Informatics Education (HK) Limited

SHORT COURSE IN DATA SCIENCE: Advanced

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Course Information

Registration period
Year-round Recruitment
Price
-
Course Level
Study Mode
Duration
120 Hour(s)
Language
Cantonese, English
Location
-
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Course Overview

Overview and Objectives
This Short Course in Data Science: Advanced focuses on Analytics practitioners who have more than 3 years experience operating in a data analytics or analysis team looking to increase their knowledge in data science.

This course provides knowledge on all the key techniques such as ETL, Linear Algebra, Matrices, R programming, Visualisation with R’s packages, Automated Knowledge Acquisition, Storytelling and Dashboard design and many more.

Overview
Data science is the study of extracting meaningful insights from data. It is a forward-looking approach with the focus on analysing the past or current data and predicting the future outcomes with the aim of making informed decisions.

Regardless of industry or size, organisations that wish to remain competitive in the age of big data need to efficiently develop and implement data science capabilities.

The Short Courses in Data Science have four levels:

Short Course in Data Science: An Introduction
Short Course in Data Science: Intermediate
Short Course in Data Science: Advanced
Short Course in Data Science: Expert

Entry Requirements

Short Couse in Data Science: Advance

Analytics practitioners who have more than 3 years of experience operating in a data analytics or analysis team looking to increase their knowledge in data science. They must have attended and passed the Intermediate Data Science course or other associated data science course.

What You’ll Learn

Short Course in Data Science: Advanced

  • Be able to extract, transform and load (ETL) data
  • Be able to explain ETL testing process and types of ELT testing
  • Be able to create the ETL test case in a given scenario
  • Setting up the data analytics team structures, roles and responsibilities
  • Be able to represent unstructured text documents with appropriate format
  • Be able classify text to classes or categories (Naïve Bayes, k Nearest Neighbour (kNN), Logistic Regression)
  • Be able to identify the clustering structure of mass text

Topics
The following topics will be covered:

  • Statistics
  • Coding
  • Modelling
  • Data Visualisation


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