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All Levels
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24 Weeks
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MIT Certification
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Industry Immersion
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Capstone Projects
Overview
Our Post Graduation in Data Science in Thane combines data science with artificial intelligence and machine learning. Learn to manage and analyze data, apply machine learning algorithms, and explore AI techniques. Through hands-on projects and case studies, gain the expertise for high-demand AI and data science roles.
- Data Scientist
- Machine Learning Engineer
- AI Engineer
- Data Engineer
- NLP Engineer
- RPA Developer
Targeted Job
Roles
Training and Methodology
When you register for this course, you receive access to -
Integrated Learning: - Blend of data science, AI, and ML. - Hands-On Projects - Real-world case studies and practical tasks.
- Expert Instruction - Learn from experienced industry professionals.
Why Choose This
Course?
Transform your career with our Post Graduation in Data Science in Thane. This program dives deep into data science, AI, and advanced machine learning, equipping you with the skills needed for leadership roles. Gain hands-on experience and industry insights to drive innovation in data science and AI.
Register Now-
100% Placement Assistance Program
Secure your dream job with personalized placement assistance.
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Real time projects
Build essential skills through practical, real-world experience.
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Reviews and Feedback
Assess your progress with regular reviews and detailed feedback.
Gain essential skills with PG in Data Science in Thane
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Learn advanced techniques in data manipulation and analysis
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Gain deep expertise in machine learning algorithms and models
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Become proficient in AI technologies and applications
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Acquire hands-on experience through real-world projects and case studies
Tools & Languages Included In PG in Data Science Course
Ultimate Syllabus for PG in Data Science Course
Develop Key Skills in One Complete Course
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Overview of Data Science
- Data Science Fundamentals
- Data Manipulation and Analysis
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Python Programming
- Python Installation and Basics
- Syntax and programming Structures
- Variables, Operators, Keywords, Expressions
- Decision Making: if, elif, else
- Loops: while, for, break, continue, pass
- List, Tuple, Dictionary, Set
- Functions, Modules
- Object Oriented Programming
- Exception handling
- File handling
- Web scrapping and regular expression (RegEx)g
- CASE STUDY -: IMDB TOP 250 MOVIE DATA WEB SCRAPPIN
- Libraries for data manipulation and data visualization
- Introduction to numpy and its functions
- Introduction to pandas and its functions
- Introduction to matplotlib and seaborn for data visualization
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Machine Learning
- Introduction to Machine Learning
- What is Machine Learning
- Applications of Machine Learning
- Supervised Vs Unsupervised Machine Learning
- Regression vs classification
- Exploratory Data Analysis (EDA)
- Finding null values
- Detecting and removal of outliers
- Feature scaling – : Standardization and normalization
- Introduction to Linear Regression
- What is regression?
- What is linear regression?
- Building First ML model for marks prediction
- Simple linear regression
- Multiple Regression
- Polynomial Regression
- Error functions in Regression (MAE, MSE, RMSE)
- Calculating accuracy using R2Score
- CASE STUDY -: Car Price Prediction on cars24 dataset
- Introduction to Overfitting and underfitting
- Overfitting Vs underfitting
- Bias-Variance Tradeoff
- Regularization Techniques -: Ridge and Lasso
- Understanding and demonstrating Ridge and lasso regression techniques
- Cross Validation Techniques
- Introduction to Logistic Regression
- Sigmoid function
- Understanding parameters of logistic regression
- ROC AUC Curve
- Confusion Matrix -: Precision, Recall, accuracy, f1 Score
- Introduction to KNN
- Understanding working of K – Nearest Neighbors
- Advantages and drawbacks of using KNN
- KNN for regression
- Introduction to SVM
- Understanding Support Vector Machine
- Hard and soft margin
- Understanding Support Vectors , Hyperplane
- Kernel technique
- SVM for regression
- Naive Bayes Classifier
- Understanding Naive Bayes Theorem
- Introduction to text classification
- NLP pipeline
- Vectorization of text data
- Case Study -: Spam mail classification using naive bayes
- Understanding Support Vector Machine
- Hard and soft margin
- Understanding Support Vectors, Hyperplane
- Kernel technique
- SVM for regression
- Decision Tree classifier
- Working of DT
- Gini Index and Entropy
- Pruning techniques
- Advantages and disadvantages of Decision Tree
- Decision Tree for regression
- Introduction to Ensemble learning
- What is Bagging?
- Random Forest Classifier
- ADA Boost, XGboost, Gradient Boost
- Unsupervised Machine Learning Algorithm
- Project deployment using Flask Framework
- Clustering
- K-means Clustering
- Hierarchical clustering
- Association rules
- PCA (principle component analysis)
- CASE STUDY ON BREAST CANCER DETECTION USING CLASSIFICATION ALGORITHMS
- CASE STUDY ON FRAUD DETECTION USING CLASSIFICATION ALGORITHMS
- Introduction to Machine Learning
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Artificial Intelligence
- AI Concepts and Techniques
- Neural Networks and Deep Learning
- AI Applications and Tools
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Deep Learning
- Artificial Neural Network (ANN)
- What is Deep Learning
- DL vs ML
- Forward and backward propagation
- Activation functions
- Optimizer
- Early stopping and dropout layer to handle overfitting
- CASE STUDY – DIGIT CLASSIFICATION USING ANN
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Computer Vision
- Image pre-processing
- Detecting edges
- Understanding Convolutional layer and pooling layer
- Image classification using CNN
- Image Augmentation
- Reading text data from an image.
- Case Study -: Hand gesture volume controller using MediaPipe
- Case Study -: AI Exercise counter using MediaPipe
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Course Project
- Comprehensive Data Science and AI Project
Want to
experience
excellence?
Kickstart your career with Milestone's all-in-one educational solutions in Thane. Book your free demo session today and take the first step toward achieving your success.
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Recruiters looking for Post Graduation in Data Science Students
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What Our Students Say
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Faculty offering personal attention to struggling students is more satisfying compared to other institutes.
Rahul Agawane
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I'm very thankful to MIT staff for their cooperative support throughout the entire course.
Mohit Talar
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There are many demanding software programs, and in my experience, MIT is one of the best choice.
Akshay Kadam
Frequently Asked Questions
Discover all the essential details about our Post Graduation in Data Science in Thane. Explore various topics and find the course that best aligns with your interests and career goals. We're here to guide you in choosing the perfect path for your educational journey.
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Who can enroll in the PG in Data Science Course in Thane?
Graduates from any stream, especially those with IT, Engineering, or Math backgrounds, can enroll. No prior coding experience is required.
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What will I learn in this PG in Data Science Course in Thane?
You will learn Python, Data Analysis, Machine Learning, Artificial Intelligence, SQL, Power BI, and key tools used in data science.
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Will I get to work on real projects during the course?
Yes, you will work on real-time projects and case studies to build practical skills and a strong portfolio.
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Will I receive a certificate after completing the course?
Yes, you will get a Post Graduation certificate from Milestone Institute of Technology after successful completion.
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Do you provide placement assistance after the course?
Yes, we offer full placement support to help you get job opportunities in data science, AI, and machine learning fields.