Is prior programming experience required?

No. We start from Python fundamentals and progressively move into data analysis, visualization, statistics, and ML. Beginners can follow along with mentor support and hands-on labs.

What is the duration of the program?

The program spans ~95 guided hours, typically completed over 8–10 weeks with live sessions, labs, code reviews, and a capstone project.

Will I get job placement assistance?

Yes. You’ll receive resume and LinkedIn polishing, portfolio review, mock interviews, and curated referrals for relevant ML/Data roles.

What tools and libraries are covered?

Python, NumPy, Pandas, Matplotlib/Seaborn, scikit-learn, Jupyter, and SQL. Where relevant, we demo Plotly, FastAPI, or lightweight deploys.

Do I get a certificate?

Yes. You’ll receive a QR-verified certificate from Cinute Digital Pvt. Ltd. upon successful completion of assessments and the capstone.

Live Online + Classroom95 HoursProject-basedPlacement Support

Master Program in Machine Learning & Data Science with Python

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Become industry-ready with Python, NumPy, Pandas, Data Visualization, scikit-learn, model building, API integration, and cloud deployments. Build portfolio projects and earn a QR-verified certificate.

Curriculum includes EDA best practices, train/validation workflows, metrics, feature engineering, lightweight MLOps (environments, reproducibility), and deployment patterns.

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80% practical labs with mentor feedback
Supervised/unsupervised ML with scikit-learn
Portfolio projects with docs & GitHub
Deployments on cloud + basic MLOps

Hands-On

Expert Faculty

Verified Cert

Placement Help

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★★★★★#1 Mumbai’s Premium Training Institute

Why Invest in Machine Learning & Data Science?

Data is the new oil-and Python is the engine powering AI, analytics, and automation across industries.

Market growth (2020–2030)

Job Vacancies in India

0 LPA

Data Science freshers’ average salary

Job Satisfaction

India’s share in the global market

Growing adoption across FinTech, Healthcare, Retail, and SaaS is accelerating demand for Python-first ML engineers, data analysts, and MLOps practitioners.

*Figures are sourced from the program brochure and are indicative; they may vary by role, skills, and location.

Advanced Data Science: A Complete Overview

A mentor-led, job-ready journey through Python, Data Visualization, Statistics & Probability, and Machine Learning — built around hands-on labs and real portfolio projects.

95 Hours4 CertificatesHands-on ProjectsNo Prerequisites100% Job-Ready

This Hero Program blends theory with practice so you can confidently analyze data, build models, and ship real demos. You’ll learn to structure datasets, visualize insights, apply statistical reasoning, and implement machine learning algorithms in Python. Each module ends with a guided mini-project and code review to ensure you’re job-ready for data-driven roles.

What You’ll Learn

Python foundations → EDA → feature engineering
Visualizations with Matplotlib & Plotly
Statistics & Probability for inference and A/B testing
ML with scikit-learn: supervised & unsupervised
Model evaluation, metrics, and fair comparison
Lightweight MLOps: environments, reproducibility, deploy

Career Paths

Data Analyst • ML Engineer • Data Scientist

Toolchain

Python • NumPy • Pandas • scikit-learn

Deliverables

3+ portfolio projects with docs & demos

Includes mentor guidance, interview prep, resume & LinkedIn polishing, and placement assistance for relevant roles.

*Outcomes depend on practice, project quality, and interview performance.

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12-Core Curriculum Modules

Industry-aligned syllabus spanning Python, NumPy/pandas, Matplotlib/Seaborn, Statistics, and Machine Learning with rigorous EDA and validation.

