Course Content
Basic Concepts
1. Introduction to Data Science 2. Overview of Data Science Lifecycle 3. Data Collection and Data Types 4. Data Cleaning and Preprocessing 5. Exploratory Data Analysis (EDA) 6. Introduction to Statistics for Data Science 7. Data Visualization Techniques 8. Introduction to Machine Learning 9. Linear Algebra and Calculus Basics for Data Science 10. Introduction to Python or R Programming for Data Science 11. Hands-on Projects and Exercises
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Lesson Plan – Basic Concepts
Advanced Concepts
1. Advanced Statistical Analysis 2. Feature Engineering and Selection 3. Dimensionality Reduction Techniques 4. Advanced Machine Learning Algorithms: - Ensemble Learning - Support Vector Machines (SVM) - Neural Networks and Deep Learning - Time Series Analysis - Natural Language Processing (NLP) - Recommender Systems 5. Model Evaluation and Validation 6. Hyperparameter Tuning and Optimization 7. Big Data Technologies (e.g., Apache Spark) 8. Deployment of Machine Learning Models 9. Advanced Data Visualization Libraries and Techniques 10. Advanced Python or R Programming for Data Science 11. Ethical Considerations and Bias in Data Science 12. Hands-on Projects and Case Studies
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Advanced Concepts
Data Science Training
About Lesson

1. Advanced Statistical Analysis
2. Feature Engineering and Selection
3. Dimensionality Reduction Techniques
4. Advanced Machine Learning Algorithms:
– Ensemble Learning
– Support Vector Machines (SVM)
– Neural Networks and Deep Learning
– Time Series Analysis
– Natural Language Processing (NLP)
– Recommender Systems
5. Model Evaluation and Validation
6. Hyperparameter Tuning and Optimization
7. Big Data Technologies (e.g., Apache Spark)
8. Deployment of Machine Learning Models
9. Advanced Data Visualization Libraries and Techniques
10. Advanced Python or R Programming for Data Science
11. Ethical Considerations and Bias in Data Science
12. Hands-on Projects and Case Studies

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