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.
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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)
Categories
More Information
- NTUC LearningHub
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