01
Python Programming Fundamentals
- History & setup (Jupyter), syntax, variables, data types, operators, input/print, strings, and clean coding foundations.
Outcomes: hands-on labs, assessment checklists, and take-home exercises for mastery.
02
Control Flow & Core Data Structures
- If/elif/else, logical ops, loops; lists, tuples, sets, dictionaries with indexing/slicing, CRUD, and common methods.
Outcomes: hands-on labs, assessment checklists, and take-home exercises for mastery.
03
Functions, Recursion, File I/O & Modules
- Defining/calling functions (args, scope), recursion, exception handling, open/read/write files, stdlib & custom modules.
Outcomes: hands-on labs, assessment checklists, and take-home exercises for mastery.
04
Object-Oriented Programming in Python
- Classes/objects, instance/class vars & methods, inheritance (single/multiple), polymorphism, encapsulation, properties.
Outcomes: hands-on labs, assessment checklists, and take-home exercises for mastery.
05
NumPy for Scientific Computing
- Arrays vs lists, creation (1D/2D/nD), indexing/slicing, reshaping/broadcasting, random, math/linear algebra utilities.
Outcomes: hands-on labs, assessment checklists, and take-home exercises for mastery.
06
Data Analysis with pandas & EDA
- Series/DataFrame, import/export (CSV/Excel/JSON), cleaning (missing/dupes), filter/sort, groupby/merge/pivot, EDA.
Outcomes: hands-on labs, assessment checklists, and take-home exercises for mastery.
07
Data Visualization with Matplotlib
- Figure/Axes, line/bar/pie, hist/box/heatmap/3D, labels/legends/annotations, simple animations & interactive widgets.
Outcomes: hands-on labs, assessment checklists, and take-home exercises for mastery.
08
Advanced Visualization with Seaborn
- Themes/palettes, dist/KDE/rug, pair/joint plots, categorical (strip/box/violin/swarm), heatmaps/clustermaps, FacetGrid.
Outcomes: hands-on labs, assessment checklists, and take-home exercises for mastery.
09
Statistics & Probability Essentials
- Descriptive stats, hist/box plots, probability rules & Bayes, discrete/continuous distributions (binomial, normal, etc.).
Outcomes: hands-on labs, assessment checklists, and take-home exercises for mastery.
10
Sampling, Hypothesis Tests & Regression
- Sampling & CLT, confidence intervals, z/t/chi-square/ANOVA, correlation (Pearson/Spearman), simple linear regression.
Outcomes: hands-on labs, assessment checklists, and take-home exercises for mastery.
11
ML Foundations & Data Preprocessing
- Supervised vs unsupervised, ML workflow; missing handling, scaling, encoding, train/val/test split; quick model demo.
Outcomes: hands-on labs, assessment checklists, and take-home exercises for mastery.
12
Core ML Algorithms & Model Validation
- Regression (linear/poly, ridge/lasso); classification (logistic, KNN, trees, RF, SVM); CV, metrics (MSE, R², AUC, F1).
Outcomes: hands-on labs, assessment checklists, and take-home exercises for mastery.

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Tools & Technologies You'll Master

Master the essential Python data stack for analytics and machine learning — from data wrangling to model training and visualization.

Python Data StackEDA & VisualizationModeling & MetricsNotebook-first Workflow

Python
Primary language for ML & data workflows
Pandas
Data wrangling, cleaning & feature prep
NumPy
Fast arrays, math & vectorized operations
Scikit-Learn
Core ML algorithms, pipelines & metrics
Matplotlib
Publication-ready charts & plots
Seaborn
Statistical visuals & quick EDA
Jupyter Notebook
Interactive notebooks for experiments
SQL
Query, join & aggregate data at source

See Modules Using These Tools

*Tooling may be extended with Plotly, FastAPI, Docker, or cloud notebooks based on cohort needs.

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Your Data Science Career Roadmap

Follow these 4 proven steps to move from learner to job-ready Data Science professional with a portfolio recruiters trust.

Program Duration

~ 10–14 Weeks

95+ hours guided learning

Portfolio Projects

Deployed & documented

Target CTC

₹8–15 LPA

Role & location vary

1
Job-Ready Foundations
Complete the 95-Hour Data Science Hero Program
Master Python, statistics, NumPy, Pandas, data wrangling, SQL, EDA, and practical visualization for business insights.
2
Portfolio & GitHub
Build 3 Portfolio Projects
Ship an analytics dashboard, a time-series forecasting model, and a classification/NLP project. Document on GitHub with READMEs and CI checks.
3
Cloud & MLOps
Deploy & Operationalize (MLOps)
Serve models via FastAPI, containerize with Docker, and deploy to cloud (AWS/GCP). Add monitoring, metrics, and API docs.
4
Offer & Onboarding
Career Prep & Land a Data Role
Resume revamp, ATS keywords, mock interviews, case studies, and SQL/ML refresher. Target ₹8–15 LPA roles (Data Analyst, ML Engineer, Data Scientist).

