University Certificate Course: Data Science Fundamentals
This course provides a comprehensive introduction to the world of Data Science, the discipline that empowers us to extract knowledge and insights from data. Students will learn the entire data science lifecycle, from data collection and cleaning to analysis, visualization, and interpretation.
Lead Instructor
Konstantin Titov
Konstantin Titov is a distinguished instructor and practitioner in artificial intelligence and cybersecurity with more than 20 years of experience. He has worked across multiple industries, developing practical security solutions and helping organizations integrate modern AI technologies safely and effectively.
He is also the author of several applied technology books. His titles include Introduction to Artificial Intelligence: Understanding the Basics (ISBN 9798874154950), Smart Home Security: Building a Safe Home with Smart Technology (ISBN 9798346933342), Machine Learning Fundamentals in Action (ISBN 9798343849080), and The AI-First Firm Blueprint (ISBN 9798269643465).
As an educator, Konstantin focuses on simplifying complex technical concepts and making them accessible to learners at all levels.
Learning Outcomes
Upon successful completion of this course, students will be able to:
- Define the data science lifecycle and articulate the role of a data scientist.
- Apply fundamental statistical concepts to describe and analyze datasets.
- Utilize Python and its core libraries (Pandas, NumPy) to clean, manipulate, and prepare data.
- Perform exploratory data analysis (EDA) to uncover patterns and formulate hypotheses.
- Create compelling and informative data visualizations using Matplotlib and Seaborn.
- Explain the core concepts of machine learning and build basic predictive models.
- Effectively communicate findings derived from data analysis to diverse audiences.
The Course Focus
Master the foundational concepts of statistics and probability that form the bedrock of all data analysis and machine learning.
Develop practical programming skills in Python, focusing on powerful libraries like Pandas and NumPy to efficiently manipulate and analyze data.
Learn to tell compelling stories with data by creating clear, insightful, and impactful visualizations using Matplotlib and Seaborn.
Gain a solid introduction to machine learning theory and practice, from understanding different model types to building your first predictive algorithms.
Who Should Enroll?
This introductory course is designed for anyone curious about the power of data. It is ideal for:
- Aspiring data scientists, data analysts, and business intelligence analysts.
- Professionals in marketing, finance, and healthcare who want to become data-literate.
- University students and recent graduates looking to add a high-demand skill set.
- Anyone with a passion for problem-solving and a desire to understand the world through data.
No prior experience in programming or statistics is required.
Course Modules
MODULE 1
Introduction to Data Science
This module sets the stage by defining what data science is (and isn't). Students will explore the data science lifecycle, understand the different roles within a data team (analyst, scientist, engineer), and discuss the critical importance of data ethics and privacy.
MODULE 2
Statistical Foundations for Data Science
Dive into the essential statistical concepts that underpin data science. This module covers descriptive statistics (mean, median, standard deviation), probability basics, distributions, and an introduction to inferential statistics, including hypothesis testing and confidence intervals.
MODULE 3
Python for Data Science: NumPy & Pandas
This module provides a practical, hands-on introduction to Python. Students will learn the fundamentals of Python programming before mastering NumPy for numerical operations and Pandas for data manipulation, including loading, filtering, grouping, and merging datasets.
MODULE 4
Data Wrangling & Exploratory Data Analysis
Real-world data is messy. This module focuses on the critical skills of data cleaning (wrangling), including how to handle missing values, correct data types, and remove inconsistencies. Students will then learn the process of EDA to systematically investigate data and uncover initial insights.
MODULE 5
Data Visualization with Matplotlib & Seaborn
A picture is worth a thousand data points. This module covers the principles of effective data visualization. Students will get hands-on experience using Python libraries Matplotlib and Seaborn to create a wide range of static plots, including bar charts, histograms, scatter plots, and box plots.
MODULE 6
Introduction to Machine Learning
This module demystifies machine learning. Students will learn the fundamental concepts, including the difference between supervised (regression, classification) and unsupervised (clustering) learning. Key ideas like feature engineering and the problem of overfitting will be introduced.
MODULE 7
Building & Evaluating Predictive Models
Put theory into practice by building your first machine learning models using the Scikit-learn library. This module covers linear regression for predicting continuous values and logistic regression for classification tasks. Students will also learn how to evaluate model performance.
MODULE 8
Communicating Insights & Next Steps
The final module focuses on the crucial "last mile" of data science: communicating results. Learn the art of storytelling with data, how to structure a data analysis project, and how to present findings effectively. This module also provides an overview of career paths.
Enrollment & Certification
This course is offered for US$450 to all students who wish to gain fundamental knowledge in Data Science.
For students seeking formal recognition of their achievement, we offer an optional University Certificate of Completion.