Course Content :

Covers machine learning basics with real-world datasets.Includes Data Science capstone project, mock interviews, and resume workshops.What is Data Science?Data lifecycle & real-world applications.Tools used in Data Science. Importing data from multiple sources (CSV, SQL, APIs) Data cleaning (missing values, duplicates), Feature engineering. Descriptive statistics (mean, median, std), Probability distributions, Hypothesis testing.

What You Will Learn?

• Data visualization techniques, Correlation & trend analysis
• Case study: analyzing a sample dataset, Matplotlib, Seaborn advanced plots
• Dashboard basics in Power BI/Tableau, Regression, classification, clustering
• Model training & evaluation, Real-world mini project
• End-to-end project: Data collection → Cleaning → EDA → Visualization → Prediction model
• Example: Customer churn analysis / Sales forecasting
• Case-study based mock interviews (business problems → data solution)
• Resume workshops highlighting Data Science projects
• Job referrals to analytics & reporting companies

Outcome

• Job-ready Data Scientist / Data Analyst with skills in statistics, data visualization, SQL, and applied machine learning for real-world datasets.