Data Science with Python Certification

Welcome to the repository for the Data Science with Python Certification Course! This serves as a backup and summary of the training received and ongoing work. It outlines the objectives and curriculum covered in the course, providing a concise overview of the learning journey.

Course Objective

The primary objective of this course is to equip participants with the foundational and advanced skills required to perform data analysis, machine learning, and data visualization using Python. It combines theoretical concepts with hands-on implementation, enabling learners to solve real-world problems.

Course Curriculum

The curriculum is designed by industry experts and covers a comprehensive range of topics. Below is a brief overview of the modules:

1. Introduction to Data Science and ML using Python

• Topics: Python overview, data science lifecycle, tools, and basics of Python programming.
• Key Skills: Python programming, data science foundations.

2. Data Handling, Sequences, and File Operations

• Topics: Data wrangling, file operations, and working with Python data structures (lists, tuples, dictionaries, sets).
• Key Skills: Input/output operations, data type manipulations.

3. Deep Dive: Functions, OOPs, Modules, Errors, and Exceptions

• Topics: Functions, object-oriented concepts, libraries, and error handling in Python.
• Key Skills: Advanced Python programming, exception handling, modular design.

4. Introduction to NumPy, Pandas, and Matplotlib

• Topics: Arrays, data structures, data analysis, and basic visualizations.
• Key Skills: Data manipulation with NumPy and Pandas, plotting with Matplotlib.

5. Data Manipulation

• Topics: Merging, concatenating, and analyzing datasets.
• Key Skills: Data exploration and manipulation using Pandas.

6. Introduction to Machine Learning with Python

• Topics: Machine learning categories, linear regression, and gradient descent.
• Key Skills: ML basics, linear regression implementation.

7. Supervised Learning - I

• Topics: Classification, decision trees, random forests.
• Key Skills: Implementing and evaluating supervised learning models.

8. Dimensionality Reduction

• Topics: PCA, LDA, scaling dimensional models.
• Key Skills: Dimensionality reduction techniques.

9. Supervised Learning - II

• Topics: Naïve Bayes, SVM, hyperparameter tuning.
• Key Skills: Advanced supervised learning techniques.

10. Unsupervised Learning

• Topics: Clustering (K-means, C-means, hierarchical).
• Key Skills: Implementing clustering algorithms.

11. Association Rules Mining and Recommendation Systems

• Topics: Apriori algorithm, collaborative and content-based filtering.
• Key Skills: Building recommender systems.

12. Reinforcement Learning (Self-Paced)

• Topics: Q-Learning, Markov Decision Processes.
• Key Skills: Reinforcement learning implementation.

13. Time Series Analysis (Self-Paced)

• Topics: ARIMA, stationarity, ACF, PACF.
• Key Skills: Time series forecasting.

14. Model Selection and Boosting

• Topics: Cross-validation, boosting algorithms (AdaBoost).
• Key Skills: Model evaluation and optimization.

15. Statistical Foundations (Self-Paced)

• Topics: EDA, heatmaps, graphical and non-graphical analysis.
• Key Skills: Exploratory data analysis.

16. Database Integration with Python (Self-Paced)

• Topics: SQL and MongoDB operations with Python.
• Key Skills: Database management and integration.

17. Data Connection and Visualization in Tableau (Self-Paced)

• Topics: Connecting data, creating charts, data blending.
• Key Skills: Visualizing and analyzing data in Tableau.

18. Advanced Visualizations (Self-Paced)

• Topics: Dashboards, story points, forecasting.
• Key Skills: Building advanced visualizations in Tableau.

19. In-Class Project (Self-Paced)

• Project: Predicting plant species using machine learning.
• Key Skills: Data pre-processing, feature engineering, ML implementation.

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Repository Usage

This repository contains:

1. Notes and summaries for each module.
2. Hands-on implementation scripts and projects.
3. References to additional resources for further learning.

Feel free to explore and contribute as needed!

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Acknowledgments

Special thanks to the course designers and instructors for their expert guidance and support throughout the learning journey.