Data Science and Analytics Certificate Program

Course Description

Duration: 100 hours

The Data Science and Analytics Certificate Program is a comprehensive, 100-hour program designed to provide a solid foundation in the principles, tools, techniques, and technologies related to the burgeoning field of data science. The course offers an in-depth exploration of statistical concepts, data manipulation, data visualization, machine learning, big data analytics, and data-driven decision-making. Hands-on programming training is provided, with a primary focus on Python and its related data science libraries. The program concludes with a capstone project that offers an opportunity to apply the knowledge and skills learned throughout the course to solve a real-world data science problem. This program is designed to cater to beginners as well as professionals looking to enhance their data science skills and knowledge.

Interested? Learn more.

Download Flyer

Apply Now

Student Learning Outcomes

Upon successful completion of this program, students will be able to:

1. Understand the principles, tools, techniques, and technologies related to data science and their applications in various domains.

2. Demonstrate proficiency in fundamental statistical concepts and their application in data analysis.

3. Apply data cleaning and wrangling techniques to prepare raw data for analysis.

4. Utilize various data visualization tools to interpret and present complex data effectively.

5. Understand the principles of machine learning and apply supervised and unsupervised learning techniques to build predictive models.

6. Apply Python programming to manipulate, analyze, and visualize data using relevant Python libraries.

7. Understand the concept of Big Data and use related tools and technologies for data analysis.

8. Demonstrate an ability to make data-driven decisions and effectively communicate analytical findings.

9. Complete a data science project from problem definition to model deployment, demonstrating proficiency in data science skills.

10. Adhere to ethical standards and considerations while dealing with data and analytics. 

Successful Completion of All the Modules is Necessary to Earn the Data Science and Analytics Certification:

Module 1: 0301 Foundations of Data Science Principles


Duration: 20 hours

Delve into the foundational aspects of data science with this introductory certificate. This program offers learners an understanding of what data science entails, the processes involved, and the ethical considerations. Additionally, it provides a rigorous overview of core statistical concepts that underpin most data science tasks. Ideal for those new to the domain, this certificate forms the base upon which more advanced skills can be built.


  • Introduction to Data Science (10 hours)
  • What is Data Science?
  • Data Science Process
  • Role and Responsibilities of a Data Scientist 
  • Ethical Considerations in Data Science
  • Fundamentals of Statistics (10 hours) 
  • Descriptive Statistics
  • Probability Distributions
  • Inferential Statistics
  • Hypothesis Testing and Confidence Intervals

Module 2: 0302 Data Handling and Visualization


Duration: 20 hours

Data isn't just about numbers; it's about telling a story. This certificate focuses on equipping learners with the skills required to process raw data and convert it into meaningful insights. From acquiring and cleaning data to representing it visually using various tools, this program ensures that learners can effectively handle and interpret data, setting the stage for more complex analytical tasks.


  • Data Wrangling and Cleaning (10 hours) 
  • Data Acquisition
  • Data Quality Assessment
  • Data Cleaning Techniques 
  • Introduction to SQL
  • Data Visualization (10 hours)
  • Principles of Data Visualization
  • Tools for Data Visualization (e.g., Matplotlib, Seaborn, Tableau) 
  • Creating Interactive Visualizations

Module 3: 0303 Machine Learning Basics


Duration: 25 hours

Step into the captivating world of machine learning with this foundational certificate. Aimed at those ready to explore predictive modeling, this program introduces learners to both supervised and unsupervised learning techniques. In addition, hands-on programming training using Python and its popular data science libraries ensures that learners are well-equipped to undertake basic machine learning projects.


  • Introduction to Machine Learning (10 hours)
  • Supervised Learning: Regression, Classification
  • Unsupervised Learning: Clustering, Dimensionality Reduction 
  • Introduction to Neural Networks
  • Model Evaluation and Validation
  • Programming for Data Science (15 hours)
  • Python for Data Science
  • Libraries: NumPy, Pandas, Scikit-learn
  • Data Manipulation and Analysis with Python

Module 4: 0304 Data Analytics


Duration: 25 hours

Dive deeper into the data science realm with this advanced certificate. Tackling the challenges and potential of Big Data, this program offers insights into tools and techniques used in modern big data analytics. Moreover, it delves into advanced machine learning concepts, ensuring that learners are prepared to handle complex datasets and derive meaningful analytical insights.


  • Big Data Analytics (10 hours)
  • Introduction to Big Data
  • Tools for Big Data (e.g.,Hadoop,Spark) 
  • Big Data Analytics
  • Applied Machine Learning (15 hours)
  • Feature Selection and Engineering
  • Advanced Supervised and Unsupervised Learning Techniques 
  • Ensemble Methods
  • Introduction to Deep Learning

Module 5: 0305 Data-Driven Decision Making


Duration: 10 hours

Analytics isn't just about understanding data; it's about making informed decisions. This certificate empowers learners to translate data analytics into actionable business insights. The program emphasizes the importance of effective communication in conveying analytical findings. With the culmination in a capstone project, learners will showcase their ability to solve real-world problems, ensuring they're ready to make data- driven decisions in any professional setting.


  • Data-Driven Decision Making (5hours)
  • Translating Analysis into Business Insights 
  • Communication of Analytical Findings
  • Decision Making under Uncertainty

Capstone Project (5 hours)

  • Real-world data science problem solving
  • From problem definition to model deployment 
  • Showcase analytical and problem-solving skills

Certification: A Certificate of Completion will be awarded upon successful completion of the program and final project.

Assessment Method: The program uses continuous assessment via quizzes and assignments at the end of each module and a final project presentation.

Basic Mathematics and Statistics: An understanding of basic mathematical concepts including algebra and calculus, as well as an introduction to statistics, is necessary for various data science tasks such as model building and interpretation.

Fundamental Computer Literacy: This includes the ability to navigate various operating systems, use a variety of software applications, and understand file structures.

Basic Programming Knowledge: While this program will cover Python programming for data science, having prior knowledge of any programming language will help students grasp the concepts more easily. However, those new to programming should not be discouraged, as Python is known for its readability and is a good language for beginners.

Critical Thinking and Problem-Solving Skills: Data science often involves analyzing complex problems and figuring out solutions. Therefore, having strong analytical thinking and problem-solving skills will be a major asset in this course.

General Notes:

  • Students can choose to complete any certificate independently or stack them for a comprehensive data science education.

  • After completing all the certificate programs, students can earn the full "Data Science and Analytics Certificate."

  • Each module consists of lectures, readings, discussion boards, quizzes, assignments, and end-of-module assessments. Feedback will be provided to students. The programs emphasize interactive and hands-on experiences to align with real-world data science scenarios.

Website Design