Best Education Provider in
Data Science Program
Post Graduate Certification Program in Data Science and Machine Learning
Master in-demand skills with a job-assured pathway, expert mentorship, and real-world projects designed to accelerate your career in Data Science and Machine Learning.
In association with
9 Months Program
Weekend Online Interactive Learning
10,000+ Career Transitions
35+ Tools & Projects
1:1 Industry Mentorship
Hands-on Capstone Projects
800+ Hiring Partners
Placement Assistance
Companies Hiring
00 +
Students Placed
00 +
Highest Package
00 LPA
Industry Experts
00 +
Highest Salary Hike
00 %
Alumni Network
Placement Stories
Avinash Singh
Chitra Singh
Bhavana Gusain
Dolly Monga
Sakshi Sharma
Karishma Solanki
Aman Kumar Singh
Tarun Negi
Payal Chaturvedi
Deepanshu Tomar
Get offers from 800+ top companies
About the Program
Sabak Harbor’s Post Graduate Certification Program in Data Science and Machine Learning is designed to equip ambitious learners with the skills needed to excel in today’s data-driven world. By combining practical training, real-world case studies, and expert mentorship, the program helps bridge the gap between academic knowledge and industry demands. Tailored for fresh graduates and early-career professionals (0–3 years of experience), this program builds strong foundations in statistics, data analysis, and machine learning while enhancing your problem-solving and analytical thinking skills.
You’ll gain hands-on experience with 35+ cutting-edge tools and industry projects, covering supervised and unsupervised learning, deep learning, natural language processing (NLP), and computer vision. The curriculum also includes applications of Generative AI and advanced ML frameworks to accelerate model building, optimization, and deployment in real-world business scenarios.
06+
Years experience7k+
Students each year24+
Award Wining
Key Highlights
Experience an all-new 2025 curriculum
- 6 Weeks
- 1200 Students
- 4.6 (890 ratings)
Build a strong foundation in programming, databases, and analytics. Learn the tools and logic required to solve real-world data problems.
Topics covered
Python Programming: Variables, data structures, functions, OOPs.
SQL for Data Analysis: Joins, subqueries, CTEs, window functions.
Excel for Business Analytics: Pivot tables, lookups, dashboards.
Data Handling with Pandas: Cleaning, filtering, aggregations.
Version Control: Git & GitHub for collaboration.
Mini Project: Customer sales insights dashboard.
Skills acquired
Python
SQL
Excel
Pandas
Git & GitHub
Data Cleaning
- 5 Weeks
- 1080 Students
- 4.6 (830 ratings)
Learn how to clean, transform, and explore datasets to uncover hidden insights and patterns before modeling.
Topics covered
Data Wrangling: Missing values, duplicates, outliers.
Feature Engineering: Encoding, scaling, transformations.
Exploratory Data Analysis: Trends, anomalies, distributions.
Visualization Tools: Seaborn, Plotly, Matplotlib.
Case Study: Retail customer segmentation.
Skills acquired
EDA
Feature Engineering
Visualization
Data Transformation
Data Profiling
- 6 Weeks
- 990 Students
- 4.7 (870 ratings)
Master the statistical and mathematical foundations that power machine learning algorithms.
Topics covered
Descriptive Statistics: Mean, variance, distributions.
Probability: Conditional probability, Bayes theorem.
Inferential Statistics: Hypothesis testing, ANOVA, t-tests.
Correlation & Regression: Linear, multiple regression basics.
Case Study: Predicting consumer behavior using stats.
Skills acquired
Probability
Hypothesis Testing
Regression
ANOVA
Bayesian Methods
- 7 Weeks
- 1020 Students
- 4.7 (910 ratings)
Dive into supervised, unsupervised, and advanced ML techniques to solve business and technical problems.
Topics covered
Supervised Learning: Regression, classification models.
Unsupervised Learning: Clustering, PCA, dimensionality reduction.
Time Series Forecasting: ARIMA, seasonal trends.
Model Evaluation: ROC, precision-recall, cross-validation.
