Course Methodology

This is an interactive course, preparing participants both for the EXIN examination and real life applications with hands-on exercises.  Course training techniques include visualization, group discussions, exercises, and presentations.

Course Objectives

By the end of the course, participants will be able to:

• Refine unstructured data from sources, such as Excel, Python, or PowerBI, into structured insights that will solve business challenges
• Understand concepts relating to generating a research question, such as collecting, cleaning, and organizing data
• Comprehend statistical methods, data mining techniques, and algorithms to support the interpretation of data
• Generate insights for the organization by illustrating findings in plots and graphs to present a business case with solidly backed insights
• Describe and illustrate Data Visualizing design techniques and utilize visualization tools

Target Audience

This course is designed for anyone at the start of a data analytics role, such as data/information analysts, business intelligence analysts, data administrators, business information managers, data/analytics managers and data scientists.

This course is also suitable for those who are interested in the business benefits of data analysis and the techniques involved in it. These roles could include, but are not limited to, digital marketing/media specialists, market research analysts, business analysts, finance professionals and HR professionals.

Target Competencies

• Data Insights
• Business Intelligence
• Cleaning Data
• Data Analysis
• Data Visualization

Turning data into insights

• Introduction to the process of data analytics
• Concepts related to data analytics
• Steps in the process of data analytics
• Risks in data analytics
• Business Intelligence and business decisions

Data collecting, organizing, and managing

• Data collection channels
• Sourcing public data
• Contemporary data laws and compliance
• Raw data, structured data, unstructured data and big data
• Database types
• Distributed file systems
• Cloud solutions and their advantages
• Dependent and independent variables
• Continuous and discrete variables

Data cleaning

• Data problems
• Data scrubbing methods:
• Variable selection
• Merging variables
• One-hot encoding
• Binning
• Data scrubbing techniques
• Data retention

Data analyzing

• Data analytics and statistics
• Descriptive statistics and inferential statistics
• Data mining
• Machine learning
• Natural Language Processing (NLP)
• Methods and techniques of Natural Language Processing (NLP)
• Data and algorithms
• Regression analysis
• Classification models
• Clustering analysis
• Association analysis and Sequence mining

Data visualizing

• Explanatory and exploratory graphics
• Types of charts
• Types of plots
• Usage of heatmaps
• Aesthetic design
• Visualization tools

Preparing for the EXIN Data Analytics Foundations exam

• Bloom levels
• Basic concepts
• Literature handling
• How to use the Sample Exam
• How to use the Rationale
• Exam tips