Four-Month Data Science Certification Program
This course is ideal for students, IT professionals, and STEM learners who want to build a strong foundation in Data Science and Artificial Intelligence.
This course is ideal for students, IT professionals, and STEM learners who want to build a strong foundation in Data Science and Artificial Intelligence.
The Data Science & Artificial Intelligence (AI) course is designed to equip learners with the fundamental knowledge and practical skills required to analyze data, build intelligent systems, and solve real-world problems using modern AI techniques.
In this course, students will learn how to collect, clean, analyze, and visualize data, and apply machine learning algorithms to make predictions and informed decisions. The course combines theory with hands-on projects using industry-standard tools and programming languages such as Python.
Feature engineering and selection
Feature engineering and selection
Feed forward and CNN classifier
Ensemble methods
Autoencoders
Foundation Models: Understanding pretraining and fine-tuning
VGG16
Foundation model classifiers
Implementing feature engineering with scikit-learn Lab
Building a basic neural network and deep network with Pytorch Lab
Training and evaluating ensemble models using Pytorch Lab
Basic understanding of Mathematics (especially algebra)
Fundamental knowledge of Statistics (mean, median, probability concepts)
Basic computer literacy
A computer or laptop (minimum 4GB RAM recommended)
Stable internet connection
Ability to install software and tools
Python installed (preferably latest version)
Code editor (VS Code, Jupyter Notebook, or similar)
Understand the Data Science lifecycle (data collection, cleaning, analysis, modeling, and deployment)
Clean, preprocess, and transform raw data into usable formats
Perform exploratory data analysis (EDA)
Visualize data using charts and dashboards
Apply statistical concepts to analyze datasets
Explain core Artificial Intelligence and Machine Learning concepts
Implement supervised learning algorithms (e.g., regression and classification)
Apply unsupervised learning techniques (e.g., clustering)
Evaluate and optimize machine learning models
Understand the basics of deep learning and neural networks
Write Python code for data analysis and modeling
Use libraries such as NumPy, Pandas, Matplotlib, and Scikit-learn
Work with Jupyter Notebook or Google Colab environments
Build and test small AI-based projects
Solve real-world problems using data-driven approaches
Interpret and communicate analytical results effectively
Understand ethical issues and responsible AI practices
Develop confidence to pursue advanced AI and Data Science topics
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Data scientist with 5+ years of experience applying machine learning to real-world healthcare and public health challenges. Proven expertise in developing predictive models, fine-tuning large language models, and leveraging explainable AI for actionable insights. I have worked extensively with health data, including the Ethiopian Demographic and Health Survey and Ethiopian Public Health Institute datasets, collaborating with public health experts on research projects addressing childhood immunization, infectious disease outbreaks, and patient care optimization. Passionate about using AI and data science to improve global health outcomes, I seek opportunities to contribute to innovative research that bridges data science and healthcare.
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I have over 12 years of experience as a software developer and manager. In addition, I have experience leading teams and creating complex, innovative apps. My coding skills: Python (Flask, Django, machine learning, data analysis), Java, PHP (CodeIgniter, Laravel), Android, C#, VB, Cordova, HTML, CSS, JavaScript, MySQL, PostgreSQL, Git, RESTful APIs, AI/ML, NodeJs, AngularJS, Docker, SQL queries, Firebase, and system integration.
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As an Advisory Data Scientist at IBM Research’s Semiconductor division, he spearheads two major data science initiatives, leveraging his expertise to address complex challenges. With a strong foundation in deep learning, mathematical optimization, and advanced data science, he has successfully driven revenue growth in cloud support services by delivering data-driven solutions. He brings over six years of experience in data science and eight years in neural networks and machine learning research. His impactful contributions at IBM include implementing a customer segmentation system, developing a chatbot, architecting data pipelines, and deploying end-to-end AI solutions. He has also built a monitoring system and crafted a machine learning solution to streamline IBM Cloud Support operations, resulting in significant savings, enhanced customer satisfaction, and substantial revenue growth. He holds a Ph.D. in Electrical Engineering with a focus on deep learning, during which he published two papers on AI methodologies to assess ultrasound quality and advanced both local and global Explainable AI (XAI) techniques. His Master’s degrees in Electrical Engineering and Mathematics further strengthen his ability to tackle complex data science problems, combining theoretical insight with practical skill.
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language
English
Duration
00h 00mLevel
beginner
Expiry period
Lifetime
Certificate
Yes
SMART AI
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Certificate Course
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