Python and Data Science

Python and Data Science

[intermediate - advanced]

This course has been designed to cater to the needs of learners who are comfortable with programming in general. The curriculum will also delve into the various usages of python with real business case studies. This course shall also enable the user to have the necessary foundation to take their training to a more advanced level should they wish to do so. It is designed to help you understand machine learning and its underlying concepts from a technical viewpoint.

The course will have hands-on implementation of all techniques covered. As such, a lab is compulsory.

Learning outcome:

1. Overview of python basics.
2. IDEs of python.
3. Understand the basic syntax structures.
4. Get updated on recent syntax changes in python 3.8 and 3.10 respectively.
5. Advanced Data handling in python.
6. Working with external libraries in python.
7. Working with files and encoding.
8. Understand different forms of data acquisition.
9. Data Extraction and conversion.
10. Learn statistical analytics of data.
11. Use Python to get the basics of Data Analytics.
12. Use simple scripting to present data.
13. Have the ability to visualize data and manipulate them using simple python.
14. Understand how data science delves into Machine Learning.
15. Explore business benefits of machine learning
16. Know the difference between machine learning and artificial intelligence and deep learning
17. Machine Learning
18. Supervised Learning - Regression
19. Supervised Learning - Classification
20. Unsupervised Learning - Clustering
21. Dimensionality Reduction
22. Recommendation Engine
23. Machine learning and coding in python using Scikit Learn
24. Machine learning and coding in python usingXGBoost
25. Using machine learning for predictions and analytics.
26. Data Acquisition and scraping.

Prerequisite

• Computer programming
• Access to Google Colab OR
• If the user wants to use python locally, they will need administrative access to the OS as well as installation of Anaconda.
• High Speed internet connection (min. 1mbps)
• Basic understanding of how the filing system works

Duration

5 days

Outline

This is a high intensity course.

1. Basics of Python
   1. Syntax structure
   2. Operations
   3. Python Variables
   4. Loops in Python
   5. Python collection Data Types
   6. OOPS concepts
2. Intermediate Python
   1. Exception Handling
   2. Regular Expression
   3. Python Numpy Arrays
   4. Matrix and its operation
   5. Functions in Python
   6. User Defined functions in Python
   7. Scope in Python
   8. Introduction to Methods
3. Advanced python
   1. Recursion
   2. Memoization
   3. Advanced LIbraries
4. Python for Data Science prerequisites
   1. Packages in Python and PIP
   2. Pandas and Data frames
   3. Import and Export data from CSV and Excel
   4. Web Scraping
   5. Other sources of data
5. Fundamentals of Machine Learning
   1. What is Machine Learning?
   2. Process of Machine Learning
   3. Life Cycle of Machine Learning
   4. Application working in Machine Learning
   5. Types Of Machine Learning
   6. Setting up the development environment lab
6. Data Preprocessing
   1. Importing Libraries
   2. Importing Dataset
   3. Taking care of Missing Data
   4. Encoding Data: Categorical Data
   5. Splitting the dataset into the Training set and Test set
   6. Feature Scaling
7. Supervised Learning Algorithm
   1. What is Supervised Learning?
   2. Types of Supervised Learning
8. Regression
   1. Simple Linear Regression
   2. Multiple Regression
   3. Polynomial Regression
9. Decision Tree
   1. Implementation of Decision Tree
10. Random Forest Regression
    1. Implementation of Random Forest Regression
11. Classification
12. Implementation of Logistic Regression
    1. Implementation of Naive Bayes
    2. Implementation of Support Vector Machine (SVM)
13. Clustering
    1. K-means Clustering
    2. K-means Selecting the Number of Clusters
    3. Implementation of k-means clustering
    4. Implementation of Hierarchical Clustering
    5. Implementation of Apriori
14. Collaborative Filtering
    1. User based
    2. Item based
    3. Implementation
15. Reinforcement Learning
    1. Implementation of Upper Confidence Bound (UCB)
    2. Implementation of Thompson Sampling
16. Deep Learning and Artificial Neural Network
    1. Implementation of Artificial Neural Networks (ANN)
    2. Implementation of Convolutional Neural Network (CNN)
    3. Implementation of XGBoost
17. Dimensionality Reduction
    1. Implementation of Principal Component Analysis
    2. Implementation of Linear Discriminant Analysis
    3. Implementation of Kernel PCA
18. Communication and Perceiving
    1. Implementation of Natural Language Processing in Python
19. Hands on Project
    1. Evaluation