Ensemble Learning: Bagging, boosting, XGBoost.
Case Study: Predictive analytics in banking/retail.
Skills acquired
Regression
Classification
Clustering
PCA
Forecasting
XGBoost
- 4 Weeks
- 870 Students
- 4.6 (780 ratings)
Work with large-scale datasets and learn how cloud platforms enable real-time analytics and scalability.
Topics covered
Big Data Concepts: Distributed data processing basics.
Apache Spark: PySpark operations for big data.
Cloud Platforms: AWS, GCP, Azure for analytics.
ETL Pipelines: Data ingestion, transformation, loading.
Mini Project: Real-time stock market data analysis.
Skills acquired
Big Data
Spark
Cloud Analytics
ETL Pipelines
Real-time Processing
- 6 Weeks
- 950 Students
- 4.7 (820 ratings)
Learn deep learning techniques and apply them to real-world tasks like image recognition, text analysis, and conversational AI.
Topics covered
Neural Networks: Perceptrons, activation, backpropagation.
CNNs: Image classification, object detection.
NLP Techniques: Tokenization, embeddings, sentiment analysis.
Transformers: BERT, GPT, advanced LLMs.
Mini Project: Customer review sentiment analyzer.
Skills acquired
Neural Networks
CNN
NLP
Transformers
LLMs
- 5 Weeks
- 910 Students
- 4.7 (800 ratings)
Learn how to deploy, monitor, and maintain machine learning solutions in production environments.
Topics covered
Deployment Frameworks: Flask, FastAPI, Streamlit.
Cloud Services: AWS SageMaker, GCP Vertex AI.
Containerization: Docker, Kubernetes basics.
MLOps Pipelines: CI/CD, model versioning, retraining.
Mini Project: Fraud detection API deployment.
Skills acquired
Model Deployment
Flask
Docker
Kubernetes
MLOps
- 4 Weeks
- 880 Students
- 4.8 (770 ratings)
Apply everything you’ve learned to solve a real-world problem and prepare for your next career step with job readiness training.
Topics covered
Capstone Projects: Domain-specific (BFSI, healthcare, retail, logistics).
Portfolio Building: Showcase GitHub projects, dashboards, notebooks.
Career Workshops: Resume prep, LinkedIn optimization, mock interviews.
Hackathons: Compete in real-time problem-solving events.
Skills acquired
Capstone Execution
Portfolio Showcase
Interview Prep
Hackathon Participation
Domain Expertise
Master the 80+ Essential Industry Tools
Python
Pandas
Sklearn
Statsmodels
MySQL
AWS
GCP
Azure
PySpark
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PyTorch
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Keras
OpenAI
GPT
LlamaIndex
Claude
CrewAI
LangGraph
Autogen
Power BI
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Tableau
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MongoDB
Docker
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Kubernetes
Streamlit
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Airflow
MLFlow
Kafka
Master real-world ML projects to accelerate your career
Work on practical ML projects, applying algorithms and models to solve critical business and industry challenges with end-to-end implementation.
