Post Graduate Program in Data Science
4.5 /5- Offered bySimplilearn
- Private Institute
- Estd. 2010
Post Graduate Program in Data Science at Simplilearn Overview
Duration | 12 months |
Total fee | ₹2.15 Lakh |
Mode of learning | Online |
Difficulty level | Intermediate |
Credential | Certificate |
Post Graduate Program in Data Science at Simplilearn Highlights
- Ranked #1 Data Science certification program by Economic Times
- Purdue Post Graduation Program certification and Alumni Association membership
- 3 Capstones and 25+ Projects with Industry datasets from Amazon, UBER, Comcast etc.
- Exclusive hackathons and Ask me Anything sessions by IBM
- 8X higher live interaction with 200+ hours of live online classes by industry experts
- Simplilearn's JobAssist helps you get noticed by top hiring companies
- Master Classes delivered by Purdue faculty and IBM experts
Post Graduate Program in Data Science at Simplilearn Course details
- 2+ years of work experience preferred
- A bachelor's degree with an average of 50% or higher marks
- Basic understanding of programming concepts and mathematics
- Gain an in-depth understanding of data structure and data manipulation
- Understand and use linear & non-linear regression models. Also learn classification techniques for data analysis
- Obtain a comprehensive knowledge of supervised and unsupervised learning models such as linear regression, logistic regression, clustering, dimensionality reduction, K-NN and pipeline
- Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO, and Weave
- Gain expertise in mathematical computing using the NumPy and Scikit-Learn package
- Master the concepts recommendation engine, time series modeling, gain practical mastery over principles, algorithms, and applications of Machine.
- Learn to analyze data using Tableau and become proficient in building interactive dashboards
- Understand deep reinforcement learning techniques applied in Natural Language Processing
- Understand the different components of the Hadoop ecosystem.
- Learn to work with HBase- its architecture and data storage. Also learn the difference between HBase and RDBMS, and use Hive and Impala for partitioning
- Understand MapReduce and its characteristics, plus learn how to ingest data using Sqoop and Flume
- Accelerate your career with this acclaimed Post Graduate Program in Data Science, in partnership with Purdue University and in collaboration with IBM. Featuring the perfect mix of theory, case studies and extensive hands-on practicum.
- Designed to give recent graduates and experienced professionals an extensive Data Science education. This Post Graduate Program is a blend of online self-paced videos, live virtual classes, hands-on projects, and labs, with mentorship sessions to provide a high-engagement learning experience and real-world applications to help you master essential Data Science skills. This program offers in-depth exposure to technologies including R, Python, Machine Learning, Tableau, Natural Language Processing, and prepares you for an exciting career in Data Science.
Post Graduate Program in Data Science at Simplilearn Curriculum
Programming Refresher
Lesson 01 - Course Introduction
Lesson 02 - Programming Basics
Statistics Essential for Data Science
Lesson 01 - Introduction
Lesson 02 - Sample or Population Data?
Lesson 03 - The Fundamentals of Descriptive Statistics
Lesson 04 - Measures of Central Tendency, Asymmetry, and Variability
Lesson 05 - Practical Example: Descriptive Statistics
Lesson 06 - Distributions
Lesson 07 - Estimators and Estimates
Lesson 08 - Confidence Intervals: Advanced Topics
Lesson 09 - Practical Example: Inferential Statistics
Lesson 10 - Hypothesis Testing: Introduction
Lesson 11 - Hypothesis Testing: Let?s Start Testing!
Lesson 12 - Practical Example: Hypothesis Testing
Lesson 13 - The Fundamentals of Regression Analysis
Lesson 14 - Subtleties of Regression Analysis
Lesson 15 - Assumptions for Linear Regression Analysis
Lesson 16 - Dealing with Categorical Data
Lesson 17 - Practical Example: Regression Analysis
R Programming for Data Science
Lesson 01 - R Basics
Lesson 02 - Data Structures in R
Lesson 03 - R Programming Fundamentals
Lesson 04 - Working with Data in R
Lesson 05 - Stings and Dates in R
Data Science with R
Lesson 01 - Introduction to Business Analytics
Lesson 02 - Introduction to R Programming
Lesson 03 - Data Structures
Lesson 04 - Data Visualization
Lesson 05 - Statistics for Data Science I
Lesson 06 - Statistics for Data Science II
Lesson 07 - Regression Analysis
Lesson 08 - Classification
Lesson 09 - Clustering
Lesson 10 - Association
Python for Data Science
Lesson 01 - Python Basics
Lesson 02 - Python Data Structures
Lesson 03 - Python Programming Fundamentals
Lesson 04 - Working with Data in Python
Lesson 05 - Working with NumPy Arrays
Data Science with Python
Lesson 01 - Data Science Overview
Lesson 02 - Data Analytics Overview
Lesson 03 - Statistical Analysis and Business Applications
Lesson 04 - Python Environment Setup and Essentials
Lesson 05 - Mathematical Computing with Python (NumPy)
Lesson 06 - Scientific Computing with Python (Scipy)
Lesson 07 - Data Manipulation with Pandas
Lesson 08 - Machine Learning with Scikit?Learn
Lesson 09 - Natural Language Processing with Scikit Learn
Lesson 10 - Data Visualization in Python using Matplotlib
Lesson 11 - Web Scraping with BeautifulSoup
Lesson 12 - Python Integration with Hadoop MapReduce and Spark
Machine Learning
Lesson 01 - Introduction to Artificial Intelligence and Machine Learning
Lesson 02 - Data Wrangling and Manipulation
Lesson 03 - Supervised Learning
Lesson 04 - Feature Engineering
Lesson 05 - Supervised Learning Classification
Lesson 06 - Unsupervised Learning
Lesson 07 - Time Series Modeling
Lesson 08 - Ensemble Learning
Lesson 09 - Recommender Systems
Lesson 10 - Text Mining
Tableau
Lesson 01 - Getting Started with Tableau
Lesson 02 - Core Tableau in Topics
Lesson 03 - Creating Charts in Tableau
Lesson 04 - Working with Metadata
Lesson 05 - Filters in Tableau
Lesson 06 - Applying Analytics to the worksheet
Lesson 07 - Dashboard in Tableau
Lesson 08 - Modifications to Data Connections
Lesson 09 - Introduction to Level of Details in Tableau (LODS)
Natural Language Processing
Lesson 01 - Introduction to Natural Language Processing
Lesson 02 - Feature Engineering on Text Data
Lesson 03 - Natural Language Understanding Techniques
Lesson 04 - Natural Language Generation
Lesson 05 - Natural Language Processing Libraries
Lesson 06 - Natural Language Processing with Machine Learning and Deep Learning
Lesson 07 - Speech Recognition Technique
Post Graduate Program in Data Science at Simplilearn Faculty details
Post Graduate Program in Data Science at Simplilearn Entry Requirements
- A bachelor?s degree with a 50% or higher marks
- Professionals with 2+ years of experience are preferred
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