Course outline for Data Science and Predictive Analysis

This course has been designed to cater to the needs of learners who are comfortable with python and picking up a specialization of python as an additional concentrated skill. This course will also delve into the various usages of python which in real business case studies. This course aims to alleviate the unnecessary jargon and cover the parts that are indeed needed.

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.

This course is designed for those who are experienced in programming with python. For absolute beginners, a Python beginners course is recommended prior to attempting this course.

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 3.9 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. Machine learning and coding in python using Scikit Learn
16. Machine learning and coding in python usingXGBoost
17. Using machine learning for predictions and analytics.
18. Data Acquisition and scraping.