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Post Graduate Certificate Program in Data Science and Machine Learning

Name: Post Graduate Certificate Program in Data Science and Machine Learning
Brand: Post Graduate Certificate Program in Data Science and Machine Learning
SKU: Data_Science_ILM_541033_458385
Price: 100000 INR
Availability: In-stock
Rating: 5 (277 reviews)

Overview

Career Leap Assurance
Manipal ProLearn Career Leap Assurance - 3 networking sessions with companies, 1 capstone project & lifetime alumni privileges.
Joint Certification
Get a joint certificate from MAHE, Manipal ProLearn and Gramener.
Experiential Learning Model
The comprehensive curriculum of our Machine Learning and Data Science training is taught through videos, reading material, projects, & assignments.
Salary Packages
The average pay for a Data Scientist with Machine Learning skills is ₹8.7 lakhs per year.
Online Live Sessions with Experts
Learn from experts with a vast experience in delivering data science training.

Career Leap Assurance

Manipal ProLearn Career Leap Assurance - 3 networking sessions with companies, 1 capstone project & lifetime alumni privileges.

Joint Certification

Get a joint certificate from MAHE, Manipal ProLearn and Gramener.

Experiential Learning Model

The comprehensive curriculum of our Machine Learning and Data Science training is taught through videos, reading material, projects, & assignments.

Salary Packages

The average pay for a Data Scientist with Machine Learning skills is ₹8.7 lakhs per year.

Online Live Sessions with Experts

Learn from experts with a vast experience in delivering data science training.

About Course

Do you want to master the essential skills of Machine Learning and derive insights for making better business decisions? Machine Learning is the science of using computers to run predictive models that learn from existing data in order to forecast future behaviours, outcomes, and trends. And with the global Big Data market booming exponentially, there is a huge demand for skilled data scientists.

Manipal ProLearn’s PG Certificate Program in Data Science and Machine Learning is designed to provide you with a broad understanding of concepts like statistical analysis, exploratory data analysis, data visualization, and machine learning. The Machine Learning and Data Science training will enable you to implement Big Data techniques using tools using R, Excel, Tableau, Hadoop, Pig, Hive, and Spark.

What skills you learn

After completing the Data Science and Machine Learning certification, you’ll be considered as a strong and competent data scientist. The course will help you to:

Perform data analysis, modelling, predictive analysis, and storytelling through data visualization which is crucial to business decision-making.
Apply the methods, tools and techniques by leveraging technologies such as Excel, R, Python, SQL, NoSQL, Hadoop, Pig, Hive, Apache Spark and other open source and proprietary products as well.
Understand and use Big Data technologies as enablers to deploy enterprise information management and solve business problems.

Who Should Attend this course

Engineers and non-engineers interested in Machine Learning

Managers and analysts interested in understanding technical aspects of Machine Learning

Business Intelligence professionals entering data analytics projects

Data Science aspirants

Engineers and non-engineers interested in Machine Learning	Managers and analysts interested in understanding technical aspects of Machine Learning
Business Intelligence professionals entering data analytics projects	Data Science aspirants

Key Features

In Collaboration with Gramener

The Data Science and Machine Learning certification course is designed in collaboration with the industry leader in Data Science - Gramener.

Manipal ProLearn Career Leap Assurance

Get exposure with 3 networking sessions with companies, 1 capstone project & lifetime alumni privileges.

Earn Course Credits

Earn 16 Credits from Manipal Academy of Higher Education from upon completion of the Machine Learning and Data Science certification.

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Course Curriculum

1.1 Introduction to Statistics

Data and information
Classification of data
Measures of central tendency
Measures of dispersion
Visual representation of data

1.2 Probability

Probability
Sample space
Events and their types
Types of Probability
Dependence and Independence
Bayes’ Theorem

1.3 Sampling and Sampling Distributions

Sampling
Various kinds of sampling
Central Limit theorem
Standard error
Confidence intervals

1.4 Testing of Hypothesis

Mann-Whitney-Wilcoxon test
Kruskal-Wallis test
Non-parametric tests
Chi-square test

