📊 Data Science for Data Analyst Career

🕒 Duration: 8 Weeks
🎯 Prerequisite: Basic knowledge of Python and statistics
🛠️ Tools & Tech:

• Python

• Jupyter Notebooks / Google Colab

• Pandas – Data manipulation

• NumPy – Numerical operations

• Matplotlib & Seaborn – Data visualization

• Scikit-learn – Simple modeling and preprocessing

• Power BI / Tableau – Business dashboards

📚 Topics Covered:

1. Introduction to Data Science

   • What is Data Science? Applications & career paths

   • Data Science vs Analytics vs Business Intelligence

   • Data lifecycle: collection → analysis → visualization → decision

2. Data Handling with Pandas & NumPy

   • Reading data from CSV/Excel/JSON

   • DataFrames, indexing, filtering, sorting

   • Aggregations, joins, and groupby

   • Handling missing values and duplicates

3. Data Cleaning & Preprocessing

   • Data types, type casting, parsing dates

   • Outlier detection, handling missing/null values

   • Feature encoding (label, one-hot), feature scaling

   • Creating new features from raw data

4. Exploratory Data Analysis (EDA)

   • Summary statistics

   • Correlation analysis

   • Visual storytelling with charts (bar, pie, histogram, heatmaps)

   • Identifying trends and relationships

5. Data Visualization with Matplotlib & Seaborn

   • Line plots, scatter plots, boxplots

   • Customizing plots, color palettes

   • Pairplots, distribution plots

   • Visualizing feature importance and group trends

6. Introduction to Predictive Modeling (Basic ML)

   • Regression and classification examples with Scikit-learn

   • Model training, evaluation using accuracy, confusion matrix

   • Train-test split, simple prediction examples

7. Dashboards & Business Insights (Overview)

   • Overview of Power BI / Tableau

   • Creating KPI dashboards

   • Connecting visualizations to real data

   • Telling a story with data

8. Mini Projects

   • Project 1: Analyze student performance or sales data

   • Project 2: Build a simple prediction model (e.g., house prices)

   • Optional: Dashboard-based report with visual insights

✅ Outcome:

By the end of the course, learners will be able to analyze, clean, visualize, and present data effectively, and build simple ML models to extract insights. They will also be introduced to dashboarding tools to communicate results professionally.