Data Science & ML

Data Science & ML

Unlock the potential of data analysis and predictive modeling. Learn to use NumPy and Pandas for data manipulation, Seaborn, Matplotlib, and Plotly for visualization, Scikit-Learn for machine learning, TensorFlow for deep learning, and more in Python for Data Science and Machine Learning. Acquire the skills to excel in this dynamic field and unlock countless opportunities.

Introduction to Python for Data Science
Week 1: Overview of data science and its role in various industries
Week 2: Setting up the development environment, installing Python, Jupyter notebook, and essential libraries

Python Basics for Data Science
Week 3: Variables, data types, and basic operators in Python
Week 4: Control flow structures: if statements, loops (for, while), and functions.

Data Cleaning and Preprocessing
Week 5: Data exploration, data cleaning, and handling missing values
Week 6: Data transformation, feature scaling, and encoding categorical variables

Data Visualization with Python
Week 7: Data visualization with Matplotlib and Seaborn.
Week 8: Interactive data visualization with Plotly and advanced plotting techniques.

Data Analysis with Pandas
Week 9: Introduction to Pandas, Series, and DataFrames
Week 10: Data indexing and selection, data aggregation, and merging datasets

Introduction to Machine Learning
Week 11: Overview of machine learning, supervised vs. unsupervised learning, and model evaluation.
Week 12: Regression analysis, linear regression, and evaluation metrics.

Classification and Model Selection
Week 13: Classification algorithms, logistic regression, decision trees, and random forests.
Week 14: Model selection and hyperparameter tuning, k-fold cross-validation, and overfitting prevention.

Machine Learning with Scikit-Learn and TensorFlow
Week 15: Introduction to Scikit-Learn for machine learning tasks.
Week 16: Introduction to TensorFlow for deep learning and neural networks.

Advanced Topics in Data Science
Week 17: Natural Language Processing (NLP) with Python.
Week 18: Time series analysis, recommendation systems, and ensemble methods.

Final Projects and Deployment
Week 19: Students work on comprehensive data science and machine learning projects.
Week 20: Project continuation, model deployment, and sharing insights.

  • Weekly coding exercises and assignments.
  • Mid-term project to apply data science and machine learning concepts learned in the first half of the course.
  • Final project assessment that demonstrates proficiency in data science and machine learning.
  • Peer code reviews for collaborative learning.
Note: Encourage students to work on personal data science and machine learning projects and contribute to open-source projects to gain practical experience. Stay up-to-date with the latest trends and developments in data science and machine learning to ensure the syllabus remains relevant.