Certified Data Scientist Professional

The Certified Data Scientist Professional (CDSP) program is a comprehensive, project-based learning journey designed to equip participants with the skills, tools, and hands-on experience needed to thrive in the dynamic world of data science and AI. Spanning the full spectrum of the data science pipeline—from programming and database management to machine learning and model deployment—this certification empowers learners to become job-ready professionals in high-demand roles such as Data Analyst, Data Scientist, Machine Learning Engineer, and more. The CDSP program equips learners with essential skills in Python, SQL, data analysis, visualization, and machine learning. Through hands-on projects and expert mentorship, participants build practical experience across the full data science workflow. The program concludes with a Capstone Project, helping learners showcase their abilities and build a strong portfolio for high-growth careers in data and AI.

No specific prerequisites are required to enroll, but basic familiarity with programming, math, and statistics will enhance your learning experience. Basic Programming Skills (Optional): a basic understanding of at least one programming language. Math and Statistics (Optional): familiarity with basic concepts in math and statistics.

How you'll learn

• Instructor-Led Training (ILT): Participate in interactive, classroom-based sessions led by experienced industry professionals for an immersive learning experience.
• Virtual Instructor-Led Training (VILT): Join live online classes from anywhere, offering flexibility without compromising interactivity and mentorship.

• Aspiring Data ScientistsThose looking to develop their skills in machine learning and data analysis.
• Developers and Software EngineersProfessionals seeking to expand their data science capabilities.
• Analytics Managers and ProfessionalsIndividuals who want to incorporate data science into business strategy.
• StatisticiansThose interested in using statistical knowledge in a machine learning context.

• Build and optimize predictive models using feature engineering.
• Design systems using data analytics and statistical methods.
• Conduct analyses with Python and communicate insights.
• Create A/B tests, deploy models, and manage APIs.
• Showcase your data science expertise to enhance your profile.

Corporate Training

24 full days, with each day lasting 7-8 hours.

Individual Learners

6-month program with 2 sessions per week, each session lasting 4 hours; or a 7-month program with 1 session per week, each session lasting 6 hours.

Upon successful completion of the Certified Data Scientist Professional (CDSP) program, participants will receive an official Certificate of Completion issued by EPSILON AI – Delaware, USA, recognizing their proficiency in data science, machine learning, and model deployment. The program is delivered in alignment with international standards, including ISO 21001 and ISO 9001, and follows globally recognized frameworks such as IACET and CPD, ensuring a high-quality, industry-relevant learning experience. All graduates receive a digitally authenticated certificate containing a unique Certificate ID and personal EPSILON ID, enabling instant, secure online verification. This credential is ideal for professional portfolios, LinkedIn profiles, and employer validation. In addition to the digital credential, a prestigious hardcopy certificate is issued, printed with advanced security and branding features, including a unique serial number and verification code, a gold-embossed seal and authorized EPSILON AI signatures, and UV-printed security elements for enhanced authenticity and protection against forgery.

• Attend a minimum of 80% of live instructional sessions.
• Achieve a minimum score of 80% on the final examination.
• Successfully complete and present the Capstone Project.

• Introduction to Data Science and the AI Landscape — Overview of AI, Data Analysis, Machine Learning, and Data Science concepts; introduction to core AI domains: Computer Vision, Autonomous Systems, and Natural Language Processing (NLP)
• The Data Science Process: A Structured Approach — Understanding each stage of the Data Science process from data collection to model deployment; key steps in creating data-driven insights and solutions
• Roles and Career Paths in Data Science — Exploration of key Data Science roles: Data Engineer, Data Analyst, Data Scientist, ML Engineer, and MLOps Engineer; skill requirements, responsibilities, and unique contributions of each role
• Career Development Roadmap for Data Science and AI — Building a career path in Data Science and AI: skills, certifications, and progression; tools and resources to accelerate career growth

• Setting Up the Python Environment — Anaconda installation; virtual environments; command line essentials; package management with Conda & Pip; introduction to Jupyter Notebook; importance of Python in Data Science
• Python Fundamentals: Input/Output; variables; Data types (Numbers & Math; Boolean, Comparison, Bitwise and Logic; Strings and String Methods); Control Structures (If statements; For & While Loops); Data Structures (Lists, Tuples, Sets, Dictionaries)
• List and Dictionary Comprehensions
• Exception Handling
• File Operations
• Functions and Lambda Expressions
• Built-in functions & Operators (zip, enumerate, range)
• Functional Programming with Map, Filter, Reduce
• Modules & Packages
• PROJECT #1 Rock Paper Scissors
• PROJECT #2 Hangman
• Git & GitHub (Version Control) — GitKraken; Upload Project to Your Profile

