Data Science Professional Certificate
Course Description
The Data Science Professional Certificate is a practical, career-focused program that helps learners build strong skills in data analysis, statistics, machine learning, and data-driven decision-making. Through hands-on training with tools like Python, data visualization, data cleaning, exploratory analysis, predictive modeling, and real-world projects, learners gain the ability to work with datasets, identify insights, create reports, and solve practical business problems using data science techniques.
Total Classes: 24
Total Hours: 48
Batch:
Class Schedule: Saturday, Monday & Wednesday
Tentative Start Date:
Last Date of Admission:
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, Agriculture).
• Working Professionals & Career Switchers (Finance, Marketing, Healthcare, IT).
• Entrepreneurs & Business Leaders.
• Aspiring Data Scientists & Analysts.
• Tech Enthusiasts & Researchers.
Career Pathways
• Tech & IT: Data Scientist, ML Engineer, Data Engineer.
• Agriculture: data analysis, decision making, predictive analysis and modeling
• E-commerce/Retail: Market/Customer Insights Analyst.
• Telecom: Network Data Analyst.
• Academia: AI Ethics Specialist / Researcher.
Tools & Resources
• Software & Tools: Python, 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).