Data Analytics Essentials for Business Professionals
Programme Overview
In this compact course, you will learn the essential skills to aggregate, visualize, and analyze large amounts of data. This course is divided into three sections. In the first section, you will learn the basics of structuring your data and how to visualize the data quickly and effectively. In the second section, you will learn descriptive and predictive models in analyzing data. In the third and last section, you will learn how to create interactive forms, reports, and data visualization outputs.
The course module summary is as follows:
- Part 1: Summarizing and Visualizing
Basics, Data Structures, Navigating, Summarizing
Visualization - Part 2: Analyzing Data
Descriptive Analytics
Predictive Analytics - Part 3: Creating Interactive Reports
Model Building, Merging, Appending
Interactive Visualization - Part 4: Applications in Data Analytics
Experiential Case Studies
Use Case Scenarios
This course is designed to enhance your data analytical understanding. This course is suitable for anyone, including those with no prior analytics experience. The contents are tailored for business professionals, managing officers, financial and operations experts, and office managers.
The coursework covers how analysts describe, predict, and inform business decisions in the specific areas of management, marketing, human resources, finance, and operations. The goal is for learners to develop basic data literacy and an analytic mindset to make strategic decisions based on data.
The learning outcomes of this course include but are not limited to
- Identify various types of data,
- Create effective data visualizations
- Understand and apply descriptive methods to aggregate and summarize data
- Apply predictive methods to forecast data
- Create interactive reports and visualizations
This course will benefit business professionals by shaping their data collection strategies, improving their management and analysis of data, and enhancing their decision-making processes through the application of faster and more efficient methods in data analytics.