- Tools covered: Excel, R,
Python & more
- 104+ hours
of live webinar
Post Graduate Certificate Program in Data Science and Machine Learning
Career Leap AssuranceManipal ProLearn Career Leap Assurance - 3 networking sessions with companies, 1 capstone project & lifetime alumni privileges.
Joint CertificationGet a joint certificate from MAHE, Manipal ProLearn and Gramener.
Experiential Learning ModelThe comprehensive curriculum of our Machine Learning and Data Science training is taught through videos, reading material, projects, & assignments.
Salary PackagesThe average pay for a Data Scientist with Machine Learning skills is ₹8.7 lakhs per year.
Online Live Sessions with ExpertsLearn from experts with a vast experience in delivering data science training.
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.
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.
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
Data and information
Classification of data
Measures of central tendency
Measures of dispersion
Visual representation of data
Events and their types
Types of Probability
Dependence and Independence
Various kinds of sampling
Central Limit theorem
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 analysis types and purpose
Handling missing values in data
Data transformation for EDA
Handling categorical & numerical variables
Visualization in EDA
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 –
Metrics and KPIs
Visualizing big data
Data story and infographic design process
Tufte's design principle
Story telling with data
Story development and delivery
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
Growth of Digital Data
Challenges of Data Processing
Introduction to Distributed Systems
Data Intensive Computations and Parallelism
History of Hadoop
Ecosystem of Hadoop
HDFS and Map Reduce Paradigm
Job Processing Pipeline
Big Data Technology Landscape
Big Data Archival and Security
Features of Hadoop
Introduction to HDFS
Cluster View of HDFS
Data Storage in HDFS
Blocks and Splits
Name node demo
HDFS data storage demo
Reliability and Rack Awareness
Data Replication - Demo
HDFS Clients - Demo
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
Overview of map reduce (MR1)
System architecture of map reduce (MR1)
Introduction to YARN
Map-Reduce job execution in YARN (MR2/Hadoop 2.x)
Map task execution
Sort and shuffle
Reduce task execution
Map Reduce and PIG
Modes of execution in PIG
Data types in PIG
Operators in PIG
Loading data into PIG demo
Transformations in PIG demo
Debugging in PIG demo
Other capabilities in PIG demo
Hive vs RDBMS
Hive Schema Model
Hive Integration with Hadoop
Hive Query Language
Transformation in Hive
Hive Database Creation - Demo
Hive Tables - Demo
Advanced Concepts Demo
Manage an XML or JSON files - Demo
Use a predefined SERDE - Demo
Apache Sqoop Introduction
Working with Sqoop - Demo
Hive Integration - Demo
Hbase Integration - Demo
Sqoop scripts - Demo
Apache Sqoop Summary
Apache Oozie Introduction
Basic Workflow Setup
Types of Oozie Actions
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
Categories of NoSQL Databases
Hbase vs RDBMS
Basic CRUD Operation Demo
Basic CRUD Operation
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.
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