Data Science Python
Unlock the power of Python for Data Science. Learn NumPy, Pandas, Seaborn, Matplotlib, Plotly, Scikit-Learn, Tensorflow, and more. Dive into hands-on projects, gain practical experience, and master data science tools. Join us and embark on your journey to becoming a data science expert, ready to tackle real-world challenges and opportunities.
Introduction to Python for Data Science:
Overview of data science and its role in various industries.
Setting up the development environment, installing Python, Jupyter Notebook, and essential libraries.
Python Basics for Data Science:
Variables, data types, and basic operators in Python.
Control flow structures: if statements, loops (for, while), and functions.
Data Cleaning and Preprocessing
Data exploration, data cleaning, and handling missing values.
Data transformation, feature scaling, and encoding categorical variables.
Data Visualization with Python
Data visualization with Matplotlib and Seaborn.
Interactive data visualization with Plotly and advanced plotting techniques.
Data Analysis with Pandas
Introduction to Pandas, Series, and DataFrames.
Data indexing and selection, data aggregation, and merging datasets.
Introduction to Machine Learning
Overview of machine learning, supervised vs. unsupervised learning, and model evaluation.
Regression analysis, linear regression, and evaluation metrics.
Classification and Model Selection
Classification algorithms, logistic regression, decision trees, and random forests.
Model selection and hyperparameter tuning, k-fold cross-validation, and overfitting prevention.
Machine Learning with Scikit-Learn and TensorFlow
Introduction to Scikit-Learn for machine learning tasks.
Introduction to TensorFlow for deep learning and neural networks.
Advanced Topics in Data Science
Natural Language Processing (NLP) with Python.
Time series analysis, recommendation systems, and ensemble methods.
Final Projects and Deployment
Students work on comprehensive data science and machine learning projects.
Project continuation, model deployment, and sharing insights.
- Weekly coding exercises, quizzes, 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.