Week 3 : Data Collection and Data Processing, Exploratory Analysis

Objectives

Course Objectives:

  • Understand the fundamental concepts and principles of data science, including data collection, preprocessing, analysis, and interpretation.
  • Apply data analysis and visualization techniques to derive insights from diverse datasets.
  • Develop proficiency in using data science tools and programming languages.
  • Explore the ethical considerations associated with data-driven decision-making.

Learning Outcomes:

  • Convert a raw data source into a version appropriate for downstream analysis using Python.
  • Demonstrate awareness of bias and ethics in data science.


Lectures

3.1 - Introduction to Pandas

3.2 - Data Collection (Part 1 of 2)

3.3 - Data Collection (Part 2 of 2) & Data Processing (Part 1)


Activities

Homework

Weekly Checkin

  • Weekly Checkin for Week 2 : Due 5 pm, June 25th, Sunday.
  • Weekly Checkin for Week 3 : Due 5 pm, July 2, Sunday

Summary ( Plan versus Achievements):

  • Strikethrough text is changes in plan.
  • Green is new items added after planning.
  • Checked boxes are completed items.

Week 3 ( June 18 to June 25 June 27)

Lectures

  • Topic: Statistics and Probability - Lecture 4 (Part 2) Will continue in Week 8
  • Lecture 2.x.1 Binomial Distribution
  • Topic: Data processing and exploratory Analysis ( 2 or 3 Lectures ):
    • 3.1 - Introduction to Pandas
    • 3.2 - Data Collection (Part 1 of 2)
    • 3.3 - Data Collection (Part 2 of 2) & Data Processing (Part 1)

Homeworks

  • Homework 1 due June 25 June 26, 5 pm
    • Homework 1 Question 3.2 dropped. 

Weekly Check-in

  • Check-in for week 2 is due on June 25, 5 pm.
  • Check-in for week 3 opens.

Assigned Reading

  • Week 3’s assigned reading will be announced and available on D2L on or before Monday.