TRAINING.

Overview

After participating in this course, you will be able to:

• Understand the Core Principles of Data Science: Gain a solid foundation in what data science entails, including its significance and potential applications across various industries.
• Be Familiar with Key Data Science Processes: Acquire knowledge about the data science workflow, from data collection and cleaning to exploratory analysis.
• Learn Basic Data Manipulation Techniques: Discover how to preprocess and clean data using user-friendly tools, preparing it for analysis without the need for programming.
• Explore Data Visually: Understand the basics of data visualization and how it can be used to extract insights and communicate findings effectively.
• Grasp the Basics of Machine Learning: Get introduced to the concept of machine learning and its role in data science, learning how algorithms can be used to make predictions and uncover patterns in data.

Description
Data Science utilizes scientific methods, processes, algorithms, and systems to extract insights and knowledge from data. It merges areas like statistics, mathematics, and computer science to analyze, interpret, and visualize data, aiming to aid decision-making and problem-solving.

The importance of Data Science lies in its ability to turn complex datasets into actionable insights, leading to the ability to make more informed decisions. By leveraging data, organizations can predict trends, improve efficiency, personalize customer experiences, and drive innovation, making Data Science a critical component in today's data-driven world.

Embark on a fascinating journey into the world of data science, tailored specifically for non-programmers. This one-day course is designed to demystify the complex field of data science, making it accessible and engaging. Discover how to leverage data for insightful decisions without the need for a programming background.

Dive into the essentials of data handling, analysis, visualization, and the basics of machine learning, all through the lens of user-friendly Python libraries.

Who Should Attend

This course is suitable for professionals, students, and individuals interested in Data Science and its applications. It caters to beginners with no prior experience in the field, as well as those with some knowledge looking to expand their understanding and practical skills.

Prerequisites
Curiosity and enthusiasm! No prior programming or data science knowledge is required.

Please note: You can check other time zones here.

Syllabus

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

Instructor

Yasser Ebrahim, Ph.D.

Dr. Yasser Ebrahim obtained his Ph.D. in Computer Science from the University of Guelph in 2006. He has a Master’s in Computer Science degree from the University of Waterloo (2003) besides a Master’s degree from DePaul University (1995) in Computer Information Systems.

For the past 22 years, Dr. Ebrahim has taught at Wilfrid Laurier, McMaster University, Ryerson University, and the University of Toronto in Mississauga. He has taught various computer science courses, including programming in C/C++, object-oriented programming in Java, Python, data structures, software architecture, operating systems, database systems, human-computer interaction, software engineering, and computer graphics.