Data Science Fundamentals for Non-Programmers

By the end of this course, you will be able to:

• Interpret and apply the core data science workflow to structure analytical problems
• Prepare and clean datasets to ensure reliability and usability for analysis
• Analyze data using basic statistical and exploratory techniques to identify patterns and trends
• Communicate data-driven insights using effective visualization approaches
• Explain how machine learning models generate predictions and support decision-making

Description

As a technical professional, you are increasingly expected to make decisions informed by data. However, many practitioners lack formal training in how to structure data problems, assess data quality, and extract meaningful insights without relying on advanced programming skills. This gap can lead to inefficiencies, uncertainty in decision-making, and missed opportunities to leverage available data effectively.

This course addresses these challenges by introducing a practical, structured approach to data science for non-programmers. You will learn how to work with data across the full lifecycle—from acquisition and preparation to analysis and visualization—using accessible, user-friendly tools. The course also introduces the role of machine learning in supporting predictive analysis, without requiring technical implementation.

Through applied examples and guided exercises, you will develop the ability to interpret data, generate actionable insights, and communicate findings clearly within your professional context.

Who This Course Is For

This course is designed for:

• Engineers and engineering technologists across all disciplines
• Technical professionals working with data but without formal data science training
• Early-career to mid-level professionals seeking to strengthen analytical and decision-making skills
• Professionals responsible for data-informed analysis, reporting, or operational decisions

Course Introduction

• Demystifying Data Science: What it is and why it matters
• The role of data in decision-making and innovation

Getting Started with Python for Data Science

• Introduction to Python's simple syntax
• Overview of Python libraries tailored for non-programmers: pandas for data manipulation

Data Acquisition and Cleaning

• Understanding data sources and acquisition methods
• Cleaning your data: dealing with missing values and inconsistencies using pandas

Exploratory Data Analysis (EDA)

• Unlocking the stories hidden in your data
• Using descriptive statistics to understand your dataset
• Introduction to user-friendly data visualization with matplotlib

Introduction to Machine Learning

• Simplifying machine learning: What it is and how it can be used
• Exploring basic machine learning concepts without the jargon
• A gentle introduction to building your first model

Wrap-Up and Next Steps

• Recap of the day's journey into data science
• Resources and recommendations for further learning
• Q&A session to address any lingering curiosities or concerns