IBM - Data Science Master's Program
4.7 /5- Offered byIntellipaat
Data Science Master's Program at Intellipaat Overview
Duration | 232 hours |
Start from | Start Now |
Total fee | ₹62,643 |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Go to Website  |
Course Level | PG Degree |
Data Science Master's Program at Intellipaat Highlights
- Earn a certificate after completion of course
- 24 x 7 Lifetime Support & Access
- Fee can be paid in installments
Data Science Master's Program at Intellipaat Course details
Data Scientists, Machine Learning Professionals, and Software Developers
Business Intelligence Professionals, Information Architects, and Project Managers
Those looking to be a Data Science Architect
MapReduce and HDFS
Real-time analytics with Spark
Data scientist roles and responsibilities
Prediction and analysis through clustering
Deploying the recommender system
Linear and logistic regression
Making sense of NoSQL data
Deep learning model in AI
The Master’s in Data Science India program is a structured learning path specially designed by industry experts which ensures that you transform into a data science expert
It offers in-depth knowledge of data science, real-time analytics, statistical computing, SQL, parsing machine-generated data, and deep learning
Class Schedule
Data Science Master's Program at Intellipaat Curriculum
Python for Data Science
Module 01 – Introduction to Data Science using Python
Module 02 – Python basic constructs
Module 03 – Maths for DS-Statistics and Probability
Module 04 – OOPs in Python
Module 05 – NumPy for mathematical computing
Module 06 – SciPy for scientific computing
Module 07 – Data manipulation
Module 08 – Data visualization with Matplotlib
Module 09 – Machine Learning using Python
Module 10 – Supervised learning
Machine Learning
Module 01 – Introduction to Machine Learning
Module 02 – Supervised Learning and Linear Regression
Module 03 – Classification and Logistic Regression
Module 04 – Decision Tree and Random Forest
Module 05 – Naïve Bayes and Support Vector Machine
Module 06 – Unsupervised Learning
Module 07 – Natural Language Processing and Text Mining
Module 08 – Introduction to Deep Learning
Module 09 – Time Series Analysis
AI and Deep Learning
Module 01 – Introduction to Deep Learning and Neural Networks
Module 02 – Multi-layered Neural Networks
Module 03 – Artificial Neural Networks and Various Methods
Module 04 – Deep Learning Libraries
Module 05 – Keras API
Module 06 – TFLearn API for TensorFlow
Module 07 – DNNS (deep neural networks)
Module 08 – CNNS (convolutional neural networks)
Module 09 – RNNS (recurrent neural networks)
Module 10 – GPU in deep learning
Data Visualization with Power BI
Module 01 – Introduction to Power BI
Module 02 – Data Extraction
Module 03 – Data Transformation – Shaping and Combining Data
Module 04 – Data Modelling and DAX
Module 05 – Data Visualization with analytics
Module 06 – Power BI Service (Cloud), Q&A, and Data Insights
Module 07 – Power BI Settings, Administration & Direct Connectivity
Module 08 – Embedded Power BI with API & Power BI
Module 09 – Power BI Advance & Power BI Premium
Data Science with R
Module 01 – Introduction to Data Science with R
Module 02 – Data Exploration
Module 03 – Data Manipulation
Module 04 – Data Visualization
Module 05 – Introduction to Statistics
Module 06 – Machine Learning
Module 07 – Logistic Regression
Module 08 – Decision Trees and Random Forest
Module 09 – Unsupervised Learning
Module 10 – Association Rule Mining and Recommendation Engines
Advanced Excel
Module 01 – Entering Data
Module 02 – Referencing in Formulas
Module 03 – Name Range
Module 04 – Understanding Logical Functions
Module 05 – Getting started with Conditional Formatting
Module 06 – Advanced-level Validation
Module 07 – Important Formulas in Excel
Module 08 – Working with Dynamic table
Module 09 – Data Sorting
Module 10 – Data Filtering
MongoDB
Module 01 – Introduction to NoSQL and MongoDB
Module 02 – MongoDB Installation
Module 03 – Importance of NoSQL
Module 04 – CRUD Operations
Module 05 – Data Modeling and Schema Design
Module 06 – Data Management and Administration
Module 07 – Data Indexing and Aggregation
Module 08 – MongoDB Security
Module 09 – Working with Unstructured Data
MS-SQL
Module 01 – Introduction to SQL
Module 02 – Database Normalization and Entity Relationship Model
Module 03 – SQL Operators
Module 04 – Working with SQL: Join, Tables, and Variables
Module 05 – Deep Dive into SQL Functions
Module 06 – Working with Subqueries
Module 07 – SQL Views, Functions, and Stored Procedures
Module 08 – Deep Dive into User-defined Functions
Module 09 – SQL Optimization and Performance
Module 10 – Advanced Topics
Data Science Master's Program at Intellipaat Entry Requirements
- N/A
A candidate must be graduate from a recognised Institute/University
- Not mentioned
Data Science Master's Program at Intellipaat Admission Process
Important Dates
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