1.5 Simple Correlation

Correlations
Correlation versus Causation

1.6 Regression

Diagnostics of Linear Regression
Binary Logistic Regression

1.7 Essentials of Exploratory Data Analysis

Introduction to Exploratory Data Analysis(EDA)
Importance of EDA in Data Science domain
Introduction Key EDA techniques

1.8 Foundational Concepts of Business Statistics

Basic concepts in business statistics
Sampling techniques & samples
Frequency distribution and central tendency Variability & shape

1.9 Foundational Concepts of Data Handling

Data for EDA in enterprises
Data types and formats
Data quality
Data analysis types and purpose
Handling missing values in data
Data transformation for EDA

1.10 Data Analysis Techniques

Handling categorical & numerical variables
Visualization in EDA
Dimension reduction
Association analysis
Clustering
Factor analysis & Principal Component Analysis

1.11 Visual Communication Elements

Visual Communication Design
Components of visual communication
Datasets and Graphs
Layout and Formatting
Classification of Visualization

1.12 Visualizing Structured Data and Big Data

Mediums of visualizing structured data –
Dashboards
Scorecards
Infographics
Metrics and KPIs
Visualizing big data

1.13 Storytelling with Data

Data story and infographic design process
Tufte's design principle
Story telling with data
Story development and delivery

2.1 Introduction to AI

Sophia
Alpha Go and the rebirth of AI
Sneak peek into the future
current trends in AI

2.2 Applications of AI

Enterprise Applications of AI-Industries
Consumer Applications - Gaming, Home Automation

2.3 AI, ML and DL

Understanding Artificial intelligence, Machine learning and Deep learning.
Use case driven comparison of AI, ML and DL

2.4 Introduction to ML

What is ML, Need for ML, classification of ML algorithms- supervised and unsupervised learning.
Types of ML algorithms

2.5 ML Techniques

Classification and Regression – understanding classification and regression techniques with case studies

2.6 ML with Scikit-learn

Introduction to Scikit- learn package in python.
Implementing ML algorithms in python using Scikit-learn

2.7 ML Algorithms

Linear, Logistic regression; Decision trees
Support Vector Machines. Python hand on using Scikit-learn

2.8 Training and Deploying models

Creating training models using ML algorithms and deploying the models,
Understand how to deploy models after training

2.9 Text Analytics

Basics of text processing, lexical processing, syntax and semantics of text processing,
other problems in text analytics

2.10 Natural Language Processing

Introduction and applications of NLP.
Learning the business use cases of Natural language processing. Statistical NLP and text similarity,
syntax and parsing techniques, text summarization techniques, semantics and generation

3.1 Introduction to BIG DATA and Hadoop

Introduction
Growth of Digital Data
Challenges of Data Processing
Introduction to Distributed Systems
Data Intensive Computations and Parallelism
History of Hadoop
Hadoop Overview
Ecosystem of Hadoop
HDFS and Map Reduce Paradigm
Job Processing Pipeline
Big Data Technology Landscape
Big Data Archival and Security
Use Cases
Features of Hadoop

3.2 HDFS Architecture

Introduction to HDFS
Cluster View of HDFS
Data Storage in HDFS
Blocks and Splits
Metadata File
Name node demo
HDFS data storage demo
Reliability and Rack Awareness
High Availability
HDFS Federation
Data Replication - Demo
HDFS Client
HDFS Clients - Demo

3.3 HDFS Operations

Introduction
HDFS commands
Basic HDFS commands demo
Read anatomy in HDFS
Write anatomy in HDFS
Additional HDFS commands demo
HDFS permission management
HDFS permission management demo -Part 1
HDFS permission management demo -Part 2

3.4 Architecture of Map-Reduce

Introduction
Traditional approach
Overview of map reduce (MR1)
System architecture of map reduce (MR1)
Introduction to YARN
Map-Reduce job execution in YARN (MR2/Hadoop 2.x)

3.5 Map-Reduce job execution

Introduction
Job flow
Job submission
Job initialization
Job scheduling
Map task execution
Sort and shuffle
Reduce task execution
Job clean-up
Scheduler

