1. Introduction to Data Science
• What is Data Science?
• Data Science lifecycle and applications.
• Tools and technologies overview.
2. Python for Data Science
• Essential Python libraries (NumPy, Pandas, Matplotlib, Seaborn).
• Setting up the environment (Jupyter, Anaconda).
3. Data Collection
• Sources of data (APIs, databases, web scraping).
• Importing datasets (CSV, Excel, SQL).
4. Data Cleaning
• Handling missing and duplicate data.
• Data transformation and normalization.
5. Exploratory Data Analysis (EDA)
• Summary statistics and correlation.
• Data visualization techniques with Matplotlib and Seaborn.
6. Data Wrangling
• Working with complex datasets.
• Merging, joining, and reshaping data.
7. Feature Engineering
• Feature selection and extraction.
• Encoding categorical variables.
8. Statistical Analysis
• Descriptive and inferential statistics.
• Hypothesis testing.
9. Machine Learning Basics
• Introduction to supervised and unsupervised learning.
• Key algorithms (Linear Regression, K-Means, Decision Trees).
10. Model Evaluation
• Train-test split and cross-validation.
• Performance metrics (accuracy, RMSE, precision, recall, F1-score).
11. Time Series Analysis
• Basics of time series data.
• Forecasting techniques (ARIMA).
12. Natural Language Processing (NLP)
• Text preprocessing and sentiment analysis.
• Word embeddings and topic modeling.
13. Big Data Basics
• Introduction to Hadoop and Spark.
• Working with large datasets.
14. Data Visualization
• Advanced visualization tools (Tableau, Power BI).
• Interactive visualizations in Python (Plotly, Dash).
15. Capstone Project
• Real-world data science project.
• Insights presentation and storytelling.
Duration: 60 Days