Course Overview

Data Science Professional Certificate

Instructor:
1. Dipto Biswas | Lecturer (Senior Scale) , Dept. of Information and Communication Engineering (ICE), DIU. His research interests include Machine Learning, NLP, Expert Systems, Cloud Computing, and AI.

2. Md. Sakaid Hosain Shakir | Lecturer , Department of Information and Communication Engineering, DIU

⏱ Duration: 48 Hours | Pace: Self-paced with weekly deadlines and live sessions

Evidence of Demand

A 2025 study by the UK's Department of Education revealed that 90% of businesses are grappling with skills gaps, particularly in entry-level and specialist roles. The data science field is experiencing rapid expansion, with projections indicating a market size of $178.5 billion globally by 2025. In Bangladesh, industries like e-commerce (e.g., Daraz), telecommunications, and banking are increasingly adopting data-driven strategies, creating a vibrant job market for data science professionals.

Purpose and Objectives

Purpose: To equip learners with essential data science skills, enabling them to analyze, visualize, and interpret complex datasets to make data-driven decisions.

Objectives:

• Address the global and local skills gap in data science.
• Align with employer expectations for hands-on experience in data analytics and machine learning.
• Prepare learners for roles such as Data Analyst, Machine Learning Engineer, and Business Intelligence Analyst.
• Provide real-world projects to build a professional portfolio.

Course Content & Class Plan (Modules)

• Module 1: Introduction to Data Science (Week 1-2)

• Module 2: Data Wrangling and Cleaning (Week 3-4)

• Module 3: Data Exploration and Visualization (Week 5-6)

• Module 4: Introduction to Machine Learning (Week 7-8)

• Module 5: Advanced Machine Learning Techniques (Week 9-12)

• Module 6: Deep Learning and Neural Networks (Week 13-14)

• Module 7: Data Science in Practice (Week 15-16)

• Final Capstone Project: (Week 17-18)

Practical & Field Work

Learners will work on a Collaborative group project (selecting a real-world problem, analyzing data, and building a model) and a Final Capstone Project. The capstone requires students to apply everything learned to develop and present a comprehensive data science solution involving data collection, cleaning, analysis, model building, and evaluation.

Learning Outcomes

• Master Python for data science, including data manipulation, visualization, and analysis.
• Perform data wrangling and exploratory data analysis (EDA).
• Apply machine learning models for classification, regression, and clustering tasks.
• Evaluate and optimize models using performance metrics.
• Work on real-world projects to build a portfolio.

Target Audience

• Students & Fresh Graduates (IT, CS, Engineering, Business, Statistics).
• Working Professionals & Career Switchers (Finance, Marketing, Healthcare, IT).
• Entrepreneurs & Business Leaders.
• Aspiring Data Scientists & Analysts.
• Tech Enthusiasts & Researchers.

Entry Requirements

• Minimum Age: 18 years (Ensures participants have the necessary educational background and cognitive skills).
• Basic Programming Knowledge: Familiarity with Python or basic programming concepts (syntax, loops, functions).
• Fundamental Math & Statistics: Basic algebra, probability, and statistics.
• No prior Data Science experience required.

Career Pathways

Career opportunities in:

• Tech & IT: Data Scientist, ML Engineer, Data Engineer.
• Finance & Banking: Risk/Quantitative Analyst.
• Healthcare: Bioinformatics Specialist.
• E-commerce/Retail: Market/Customer Insights Analyst.
• Telecom: Network Data Analyst.
• Academia: AI Ethics Specialist / Researcher.

Tools & Resources

• Software & Tools: Python, R, Pandas, NumPy, Matplotlib, Scikit-learn, TensorFlow, Power BI, SQL.
• Cloud Platforms: Google Colab, AWS, or Google Cloud.
• Platforms: GitHub, Slack/Teams, Zoom, standard LMS (Moodle/Canvas).

Assessment Criteria

• Hands-on Projects: 40%
• Quizzes & Weekly Assignments: 30%
• Final Exam: 20%
• Final Capstone Project: 10%

Financial Information & Certification

• Tentative Course Fee: BDT 5,000 (Includes access to video lectures, assignments, live webinars, software tools, and certification).