The course is divided into three main sections: Foundations of Data Science, Data Analysis and Machine Learning, and Advanced Topics in Data Science. Each week includes a mix of theoretical concepts, practical applications, and hands-on projects.

Week 1-2: Introduction to Data Science
– Overview of Data Science
– Importance of Data in Decision Making
– Introduction to Python for Data Science
– Basics of Jupyter Notebooks and Data Manipulation with Pandas

Week 3-4: Data Wrangling and Exploration
– Data Cleaning and Preprocessing
– Exploratory Data Analysis (EDA)
– Data Visualization with Matplotlib and Seaborn
– Statistical Analysis with NumPy and SciPy

Week 5-6: Machine Learning Fundamentals
– Introduction to Machine Learning
– Supervised Learning: Regression and Classification
– Unsupervised Learning: Clustering and Dimensionality Reduction
– Model Evaluation and Validation

Week 7-8: Advanced Machine Learning
– Ensemble Learning (Random Forests, Gradient Boosting)
– Neural Networks and Deep Learning Basics
– Feature Engineering
– Hyperparameter Tuning

Week 9-10: Big Data and Tools for Data Science
– Introduction to Big Data
– Apache Hadoop and Spark
– SQL and NoSQL databases
– Introduction to Data Science Libraries (Scikit-learn, TensorFlow, PyTorch)

Week 11-12: Capstone Project and Special Topics
– Capstone Project: Real-world application of Data Science skills
– Ethical Considerations in Data Science
– Emerging Trends in Data Science (e.g., AI ethics, Explainable AI)
– Guest Lectures and Industry Applications

Assessment:
– Weekly Assignments and Quizzes
– Mid-term Project: Data Analysis and Visualization
– Final Project: Machine Learning Application and Presentation

Resources:
– “Python for Data Analysis” by Wes McKinney
– “Hands-On Machine Learning with Scikit-Learn and TensorFlow” by Aurélien Géron
– Online tutorials and documentation for relevant tools and libraries

Note: Adjustments can be made based on the background and preferences of the students and any specific tools or technologies that are more relevant at the time of the course. Additionally, encourage students to work on real-world projects and stay updated with the latest developments in the field.

Get A Free Counselling
Enter Your Information below and we will get back to you with an estimate within few hours
Get A Free Counselling
Enter Your Information below and we will get back to you with an estimate within few hours
Get A Free Counselling
Enter Your Information below and we will get back to you with an estimate within few hours
Get A Free Counselling
Enter Your Information below and we will get back to you with an estimate within few hours
Get A Free Counselling
Enter Your Information below and we will get back to you with an estimate within few hours
Get A Free Counselling
Enter Your Information below and we will get back to you with an estimate within few hours
Get A Free Counselling
Enter Your Information below and we will get back to you with an estimate within few hours