NTUC LearningHub

Course Objectives

 

  • Learn how to get data from external sources into the Python environment and manipulate and analyse data.
  • Use statistical algorithms and methods to analyse data as part of the data analytics process
  • Learn how python libraries can be leveraged to deal with data inconsistencies, issues with data and making them fit for data analytics
  • Use appropriate statistical methods and visualizations for descriptive analytics
  • Form hypothesis and do predictive analytics
  • Learn the basics of how supervised machine learning models work and how evaluation and optimization can be carried out
  • Understand basic metric and KPIs (Key Performance Indicators) of different business cases
  • Plot advanced interactive visualizations for data analysis to gain insights from data.

 

Pre-requisites

 

  • Knowledge, Skills & Experience
    • This course requires a basic programming knowledge of Python.
    • Participants who do not have basic Python knowledge are encouraged to take up Fundamentals of Python prior to this course.
  • Hardware & Software

This course will be conducted as a Virtual Live Class (VLC) via Zoom platform. Participants must own a zoom account and have a laptop or a desktop with “Zoom Client for Meetings” installed. This can be downloaded from https://zoom.us/download

 

System Requirement
Must Have:

Please ensure that your computer or laptop meets the following requirements.

  • Operating system: Windows 10 or MacOS (64 bit or above)
  • Processor/CPU: 1.8 GHz, 2-core Intel Core i3 or higher
  • Minimum 20 GB hard disk space.
  • Minimum 8 Gb RAM
  • Webcam (The camera must be turned on for the duration of the class)
  • Microphone
  • Internet Connection: Wired or Wireless broadband
  • Latest version of Zoom software to be installed on computer or laptop prior to the class.
 

 

Course Outline

 

MODULE 1: Understanding Data
Introduction to python packages for data manipulation
Importing and exporting data
Importing datasets and understanding data
Basics of analyzing the data

MODULE 2: Data Wrangling
Dealing with data issues and preparation in Python
Data Formatting and conversions in Python
Data Wrangling
Working with Pandas

MODULE 3: Exploratory Data Analysis
Performing descriptive statistics with Python
Correlations, Scatter-plots and charts with matplotlib in Python
Understanding data analysis with respect to various business scenarios

MODULE 4: Model Development for Analysis
Hypothesis Testing
Linear Regression and Multiple Regression Models
Model Evaluation Methods
Model selection

MODULE 5: Data Visualization
Understanding basic metrics and KPIs
Visualizations using Python (Seaborn, Folium)

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