• Relational Database Concepts - RDBMS — Database design; Tables, Columns and Data types; Relationships (One-To-Many & Many-To-Many)
• MySQL for Data Science — MySQL Workbench; CRUD Operations; Selecting data; Filtering data; Ordering data; Limiting data; Aggregate Functions; Grouping data; Subqueries; Date and time management; Inserting new data; Updating data; Deleting data
• Database connectivity and data manipulation using Python
• ACTIVITY: Design database structure like Facebook, Talabat, and YouTube
• PROJECT #3 Design E-commerce Database
• PROJECT #4 E-commerce System Database Analysis
• PROJECT #5 Lynda Courses Database Analysis

• Linear Algebra — Vector operations; Matrix operations; Vector Norm; Eigen Values, Eigen Vectors and Eigen decomposition
• Statistics Essentials — Descriptive Statistics: Understanding data; Central Tendency; Measures of Dispersion; Correlation; Normal Distributions; Standard Normal Distributions; Sample Distribution
• Inferential Statistics — Central Limit Theorem; Statistical Significance; Hypothesis Testing; A/B Testing; Confidence Interval
• Probability — Basics of probability; conditional probability; Bayes' theorem
• Calculus — Rate of Change; First order and second order derivatives; Partial Derivatives; Chain rule

• EDA - Exploratory Data Analysis Process
• Linear Algebra with NumPy — Vector operations; Matrix operations; Vector Norm
• NumPy — Create NumPy Array; Indexing; Arithmetic and Logical operations; Universal Array Functions
• Statistics Essentials — Descriptive Statistics: Understanding data; Central Tendency; Measures of Dispersion; Correlation; Normal Distributions; Standard Normal Distributions; Sample Distribution
• Inferential Statistics — Central Limit Theorem; Statistical Significance; Hypothesis Testing; A/B Testing; Confidence Interval
• Pandas — Series; DataFrames; Data Input & Output; Data Cleaning and transformation; Useful Methods; Apply function; Grouping data and aggregate functions; Merging, Joining and Concatenating; Pivoting Data
• PROJECT #6 Movies dataset from Kaggle
• PROJECT #7 FIFA dataset from Kaggle

• Plotly — Distribution Plots; Categorical Plots; Matrix Plots
• Streamlit — Customization of plots (adjusting colors, markers, line styles, Limits, Legends, Layouts); Text and Annotations; Building dashboards and interactive visualizations
• PROJECT #6 Movies dataset from Kaggle (continued)
• PROJECT #7 FIFA dataset from Kaggle (continued)

• Feature Engineering and Extraction — Domain knowledge features; Date and Time features; String operations; Web Data; Geospatial features
• Feature Transformations — Data Cleaning or Cleansing; Work with Duplicated data; Detect and Handle Outliers; Work with Missing data; Work with Categorical data; Dealing with Imbalanced classes; Split data to Train and Test Sets; Feature Scaling; Data Preprocessing Mind Map
• PROJECT #8 Google Play Store
• PROJECT #9 Uber Analysis

• Python as a Backend Language
• Streamlit as an app framework for data apps
• Integrate Machine Learning Model
• Make a web service using Streamlit
• Deployment with Streamlit to cloud
• PROJECT #10 Deployment on Streamlit

• End-to-end project demonstrating data cleaning, EDA, visualization, and insights presentation

• Intro to Machine Learning
• Calculus — Rate of Change; First order and second order derivatives; Partial Derivatives; Chain rule
• Supervised Learning — Regression: Simple Linear Regression; Multiple Linear Regression; Other Regression Methods (polynomial); Regularization Techniques; Evaluating Regression Model Performance
• PROJECT #11 Used Cars Prices Prediction
• PROJECT #12 Air Flight Price Predictions
• Supervised Learning — Classification: Logistic Regression; K-Nearest Neighbors (KNN); Evaluate Model (Accuracy); SVM; Probability; Bayes Theorem; Naive Bayes; Decision Trees; Random Forests; Evaluating Model (ROC)
• PROJECT #13 Airline Passenger Satisfaction Problem
• PROJECT #14 Credit Card Approval Problem
• Unsupervised Learning — Clustering: K-Means; Hierarchical Clustering
• PROJECT #15 House Clustering
• PROJECT #16 Online Retail Clustering
• Model Selection & Evaluation — Cross Validation; Hyperparameter Tuning: Grid Search; Randomized Search

• PROJECT #17 Used Cars Price Predictor Web Application Deployment on Streamlit

• Professional Development — Building an online presence: Kaggle, GitHub, Medium, and LinkedIn; Resume building, networking strategies for data science professionals, and Job seeking

• FINAL PROJECT DISCUSSION — Comprehensive project synthesizing skills in data collection, EDA, model building, and deployment