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






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

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

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

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

Correlations
Correlation versus Causation

Diagnostics of Linear Regression
Binary Logistic Regression

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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)

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

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

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

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

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

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