Post Graduate Certification Program in Data Science and Machine Learning

Build practical expertise in Python, SQL, Statistics, Data Analytics, Machine Learning, Deep Learning and Predictive Modelling through live instructor-led training, real-world projects and industry-focused learning.

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About the Program

The Post Graduate Certification Program in Data Science and Machine Learning is designed to help learners develop strong analytical, programming and predictive modelling skills required in today’s data-driven industries.

The program covers Python, SQL, Statistics, Data Visualization, Machine Learning, Deep Learning and Model Deployment, with a strong focus on practical implementation.

Through live sessions, real-world datasets, industry projects and hands-on assignments, learners gain experience in solving business problems and building intelligent predictive solutions.

The program is suitable for graduates, working professionals and career switchers looking to build a career in Data Science and Machine Learning.

The Sifr Sophia Advantage

A career-focused learning experience designed to combine technical knowledge, practical implementation, mentorship and professional development.

1. Career Launch & Job Assurance

Get structured support with resume building, interview preparation, career counselling and relevant employment opportunities.

2. Dedicated Mentorship

Learn from experienced trainers and receive continuous guidance throughout your program journey.

3. Hands-On Learning

Work with real datasets, assignments and practical machine learning exercises based on industry scenarios.

4. Professional Certification

Earn a professional certification after successfully completing the required learning modules, projects and assessments.

5. Real-World Projects

Build portfolio-ready projects covering analytics, machine learning, predictive modelling and business problem-solving.

6. Domain Specialization

Understand how Data Science and Machine Learning are applied in finance, retail, healthcare, marketing, technology and operations.

WHAT YOU WILL LEARN

A Curriculum Built for the AI-Driven Industry

Foundations of Data Science

Build a strong foundation in Data Science by understanding how data is collected, cleaned, analyzed, and transformed into meaningful insights for business decision-making.

Topics Covered

  • Introduction to Data Science
  • Data Science Lifecycle
  • Structured & Unstructured Data
  • Data Collection & Cleaning
  • Exploratory Data Analysis
  • Business Analytics Fundamentals
  • Data-Driven Decision Making

Build a strong foundation in Python and learn how it is used for data processing, analysis, automation and Machine Learning applications.

Topics Covered

  • Python Fundamentals
  • Variables & Data Types
  • Conditional Statements
  • Loops
  • Functions
  • Lists, Tuples & Dictionaries
  • NumPy
  • Pandas
  • Data Manipulation
  • Python for Analytics

Develop practical SQL skills to access, manage and analyze large datasets stored in relational database systems.

Topics Covered

  • Database Fundamentals
  • SQL Queries
  • SELECT Statements
  • Filtering & Sorting
  • Joins
  • Subqueries
  • Aggregate Functions
  • GROUP BY
  • Window Functions
  • Advanced SQL
  • Data Extraction

Develop the statistical foundation required to interpret data, identify patterns and build reliable Machine Learning models.

Topics Covered

  • Descriptive Statistics
  • Mean, Median & Mode
  • Variance & Standard Deviation
  • Probability Fundamentals
  • Probability Distributions
  • Sampling
  • Hypothesis Testing
  • Confidence Intervals
  • Correlation
  • Regression Fundamentals

Learn how to represent complex datasets through visual reports and dashboards that support effective business decision-making.

Topics Covered

  • Data Visualization Principles
  • Charts & Graphs
  • Matplotlib
  • Data Storytelling
  • Power BI
  • Tableau
  • Dashboard Design
  • KPI Development
  • Interactive Reporting
  • Business Intelligence

Understand the core concepts of Machine Learning and learn how algorithms use historical data to generate predictions and classifications.

Topics Covered

  • Introduction to Machine Learning
  • Supervised Learning
  • Unsupervised Learning
  • Training & Testing Data
  • Linear Regression
  • Logistic Regression
  • Classification
  • Clustering
  • Model Evaluation
  • Feature Engineering

Learn how labelled data is used to train predictive models for classification and regression-based business problems.

Topics Covered

  • Linear Regression
  • Logistic Regression
  • Decision Trees
  • Random Forest
  • K-Nearest Neighbours
  • Support Vector Machines
  • Classification Models
  • Regression Models
  • Accuracy Metrics
  • Model Validation
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Step by Step

How Do I Join the PG Certification Program in Data Science and Machine Learning?

Step 1

Apply Online

Step 1

Assessment

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Step 1

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