3.6 Apache Pig

Introduction
Map Reduce and PIG
Modes of execution in PIG
Pig client
Data types in PIG
Operators in PIG
Pig Usage
Loading data into PIG demo
Pig dialects
Transformations in PIG demo
Debugging in PIG demo
Other capabilities in PIG demo

3.7 Apache Hive

Introduction
Hive vs RDBMS
Hive Architecture
Hive Components
Hive Schema Model
Hive Integration with Hadoop
Hive Query Language
Transformation in Hive
Hive Database Creation - Demo
Hive Tables - Demo
Advanced Hive
Partitioning Demo
Bucketing Demo
Advanced Concepts Demo
Manage an XML or JSON files - Demo
Use a predefined SERDE - Demo
Summary

3.8 Apache Sqoop, Oozie and Hue

Apache Sqoop Introduction
Sqoop Usage
Working with Sqoop - Demo
Advanced Sqoop
Hive Integration - Demo
Hbase Integration - Demo
Sqoop scripts - Demo
Apache Sqoop Summary
Apache Oozie Introduction
Oozie Client
Basic Workflow Setup
Types of Oozie Actions
Control Statements
Defining a Workflow
Run MapReduce with Oozie - Demo
Apache Oozie Summary
Apache Hue Introduction
Hue User Interface
Working with Hive using Hue
Working with Pig using Hue
Monitoring an Oozie Job using Hue
Apache Hue - Demo
Apache Hue Summary

3.9 Apache Hbase

Introduction
Categories of NoSQL Databases
Hbase Evolution
Hbase vs RDBMS
Hbase Architecture
Hbase Components
Column Family
Hbase Fundamentals
Hbase Client
Basic CRUD Operation Demo
Basic CRUD Operation
Zookeeper
Zookeeper Demo
Summary

Frequent Questions we get

Why should I take up a Manipal ProLearn course?

Manipal ProLearn, a part of Manipal Global Education Services, offers a variety of professional certification courses across Technology, Digital Marketing, Data Sciences, Project Management, and Finance domains.

Carrying forward the Manipal legacy of over six decades in education, Manipal ProLearn helps working professionals and students to enhance their skills and fast-track their careers. Manipal ProLearn has partnered with industry leaders like Google, Microsoft, AWS Educate, EY, Sandbox, and Chartered Institute of Management Accountants (CIMA) to provide quality courses that add to your skill set.

We have redefined learning in the professional certification program space with a wide range of course options taught by expert faculty on an award-winning learning platform. Benchmarked against global certification standards of PMI®, CIMA, PMBOK® etc., our course content is designed and developed by industry experts. Not only are these certification courses good for skill enhancement, but with industry-recognized certification, they are also a great value-add to one's resume. Over the last two years, more than 80,000 learners have advanced their careers with the help of our courses.

Do I have to clear any entrance (screening) test before taking up the course?

Currently, none of the courses has an entrance test. But some certifications might have their prerequisites, which are mentioned on the respective course pages.

What type of support can I expect in terms of course material, assessment, evaluation, feedback, discussion forums, mentoring etc.?

Once you have enrolled in a course, you will receive a welcome e-mail and a telephonic call from our customer support team. The features offered as part of the course will be explained clearly by our representative and also will also be listed in the mail correspondence.

Besides, our Customer Support team is available between 8 AM to 8 PM every day to help students on any course related issues.

Will I be able to access the course material post-completion too?

The course material access will be active till the validity of your course that is 12 months from the date of your enrolment.

How will successful completion of the course improve my job prospects?

Manipal ProLearn gives learners an edge when it comes to employment opportunities. By partnering with industry leaders to provide quality courses, we add to your individual skill set and make you industry ready. Also, the constantly expanding list of top-end knowledge partners ensures that learners are exposed to the latest developments and trends across sectors.

What should I do if I face any issues?

In case you come across any issue, please write to us at support@manipalprolearn.com and we will be happy to assist you.

Do you have a refund policy if I am not satisfied with the course and want to withdraw? Is there a timeframe for the same?

All refund requests received by Manipal ProLearn are processed through the same gateway within 14 working days of receiving the request. For any exceptional cases where you have not received the refund amount, please write to us at support@manipalprolearn.com. We shall get back to you with an update on your refund request or valid reasons in case of rejections.

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