Learn from anywhere. Your journey to a Data Science career starts here.

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Real-World Projects You’ll Build

Build a portfolio recruiters love: hands-on Python, Data Science, and Machine Learning projects with clean code, clear storytelling, and business impact. Each project is production-minded and interview-ready.

# Data Analysis Dashboard (Executive Insights)

Transform messy business data into interactive dashboards that reveal KPIs, trends, and anomalies. Use Python to clean, join, and visualize data for decision-makers.

Design KPI scorecards and cohort views
Build drill-downs for product, region, and channel
Ship a shareable dashboard and executive summary

PythonPandasPlotlyFastAPISQL

Job-Ready • Portfolio-Ready

# Sales Forecasting Model (Time-Series ML)

Engineer a robust forecasting pipeline using classical and ML approaches to predict weekly sales with confidence intervals and scenario analysis.

Feature engineering for seasonality & promotions
Compare ARIMA/Prophet vs. ML regressors
Backtesting & error analysis (MAE, MAPE, SMAPE)

scikit-learnProphetNumPyMatplotlib

Job-Ready • Portfolio-Ready

# Customer Segmentation (Clustering & RFM)

Segment customers with unsupervised learning to personalize campaigns and improve retention using explainable, data-driven cohorts.

RFM scoring + K-Means/DBSCAN comparison
Silhouette evaluation & interpretability
Actionable cohort playbooks for marketing

scikit-learnPandasSeabornUmap-learn

Job-Ready • Portfolio-Ready

# Churn Prediction (Classification Pipeline)

Predict user churn with an end-to-end ML pipeline—balanced training, model selection, and business-friendly, explainable results.

Data imbalance handling (SMOTE/weights)
ROC-AUC, PR-AUC, and calibration reporting
SHAP-based explanations & retention levers

scikit-learnXGBoostSHAPMLflow

Job-Ready • Portfolio-Ready

# NLP Insights (Reviews & Sentiment)

Mine product reviews to extract topics, sentiment, and pain points. Turn qualitative text into quantifiable product insights.

Text cleaning & embeddings
Topic modeling with coherence checks
Aspect-based sentiment & insight report

spaCyNLTKSentence-TransformersUMAP

Job-Ready • Portfolio-Ready

# End-to-End AI App (Data → Model → API → UI)

Ship a production-ready mini-application: automated data flows, trained model, REST API, and a lightweight UI with auth and monitoring.

Orchestrate pipelines & model registry
Serve predictions via FastAPI
Observability & CI/CD with tests

FastAPIDockerPyTestGitHub ActionsVercel

Job-Ready • Portfolio-Ready

These industry-aligned data projects reflect real metrics, clean engineering practices, and clear communication—exactly what hiring managers look for in Data Analysts, ML Engineers, and Data Scientists.

*Outcomes and tools may vary by dataset, domain, and role.

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Top Companies Hiring Data Science Professionals

•101,000+ Job Vacancies in India•Pan-India • Product & Services • Startup & Enterprise

Join the ranks of Data Scientists, ML Engineers, and Analytics Professionals at India’s leading product and services companies.

TCS
Infosys
Wipro
Cognizant
Accenture
Capgemini
HCLTech
IBM

Data ScientistML EngineerData AnalystMLOps EngineerBI Analyst

*Logos are for illustration of hiring potential. Roles and openings vary by location, skills, and experience.

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Who Is This Course For

Whether you’re starting out or leveling up, this program turns Python, Data Science, and Machine Learning skills into measurable career outcomes—portfolio projects, practical tooling, and interview-ready confidence.

# Beginners & Non-Coders

Start your Data Science journey from zero—learn Python, statistics, and analytics step by step with hands-on practice.

No prior coding required
Guided projects and mentor feedback

Beginner-Friendly • Job-Ready

# Working Professionals

Upskill with Python-first ML to boost roles in technology, finance, healthcare, marketing, and education.

Career-oriented case studies
Practical tools: Pandas, SQL, scikit-learn

Beginner-Friendly • Job-Ready

# Intermediate Learners

Go deeper into machine learning, model evaluation, and MLOps to deliver production-ready solutions.

