Hello and welcome to Lab DATA2020

Introduction To Course

This course covers the major programming tools used in Data Science. This includes libraries within Python and the R programming language. Python libraries include those used in data preprocessing, managing data tables and data pipelines, dataflow programming on GPUs, visualization and scientific computing. Some R counterparts to these are covered. Major components of the R development environment are also taught including documentation and external integration.

Most of the labs are project-based application so that you can understand more how to apply it into real problems.

Submission Requirements (Depends on each lab)

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For Python Lab: submit .ipynb notebook file

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For R Lab: submit .Rmd notebook file and .pdf output using knit

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Application Lab Week 1

Objectives

• Overview of the course and assessment structure
• The role of programming in data science
• Setting up the working environment
• Introduction to key tools: Python, R, Jupyter, RStudio

The Role of Programming In DS

Discussion Questions

• Why do data scientists need programming?
• What are some real-world problems solved with data science?

Example: Uber ride datasets

https://www.kaggle.com/datasets/yashdevladdha/uber-ride-analytics-dashboard/data

• With programming, we could answer:
  • What percentage of total bookings?
  • Customer/ Driver Cancellations Reason?
  • Can we build a model to predict cancellation?