Certified Data Scientist Expert (Advanced)

The Certified Data Scientist Expert (CDSE) program is a top-tier, project-based training designed for professionals ready to master advanced data science skills. Spanning expert-level Python programming, big data analysis with Dask, advanced SQL, machine learning, real-world deployment, and specialized modules such as Market Basket Analysis using advanced algorithms like Apriori, FP-Growth, and Eclat. Participants complete over 17 real-world projects, gaining practical experience in building scalable solutions, automating workflows, deploying models, and leading data initiatives in any industry. The program takes professionals beyond foundational knowledge, culminating in a Capstone Project that showcases their ability to lead and deliver impactful data science solutions at scale.

To enroll in the Certified Data Scientist Expert (CDSE) program, a Certified Data Scientist Professional (CDSP) certification is required (mandatory).

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.

• Certified Data Scientist Professionals (CDSP graduates)CDSP graduates aiming to level up into expert-level data science roles.
• Data Scientists & AnalystsData Scientists and Analysts seeking advanced, real-world experience.
• Software Engineers & DevelopersSoftware Engineers and Developers moving into AI and ML leadership.
• Tech Leads & ManagersTech Leads and Managers looking to drive data-driven innovation.
• StatisticiansStatisticians applying their expertise in machine learning at scale.

• Build and optimize advanced predictive models.
• Apply feature engineering and statistical methods at scale.
• Perform in-depth data analysis using Python and real-world datasets.
• Deploy models with APIs, Docker, and cloud tools.
• Present expert-level projects to showcase leadership in data science.

Program Hours

88 Hours

Corporate Training

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

Individual Learners

3-month program with 2 sessions per week (4 hours each), or 4-month program with 1 session per week (6 hours each).

Upon successful completion of the Certified Data Scientist Expert (CDSE) program, participants are awarded an official Certificate of Completion issued by EPSILON AI – Delaware, USA, recognizing their advanced 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. All graduates receive a digitally authenticated certificate containing a unique Certificate ID and personal EPSILON ID for instant, secure online verification, plus a prestigious hardcopy certificate printed with advanced security and branding features: a unique serial number and verification code, a gold-embossed seal with 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 (direct contact hours).
• Achieve a minimum score of 80% on the final examination.
• Successfully complete and present the Capstone Project.

• Object-Oriented Programming (OOP)
• Classes & Objects
• Data Hiding and Encapsulation
• Inheritance
• Project #1: Library System using OOP
• Project #2: Bank System using OOP

• Public datasets websites
• Network Topologies
• Internet and Web Servers
• HTTP Request/Response Cycle
• Web Services & JSON
• Intro to HTML and CSS (online Playlist)
• Scraping Concept
• Download Files
• Beautiful Soup Library
• Project #3: Wuzzuf Jobs data collecting using web services
• Project #4: Diwan Books data collecting system
• Advanced Joins: Full outer joins, self-joins, and cross joins
• Window Functions: Using ROW_NUMBER(), RANK(), DENSE_RANK(), and NTILE(); applying the OVER() clause for advanced data analysis
• Common Table Expressions (CTEs): Introduction to CTEs and their benefits; recursive CTEs for hierarchical data
• Triggers and Stored Procedures: Writing and using triggers for automated actions; creating stored procedures for reusable logic
• Error Handling in SQL: Implementing TRY...CATCH blocks for handling errors
• Advanced Subqueries: Correlated subqueries and their practical use cases
• Data Security and Permissions: Granting and revoking access rights; ensuring data integrity with constraints and best practices
• Project #5: E-commerce system database analysis
• Python and MySQL
• Project #6: Lynda Courses database analysis

• Overview of Dask: Introduction to Dask and its purpose for parallel and distributed computing; differences between Dask and traditional Python tools
• Dask Data Structures: Using Dask Arrays and Dask DataFrames for large-scale data handling; comparing Dask DataFrame operations to pandas
• Lazy Evaluation: Explanation and benefits of Dask's lazy evaluation model; how to trigger computation using .compute()
• Parallel Processing with Dask: Creating and executing Dask computations in parallel; task scheduling with Dask Scheduler; setting up Dask and performing parallel computations
• Project #7: Flights Dataset Analysis with Dask
• Optimizing Dask Performance: Best practices for partitioning data; understanding and monitoring Dask performance using the Dask Dashboard
• Handling Large Datasets: Reading and processing data that doesn't fit into memory; chunking data and using Dask for out-of-core computations
• Dask Delayed and Futures: Using dask.delayed for custom parallel workflows; understanding the Future interface for asynchronous computations
• Hands-On Project: Building and running a data processing pipeline using Dask to handle a large dataset, showcasing distributed computation and optimization techniques
• Project #8: Stanford Open Policing Project with Dask

• Advanced Data Augmentation Techniques: Generating synthetic data for imbalanced datasets using SMOTE or ADASYN; applying data augmentation strategies for robust modeling
• Handling Complex Data Types: Working with text data using advanced vectorization techniques (e.g., TF-IDF, Word2Vec); dealing with time-series data: lag features, rolling statistics, and trend extraction
• Hands-On Project #9: Build and execute an end-to-end data preprocessing pipeline with automation for a complex dataset (Loan Prediction)

• Ensemble Methods
• Bagging & Boosting
• XGBoost
• Evaluating Models
• Project #10: Churn Problem
• Clustering Problems
• DBSCAN
• Project #11: RFM Clustering Customer Segmentation
• Association Rules
• Apriori Algorithm
• Dimension Reduction: Linear Transformations; Eigen Values, Eigen Vectors, and Eigen decomposition; PCA
• Project #12: MNIST Data with PCA and Traditional ML
• Project #13: X-ray Data
• Feature Selection Techniques: Filter methods (e.g., correlation, chi-square test); Wrapper methods (e.g., Recursive Feature Elimination); Embedded methods (e.g., feature importance using tree-based models)
• Hands-On Activities: Apply feature engineering techniques on a dataset
• Market Basket Analysis: Market Basket Analysis concepts; Association Rules
• Project #14: Market Basket Analysis
• Apriori Algorithm
• Project #15: Market Basket Analysis using Apriori
• FP-Growth Algorithm
• Eclat Algorithm
• Project #16: Market Basket Analysis using Eclat

• Introduction to Model Deployment
• Preparing the Model for Deployment
• Creating a Flask API for the Model
• Writing the Dockerfile
• Deploy Model with Docker
• Testing the Deployed API
• Deploying a Cloud Platform
• Monitoring and Maintenance
• Project #17: Model Deployment with Flask, Docker, and Cloud

• Capstone Project discussion, presentation, and evaluation.