Tools and Techniques In DataScience

Final Term Syllabus

1. Chapter 2 of datacamp course Exploratory Data Analysis in Python

2. MS Word related video links  Setting up Heading formatting and numbering as well as tables of contents for Scientific reports, documents and Theses , Inserting Figures and Figure Legends in Microsoft Word,  Adding sections BEFORE your table of contents without numbering (optional), Producing a Figure for publication containing microscopy images in MS Word (optional)  ampling

3. Prompt Engineering, Vibe coding,

   1. Types of prompts: ICC, zero shot, n-shot, CoT

   2. LLMs are not deterministic models, Temperature, Tokens, Top p, Top k, context window, tool calling- Internet Search, Hallucination

   3. Importance of Domain Knowlede, and Meta Skills in use of LLM models and vibe coding

   4. System and User Prompt

   5. Definition of Vibe coding and practice with Google AI studio 

   6. 
      

4. Sampling

   1. Types of Sampling Methods (4.1) see this video https://www.youtube.com/watch?v=pTuj57uXWlk 

   2. Read the html file related to sampling we discussed in the class  [download this file and double click it to open in your web browser e.g. chrome, firefox, or edge etc]

   3. Census, Nonresponse, and Undercoverage (4.2)  https://www.youtube.com/watch?v=EZrP_av3cmA

5.  The Normal Distribution and the 68-95-99.7 Rule (5.2) https://www.youtube.com/watch?v=mtbJbDwqWLE

6. Z-Scores, Standardization, and the Standard Normal Distribution (5.3) https://www.youtube.com/watch?v=2tuBREK_mgE

7. Project: Analyzing Crime in Los Angeles - see project description from here and press continue project hyperlink. On the left window you will see various tabs related to project guides, resouces, and solution. On the Right (main window) you will see project notebook, where you will run the code

8. Note:
   - As discussed in the class this is part of yout final term paper. So complete it yourself and it will be part of your sessionals assesment as well.
   - Students having less than 70% attendance are not elligible to sit in the final term exams and are also not elligible for sessional marks.

9. https://media.datacamp.com/cms/python-with-ai.pdf (TBD)

10. Data Science Work Flow

    1. Defining the problem

    2. Data Prepation and Injection - Extract Transforkm Load (ETL)

    3. Data Preprocessing

       1. One hot encoding

    4. Data Modeling, Analysis and Machine Learning 

       1. Introduction to Supervised Machine Learning: Classification - Regression, Model evaluation metric (accuracy), DV, IV

    5. Delievering the results

       1. Report writing

11. https://www.datacamp.com/blog/sports-analytics-euro-2024?src=data-ai-world-cup (TBD)

12. https://www.datacamp.com/code-along/create-claude-skills-for-data-tasks?src=data-ai-world-cup (TBD)

    1. https://media.datacamp.com/cms/resources---create-claude-skills-for-data-tasks-v2.pdf

    2. https://media.datacamp.com/cms/claude-skills-for-data-tasks.pdf

Experimental Design

• Causation vs Association, and an Introduction to Experiments (3.1) 

• Placebo Effect, Control Groups, and the Double Blind Experiment (3.2)

• Types of Experimental Designs (3.3)

• Writing Efficient Python Code

• Exploratory Data Analysis in Python 

• Introduction to Statistics (in addition to related youtube videos one can learn theory from here)

• Introduction to Statistics in Python (in addition to related youtube channels one can learn from here)

• Sampling in Python (in addition to related youtube videos one can learn theory from here)

• Hypothesis Testing in Python (It will complement the topic discussed in EDA related course)

Note: It is must to attempt related Practice in Python, Theory and also related Projects available at datacamp!

Activity: Read the following link and explore various projects: Link to KD-Nuggets' Ten project list for Data Scientist 

Datacamp tracks 

• Excel Fundamentals

• Introduction to Data Science in Python

• Data Analyst in Python

• Data Analyst in Power BI