25+
Machine Learning Projects
Loan Default Prediction
Predicting loan defaults is a critical task for banks and financial institutions to reduce losses and manage risk. In this project, you will clean and preprocess financial data, including income, credit history, and repayment behavior. Using ML models such as Logistic Regression and Random Forest, you will identify high-risk applicants with precision. Feature engineering will be applied to capture hidden patterns in the dataset for better accuracy. Advanced methods like Gradient Boosting and XGBoost will be used to optimize model performance. The model will be evaluated using ROC-AUC and Precision-Recall metrics for reliability. Finally, deploy the model as a decision-support tool for loan officers. Know More
Skills Learned
Classification
Feature Engineering
Risk Modeling
Customer Churn Prediction
Customer churn is a major challenge for telecom and subscription-based businesses. In this project, you will work on customer demographic and usage datasets to identify churn patterns. Classification models such as Logistic Regression, Random Forest, and Gradient Boosting will be applied. You will learn to balance imbalanced datasets using resampling and SMOTE techniques. The project also explores feature importance to identify critical churn factors. Model accuracy will be measured with F1-score and confusion matrix to ensure robustness. Deliver insights to help businesses design better customer retention strategies. Know More
Skills Learned
Logistic Regression
Ensemble Learning
Imbalanced Data Handling
House Price Prediction
Predicting real estate prices is one of the most common ML use cases. In this project, you will work with housing datasets containing multiple features like size, location, and amenities. Linear Regression, Ridge, and Lasso will be applied for predictive modeling. Feature scaling and outlier detection will improve model generalization. Advanced models like Random Forest Regressor and Gradient Boosting will be tested for better accuracy. The project also emphasizes cross-validation and hyperparameter tuning. Build a web app prototype where users can input property details to get predictions. Know More
Skills Learned
Regression
Model Optimization
Feature Scaling
Stock Price Forecasting
Stock markets are highly volatile, making prediction a complex challenge. This project focuses on time series forecasting of stock prices using historical data. ARIMA, Prophet, and LSTM models will be applied to identify trends and volatility. Feature engineering on lag variables and moving averages will improve accuracy. Advanced models will incorporate external features such as news sentiment and market indices. Model evaluation will use RMSE and MAPE for quantitative performance. The final solution can serve as a financial advisory tool. Know More
Skills Learned
Time Series
LSTM
ARIMA
Disease Prediction from Health Records
Healthcare providers increasingly rely on predictive analytics to save lives. In this project, you will use datasets of patient records including lifestyle and medical history. Classification models will predict diseases like diabetes and heart disease. Data preprocessing will include handling missing values and normalization. The project also applies feature selection to remove irrelevant factors. Accuracy and sensitivity will be evaluated to ensure fairness and usability. Such solutions help in preventive healthcare and timely intervention. Know More
Skills Learned
Classification
Healthcare Analytics
Data Preprocessing
Image Classification
Image classification is a popular ML problem in computer vision. In this project, you will build models to classify images into categories like animals, vehicles, or objects. CNN architectures such as VGG and ResNet will be implemented. Data augmentation will be applied to improve generalization. The project also explores transfer learning with pre-trained models. Model evaluation will include confusion matrix and F1-score. Build a prototype system capable of classifying real-world images. Know More
Skills Learned
CNN
Deep Learning
Transfer Learning
Employee Attrition Prediction
High employee turnover is costly for organizations. This project predicts employee attrition using HR datasets with features like salary, promotions, and tenure. Classification algorithms such as Decision Trees and Random Forests will be applied. Data balancing techniques will improve model accuracy. The project includes feature importance analysis to identify main attrition drivers. HR teams can use these insights for workforce planning. The model can be integrated with HR dashboards for decision support. Know More
Skills Learned
HR Analytics
Decision Trees
Workforce Planning
Energy Consumption Forecasting
Predicting electricity usage is essential for energy providers. This project uses time series data from energy grids to forecast consumption. Regression and ARIMA models will be applied for short-term predictions. Weather data will also be incorporated as external features. Deep learning models such as LSTM will handle long-term dependencies. Forecast accuracy will be evaluated using RMSE. Results will help optimize energy production and reduce wastage. Know More
Skills Learned
Forecasting
Time Series
LSTM
Spam Email Classifier
Spam detection is a classic NLP use case. In this project, you will build a spam classifier using email datasets. Text preprocessing techniques such as tokenization, TF-IDF, and word embeddings will be applied. Classification models like Naïve Bayes and SVM will be trained. Deep learning methods like RNNs will also be tested for sequence modeling. Precision and recall will be evaluated to reduce false positives. The solution can be deployed as an email filter. Know More
Skills Learned
NLP
Text Classification
Word Embeddings
Credit Scoring Model
Credit scoring is essential in financial services. This project uses applicant financial and behavioral data to predict creditworthiness. Decision Trees, Random Forests, and Gradient Boosting models will be applied. Feature engineering will enhance prediction capability. Advanced techniques like ensemble stacking will improve performance. The project also applies SHAP analysis for explainability. Final deployment will be as a scoring API for loan systems. Know More
Skills Learned
Decision Trees
Ensemble Learning
Explainable AI
Future-Ready Education for Career Success
Placement-Oriented Training
Accelerate your career with job-focused training designed to build the exact skills employers demand. We prepare you with industry projects, mock interviews, and career guidance.