End-to-end ML pipelines
Model tuning, SHAP, experiment tracking

Beginner-Friendly • Job-Ready

# Students & Career Switchers

Build a job-ready portfolio and transition into high-growth roles like Data Analyst, ML Engineer, or Data Scientist.

Portfolio projects recruiters love
Interview prep & placement guidance

Beginner-Friendly • Job-Ready

Ideal for Python beginners, working professionals, intermediate learners, and career switchers seeking Data Analyst, ML Engineer, or Data Scientist roles.

*Learning paths adapt by background and pace.

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Frequently Asked Questions

Everything you need to know about our mentor-led Machine Learning & Data Science program.

Is prior programming experience required?
No. We start from Python fundamentals and progressively move into data analysis, visualization, statistics, and ML. Beginners can follow along with mentor support and hands-on labs.
What is the duration of the program?
The program spans ~95 guided hours, typically completed over 8–10 weeks with live sessions, labs, code reviews, and a capstone project.
Will I get job placement assistance?
Yes. You’ll receive resume and LinkedIn polishing, portfolio review, mock interviews, and curated referrals for relevant ML/Data roles.
What tools and libraries are covered?
Python, NumPy, Pandas, Matplotlib/Seaborn, scikit-learn, Jupyter, and SQL. Where relevant, we demo Plotly, FastAPI, or lightweight deploys.
Do I get a certificate?
Yes. You’ll receive a QR-verified certificate from Cinute Digital Pvt. Ltd. upon successful completion of assessments and the capstone.

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Master Program in Machine Learning & Data Science with Python

Request a Callback

Request a Callback

Advanced Data Science: A Complete Overview

What You’ll Learn

12-Core Curriculum Modules

Python Programming Fundamentals

Control Flow & Core Data Structures

Functions, Recursion, File I/O & Modules

Object-Oriented Programming in Python

NumPy for Scientific Computing

Data Analysis with pandas & EDA

Data Visualization with Matplotlib

Advanced Visualization with Seaborn

Statistics & Probability Essentials

Sampling, Hypothesis Tests & Regression

ML Foundations & Data Preprocessing

Core ML Algorithms & Model Validation

Python

Pandas

NumPy

Scikit-Learn

Matplotlib

Seaborn

Jupyter Notebook

SQL

Complete the 95-Hour Data Science Hero Program

Build 3 Portfolio Projects

Deploy & Operationalize (MLOps)

Career Prep & Land a Data Role

# Data Analysis Dashboard (Executive Insights)

# Sales Forecasting Model (Time-Series ML)

# Customer Segmentation (Clustering & RFM)

# Churn Prediction (Classification Pipeline)

# NLP Insights (Reviews & Sentiment)

# End-to-End AI App (Data → Model → API → UI)

Top Companies Hiring Data Science Professionals

# Beginners & Non-Coders

# Working Professionals

# Intermediate Learners

# Students & Career Switchers

Advanced Data Science: A Complete Overview

What You’ll Learn

12-Core Curriculum Modules

Python Programming Fundamentals

Control Flow & Core Data Structures

Functions, Recursion, File I/O & Modules

Object-Oriented Programming in Python

NumPy for Scientific Computing

Data Analysis with pandas & EDA

Data Visualization with Matplotlib

Advanced Visualization with Seaborn

Statistics & Probability Essentials

Sampling, Hypothesis Tests & Regression

ML Foundations & Data Preprocessing

Core ML Algorithms & Model Validation

Python

Pandas

NumPy

Scikit-Learn

Matplotlib

Seaborn

Jupyter Notebook

SQL

Complete the 95-Hour Data Science Hero Program

Build 3 Portfolio Projects

Deploy & Operationalize (MLOps)

Career Prep & Land a Data Role

# Data Analysis Dashboard (Executive Insights)

# Sales Forecasting Model (Time-Series ML)

# Customer Segmentation (Clustering & RFM)

# Churn Prediction (Classification Pipeline)

# NLP Insights (Reviews & Sentiment)

# End-to-End AI App (Data → Model → API → UI)

Top Companies Hiring Data Science Professionals

# Beginners & Non-Coders

# Working Professionals

# Intermediate Learners

# Students & Career Switchers