
- Data cleaning: missing values, outliers, formatting
- Exploratory Data Analysis (EDA) with Pandas, Matplotlib, Seaborn
- Descriptive statistics & data distributions
👉 Hands-on: Clean & analyze a public dataset (COVID, Sales, Iris)
Week 3: Data Visualization and Storytelling
- Principles of effective visualization
- Tools: Excel, Power BI/Tableau, Python libraries (Seaborn, Plotly, Matplotlib)
- Storytelling with data for business insights
👉 Hands-on: Build dashboards in Power BI & Python
- Tools: Excel, Power BI/Tableau, Python libraries (Seaborn, Plotly, Matplotlib)
- Storytelling with data for business insights
👉 Hands-on: Build dashboards in Power BI & Python
Week 4: Statistical Analysis and Predictive Modelling
- Probability, distributions, correlation
- Hypothesis testing, p-values, confidence intervals
- Linear regression & forecasting basics
- Intro to Machine Learning for predictions
👉 Hands-on: Perform regression analysis & visualize predictions
- Hypothesis testing, p-values, confidence intervals
- Linear regression & forecasting basics
- Intro to Machine Learning for predictions
👉 Hands-on: Perform regression analysis & visualize predictions
Week 5: Business Applications & Capstone Project
- Case studies: Retail, Finance, HR, Marketing
- KPI design & business reporting
- Creating actionable recommendations
👉 Hands-on: Capstone project (sales performance, churn analysis, or customer segmentation)
- KPI design & business reporting
- Creating actionable recommendations
👉 Hands-on: Capstone project (sales performance, churn analysis, or customer segmentation)
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Tools and Platforms
Python | Pandas | NumPy | Matplotlib | Seaborn | Excel | Power BI | Tableau | Google Sheets | Google Data Studio
Assessment
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Capstone Project
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