Certification with Credibility
Earn globally recognized certifications that strengthen your resume and give you a competitive edge in the job market.
Flexible Learning Paths
Learn at your own pace with customized schedules that adapt to your lifestyle. Whether you’re working, studying, or switching careers—our flexible programs fit right in.
Career Growth Support
From resume building to interview prep, our dedicated career support team helps you land your dream role and advance your professional journey.
Lifetime Resource Access
Enjoy unlimited access to premium course materials, toolkits, and recorded sessions, ensuring you always stay ahead—even after completing the program.
Global Networking Opportunities
Join an active community of learners, alumni, and industry professionals worldwide. Build valuable connections and unlock new career opportunities.
Expert-Led Mentorship
Learn directly from seasoned mentors with years of real-world experience. Get personalized guidance, career advice, and feedback to sharpen your skills.
Hands-On Industry Projects
Work on live projects and case studies that simulate real business challenges. Gain practical experience and showcase job-ready skills to future employers.
Learner Support
Dedicated Mentorship
- One-to-one guidance tailored to your learning style
- Academic support with personalized study strategies
- Career navigation with resume and interview prep
- Interactive sessions with industry-experienced mentors
- Continuous motivation to keep you on track
24 x 7 Available
1:1 Expert Guidance
On-Demand Tutoring
- Quick assistance for challenging subjects
- Structured sessions with certified tutors
- Doubt resolution within minutes
- Concept reinforcement through interactive lessons
- Proven track record with top learner ratings
Highly Rated
Smart Learning
Professional Skills Training
- Practical workshops on leadership and teamwork
- Communication and public speaking coaching
- Real-world scenarios to practice problem solving
- Confidence building for interviews and presentations
- Industry-focused approach to workplace readiness
Expert Coaches
Career-Driven
What Can I Become?
Data Scientist
12k+
Job Openings
₹10–18 LPA
Average Salary
Data Scientists analyze large datasets to uncover patterns, build predictive models, and provide actionable insights that drive strategic decisions. They use advanced statistical methods, machine learning, and AI to solve complex business problems.
Machine Learning Engineer
9k+
Job Openings
₹9–16 LPA
Average Salary
ML Engineers design, build, and optimize machine learning models that power AI-driven applications. They work with big data pipelines, deep learning frameworks, and deploy models for real-world business solutions.
Data Analyst
15k+
Job Openings
₹6–10 LPA
Average Salary
Data Analysts collect, clean, and interpret data to provide clear insights for decision-making. They create reports, dashboards, and visualizations to help organizations track performance and identify opportunities.
AI Researcher
2.5k+
Job Openings
₹12–20 LPA
Average Salary
AI Researchers focus on advancing the field of artificial intelligence by developing new algorithms, models, and applications. They work on cutting-edge areas such as deep learning, NLP, and computer vision.
Business Intelligence Analyst
8k+
Job Openings
₹7–12 LPA
Average Salary
BI Analysts transform raw data into meaningful insights, helping businesses optimize performance and uncover growth opportunities.
Deep Learning Specialist
3k+
Job Openings
₹11–18 LPA
Average Salary
Deep Learning Specialists develop and train advanced neural networks for applications such as speech recognition, autonomous systems, and AI vision.
NLP Engineer
4.5k+
Job Openings
₹9–15 LPA
Average Salary
NLP Engineers specialize in language-based AI, building applications like chatbots, translators, and sentiment analysis systems.
Computer Vision Engineer
3.8k+
Job Openings
₹10–17 LPA
Average Salary
Computer Vision Engineers design AI systems that enable machines to analyze and understand images and videos for real-world applications.
