Data Science Professional Certificate

This instructor-led Data Science Professional Certificate program delivers a comprehensive, hands-on learning journey that equips participants with the essential skills, tools, and methodologies used in modern data science.

Designed for analysts, developers, IT professionals, and career changers, the program offers comprehensive training covering data wrangling, statistical analysis, machine learning, visualization, and deployment. Participants will gain job-ready, industry-relevant experience working with real-world datasets and tools used by data professionals.

Overview

Duration & Delivery

• Format: Instructor-led training (Online or In-Person)

• Duration: 8 Days (recommended; customizable)

• Includes: Lectures, live coding, hands-on labs, practical exercises, capstone project, course materials

• Instructor: Experienced data scientist with industry expertise

Learning Objectives

By the end of this program, participants will be able to:

• Understand the data science lifecycle and process

• Collect, clean, and prepare data for analysis

• Perform exploratory data analysis (EDA) using statistical methods

• Build, evaluate, and deploy machine learning models

• Use popular libraries such as Pandas, NumPy, Scikit-learn, TensorFlow, and Matplotlib

• Communicate insights effectively through visualization and storytelling

• Apply data science to solve real-world business problems

• Understand ethical considerations in data science projects

Program Features

• Comprehensive, structured curriculum from data basics to advanced modeling

• Live instructor-led delivery with interactive lectures

• Hands-on labs and real-world projects using industry-standard tools

• Practical exercises with real-world datasets

• Capstone project demonstrating end-to-end data science skills

• Guidance on industry best practices and career readiness

• Delivered by an experienced data scientist with teaching expertise

Detailed Course Schedule

Day 01: Introduction to Cybersecurity Fundamentals

• Overview of cybersecurity landscape and threats

• Key principles: Confidentiality, Integrity, Availability (CIA Triad)

• Risk management concepts

• Security frameworks and compliance (ISO, NIST)

• Defensive security best practices

Outcome: Foundational understanding of cybersecurity principles and risk management strategies.

Day 02: Ethical Hacking Concepts and Legal Considerations

• What is ethical hacking?

• Penetration testing lifecycle and methodology

• Reconnaissance and information-gathering techniques

• Rules of engagement, legal boundaries, and responsible disclosure

• Case studies of real-world breaches

Outcome: Clear understanding of ethical hacking processes and legal/ethical responsibilities.

Day 03: Reconnaissance and Vulnerability Scanning

• Active and passive reconnaissance techniques

• Open-Source Intelligence (OSINT) tools and methods

• Vulnerability scanning fundamentals

• Hands-on labs using tools like Nmap and OpenVAS

• Interpreting scan results and prioritizing risks

Outcome: Ability to conduct reconnaissance and identify vulnerabilities using industry-standard tools.

Day 04: Exploitation Techniques and Defensive Countermeasures

• Common attack vectors: web, network, system, social engineering

• Exploitation fundamentals and controlled demonstrations

• Password attacks, privilege escalation, lateral movement

• Hands-on practice with safe, simulated exploits

• Defensive strategies and hardening systems

Outcome: Awareness of exploitation techniques and methods to defend against them.

Day 05: Reporting, Risk Mitigation, and Next Steps

• Documenting findings and writing security assessment reports

• Communicating risks to stakeholders

• Risk mitigation planning and remediation

• Career paths in cybersecurity and recommended certifications

• Review session, Q&A, and wrap-up discussion

Outcome: Skills to report security findings, communicate risks, and plan further learning.

Day 06: Advanced Machine Learning and Deep Learning

• Feature engineering and selection

• Hyperparameter tuning

• Introduction to neural networks

• Using TensorFlow/Keras to build simple models

• Model deployment considerations

Outcome: Exposure to advanced modeling techniques and neural network implementation.

Day 07: Applied Data Science and Business Problem Solving

• Framing real-world business problems

• Translating business goals into data analysis plans

• Case studies across industries

• Communicating findings to non-technical stakeholders

• Ethical considerations in data science

Outcome: Ability to plan and execute practical data science projects for business impact.

Day 08: Capstone Project and Presentation

• End-to-end project applying learned concepts

• Data acquisition, cleaning, EDA, modeling, and visualization

• Preparing a professional report and presentation

• Group or individual project presentations

• Review, feedback, and next learning steps

Outcome: Portfolio-ready project demonstrating comprehensive data science skills.

Hands-On Learning Approach

• Live coding and instructor-led demos

• Guided labs using Jupyter Notebooks and Python

• Real-world datasets and problem-solving scenarios

• Collaborative group discussions and projects

• Capstone project showcasing end-to-end workflow

• Instructor feedback and personalized guidance

Master the end-to-end data science workflow. Build the skills you need to launch or advance your data career.

Prerequisites

• Basic understanding of programming concepts (preferably Python)

• Comfort with basic math and statistics (course includes refreshers)

• Data analysts and business analysts looking to upskill

• Software developers and IT professionals moving into data science

• University students and graduates preparing for data careers

• Technical managers evaluating data science adoption

• Career changers seeking comprehensive data science training

Who Should Attend?

Certification Readiness

This program lays the foundation for industry-recognized certifications such as:

• Microsoft Azure Data Scientist Associate

• IBM Data Science Professional Certificate

• Google Data Analytics Professional Certificate

• Coursera, edX, and Udacity Data Science tracks.