About Course
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3-Months Data Science Internship Data Science Internship Program
Week 1: Introduction to Data Science
- Welcome and Orientation
- Overview of Data Science and its importance
- Tools and software to be used during the internship
- Data Science Fundamentals: data types, structures, and statistical concepts
- Data collection methods
Week 2: Data Cleaning and Preprocessing
- Handling missing values
- Data transformation techniques
- Data normalization and standardization
- Introduction to Python for Data Science
- Basic Python programming concepts
- Libraries for Data Science: NumPy, Pandas
Week 3: Exploratory Data Science (EDA)
- Data visualization techniques
- Identifying patterns and insights
- Using Pandas and Matplotlib for EDA
- Introduction to SQL: Basic SQL queries and data manipulation
Week 4: Advanced Data Science
- Descriptive and inferential statistics
- Hypothesis testing
- Correlation and regression analysis
- Advanced data visualization tools: Seaborn, Plotly
- Creating interactive dashboards
Week 5: Machine Learning Basics
- Introduction to machine learning concepts
- Supervised vs. unsupervised learning
- Introduction to popular algorithms: Linear regression, decision trees, clustering
- Using Scikit-Learn for basic machine learning tasks
Week 6: Applied Machine Learning
- Data preparation for machine learning
- Model training and evaluation
- Cross-validation and hyperparameter tuning
- Real-world machine learning project
Week 7: Big Data and Cloud Computing
- Introduction to big data concepts
- Overview of Hadoop and Spark
- Using cloud platforms for Data Science: AWS, Google Cloud, Azure
- Hands-on exercises with cloud-based Data Science tools
Week 8: Final Project
3rd Month: Projects With codec technologies developers
Course Content
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