Data Engineer
11k+
Job Openings
₹8–14 LPA
Average Salary
Data Engineers build robust pipelines and architectures that allow organizations to collect, process, and store massive volumes of data efficiently.
Generative AI Specialist
4.2k+
Job Openings
₹10–18 LPA
Average Salary
Generative AI Specialists design, build, and deploy solutions using advanced AI models like GPT and diffusion models. They create intelligent systems for content generation, automation, chatbots, and business innovation across industries.
Data Product Manager
4k+
Job Openings
₹12–20 LPA
Average Salary
Data Product Managers bridge business goals and data capabilities, designing AI-driven products and ensuring data solutions deliver measurable business value.
AI Solutions Architect
2k+
Job Openings
₹15–25 LPA
Average Salary
AI Solutions Architects design large-scale AI systems and integrate machine learning models into enterprise environments, aligning AI initiatives with business strategy.
Learner Profiles
We welcome learners from diverse professional and academic backgrounds who aspire to accelerate or transition their careers. Our programs are designed to be inclusive, flexible, and impactful, ensuring success for individuals at every stage of their journey.
Diverse Learners, One Goal: Career Growth
Our programs attract learners from varied academic and professional backgrounds, united by the ambition to upskill, transition, and accelerate their careers.
Master your craft with renowned faculty
Instructors
Shashee Chauhan
Senior Data Science & AI Trainer
Shashee is a competent professional with 10+ years of industrial experience and currently serves as a Senior Data Science & AI Trainer. She is deeply passionate about leveraging Machine Learning and Artificial Intelligence to solve real-world problems. Her expertise spans across building advanced Question Answering Systems, Recommender Systems, and applied AI solutions that drive tangible business outcomes. Read More
Umang Vardhan Tyagi
Data Science & Business Analyst Trainer
Umang Vardhan Tyagi is a seasoned Data Science & Business Analytics Trainer with over 10 years of rich industry and academic experience. Proficient in Python, SQL, and advanced data analysis techniques, he has trained and mentored countless professionals and students in mastering data-driven decision-making. His expertise spans a wide range of machine learning methodologies including supervised and unsupervised learning, regression, clustering, neural networks, as well as Deep Learning, Computer Vision, and Natural Language Processing (NLP). With hands-on experience in transfer learning models and cutting-edge AI applications, Umang combines technical mastery with practical business insights, enabling learners to bridge the gap between theory and real-world problem-solving. Read More
Manas Tandon
Data Science Trainer
Manas Tandon is an experienced Data Science Trainer and Analytics Professional with a strong background in the US Healthcare and Insurance sector, well-versed in HIPAA policies and industry regulations. With expertise in Agile methodologies (JIRA/Confluence), he has successfully led projects by creating FRDs/FRS, design documents, and managing data products as a Product Owner while ensuring seamless client engagement. He brings a diverse professional journey spanning retail, start-up operations, and advanced data analytics, backed by a Post Graduate degree in Business Analytics from The University of Texas at Austin – McCombs School of Business. Combining his industry knowledge with academic rigor, Manas empowers learners to gain practical, business-focused data science skills. Read More
JILU BALAKRISHNAN
Data Science & AI Trainer
Jilu Balakrishnan is a Quantum AI Researcher and Artificial Intelligence Consultant with 15+ years of experience, currently pursuing a Ph.D. in Computer Science specializing in Quantum AI. She is passionate about advancing the fusion of quantum computing and machine learning to solve complex real-world problems. Her expertise spans AI/ML consulting, healthcare AI, LLMs, IoT, and cloud platforms like AWS and GCP. As an educator and mentor, she has empowered hundreds of professionals through cutting-edge AI training and research. Read More
Certification
Upon successful completion of our Post Graduate Certification Program in Data Science and ML, you will receive a prestigious professional certificate along with industry-recognized certifications from IBM and Microsoft — boosting your credibility, enhancing your value, and propelling your career toward accelerated growth.
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