DATA SCIENCE FOR BUSINESS PROFESSIONALS
Live-Online Training: N 210, 000
Classroom Training: N 280,000
3 - 4 participants: 5% discount
5 or more participants: 10% discount
(Available also for Customised Training by Duration, Venue & Fee)
Course Description:
This 3-day intensive course is designed to introduce business professionals to the fundamentals of data science. Participants will learn how to collect, analyze, and visualize data to make informed business decisions and improve organizational performance.
Course Objectives:
- Understand the key concepts and processes in data science.
- Collect, organize, and clean business data.
- Perform exploratory data analysis and statistical analysis.
- Build and interpret predictive models.
- Create data visualizations and dashboards.
- Apply data science techniques to solve business problems.
Course Content
Day One
Introduction to Data Science and Business Applications
· Welcome and Course Overview
o Introduction to the course objectives and schedule
o Importance of data science for business professionals
· Fundamentals of Data Science
o Definition and key concepts of data science
o The data science process: CRISP-DM (Cross-Industry Standard Process for Data Mining)
· Data Science in Business
o How data science is transforming various industries
o Case studies of successful data science applications in business
· Data Collection and Management
o Data sources: internal and external
o Data collection methods and best practices
o Data management and data governance
· Case Study: Business Data Analysis
o Hands-on exercise analyzing a business dataset
o Identifying key insights and business implications
Day Two
Data Analysis and Visualization
· Exploratory Data Analysis (EDA)
o Techniques for exploring and understanding data
o Descriptive statistics and data visualization
· Tools for Data Analysis
o Introduction to popular data analysis tools (Excel, Python, R)
o Basic functions and operations in these tools
· Data Visualization Techniques
o Principles of effective data visualization
o Creating visualizations using tools like Tableau, Power BI, and Python (Matplotlib, Seaborn)
· Case Study: Data Visualization for Business Insights
o Hands-on exercise creating visualizations for a business dataset
o Communicating insights through visual storytelling
Day Three
Predictive Analytics and Machine Learning
· Introduction to Predictive Analytics
o Concepts and techniques of predictive analytics
o Common algorithms used in predictive modeling
· Machine Learning Basics
o Overview of machine learning and its applications in business
o Supervised vs. unsupervised learning
· Implementing Predictive Models
o Hands-on session building predictive models using Python (scikit-learn) or R (caret)
o Model evaluation and validation techniques
· Case Study: Predictive Analytics for Business Decision-Making
o Hands-on exercise applying predictive analytics to a business problem
o Interpreting model results and making data-driven decisions
· Wrap-Up and Review
o Review of key concepts covered in the course
Q&A session and feedback.