Certified Python Data Analyst Professional

The Certified Python Data Analyst Professional (CPDAP) program is a practical, project-based training designed to build strong foundations in Python, SQL, statistics, and data visualization. Covering the full data analysis pipeline, it equips participants with the hands-on skills needed to clean, analyze, and present data effectively, preparing them for real-world roles such as Data Analyst or Business Analyst, or to advance toward Data Science roles with a job-ready portfolio. The program concludes with a comprehensive final project, enabling learners to demonstrate their capabilities and build a portfolio that supports career entry into data-focused roles.

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 AnalystsThose seeking to build a strong foundation in Python, data handling, and analytical thinking.
• Developers and Software EngineersProfessionals aiming to expand their skill set into data analytics and visualization.
• Business and Analytics ProfessionalsIndividuals looking to leverage data analysis for strategic decision-making and business insights.

• Perform data analysis and statistical evaluations using Python.
• Clean, transform, and visualize data to extract meaningful insights.
• Design and query relational databases using SQL.
• Apply feature engineering techniques to prepare data for analysis.
• Conduct A/B testing and interpret experiment results.

Program Hours

84 Hours

Corporate Training

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

Individual Learners

2-month, 3-week program with 2 sessions per week, each lasting 4 hours; OR a 3-month, 1-week program with 1 session per week, each lasting 6 hours.

Locations

USA (Newark, Delaware); Saudi Arabia (Al-Hassan Bin Aly, Riyadh); UAE — partnership with Elbadya Quantum (Jumeirah Lake Towers, Dubai); Egypt (New Cairo; Nasr City, Cairo; Mohandessin, Giza).

Upon successful completion of the Certified Python Data Analyst Professional (CPDAP) program, participants will receive an official Certificate of Completion issued by Epsilon AI – Delaware, USA, recognizing their proficiency in Python programming, data analysis, statistics, SQL, and data visualization. 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. 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 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.
• 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, and the importance of Python in Data Science.
• Python Fundamentals: Input/Output, variables, and 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, and 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): using GitKraken to upload a 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, and 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, and eigenvalues, eigenvectors and eigendecomposition.
• Statistics Essentials — Descriptive Statistics: understanding data, central tendency, measures of dispersion, correlation, normal distributions, standard normal distributions, and sample distribution.
• Inferential Statistics: Central Limit Theorem, statistical significance, hypothesis testing, A/B testing, and confidence interval.
• Probability: basics of probability, conditional probability, and Bayes' theorem.
• Calculus: rate of change, first-order and second-order derivatives, partial derivatives, and the chain rule.

• EDA - Exploratory Data Analysis Process.
• Linear Algebra with NumPy: vector operations, matrix operations, and vector norm.
• NumPy: create NumPy array, indexing, arithmetic and logical operations, and universal array functions.
• Statistics Essentials — Descriptive Statistics: understanding data, central tendency, measures of dispersion, correlation, normal distributions, standard normal distributions, and sample distribution.
• Inferential Statistics: Central Limit Theorem, statistical significance, hypothesis testing, A/B testing, and confidence interval.
• Pandas: Series, Data Frames, data input & output, data cleaning and transformation, useful methods, the apply function, grouping data and aggregate functions, merging, joining and concatenating, and pivoting data.
• Project #6: Movies dataset from Kaggle.
• Project #7: FIFA dataset from Kaggle.

• Plotly: distribution plots, categorical plots, and 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 (cont.).
• Project #7: FIFA dataset from Kaggle (cont.).

• Feature Engineering and Extraction: domain knowledge features, date and time features, string operations, web data, and geospatial features.
• Feature Transformations: data cleaning or cleansing, working with duplicated data, detecting and handling outliers, working with missing data, working with categorical data, dealing with imbalanced classes, splitting data into train and test sets, feature scaling, and the 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 the cloud.
• Project #10: Deployment on Streamlit.

• An end-to-end project where participants apply data cleaning, exploratory data analysis (EDA), visualization, and insight generation to solve a real-world data problem and showcase their full analytical skillset.