Big Data and Data Science. The globally recognised training program

Transcription

1 Big Data and Data Science The globally recognised training program

2 Certificate in Big Data Analytics Duration 5 days Big Data and Data Science enables value creation from data, through the use of calculative power, the distributed architecture and scale out algorithms. In this 5-day workshop, we will learn how to create value by leveraging business, technology, and data expertise together in your organisation. What will you learn? Concepts about Big Data, Data Science, Analytics Value Chain, Machine Learning, Artificial Intelligence and the role of business expertise How to think - to innovate and strategise for growth of business from data driven approaches After completing the course you will able to Understand how to leverage big data for operational efficiency and innovative solutions in multiple industries (Oil and Gas, Manufacturing, Retail, Consumer Goods, Digital Marketing, Ecommerce, Social Media, Healthcare, Education, Banking and Finance, etc.) Understand open source tools like Hadoop, Hive, Hbase, Python, R, D3.js and other visualisation tools Who Should Attend? Leaders, decision makers, business and technology leaders, data scientists or future data scientists, and anyone who wants to innovate using data driven approaches Dr. Satyam Priyadarshy ALL RIGHTS RESERVED. 2

3 Big Data and Data Science - Overview Duration 3 Hours Big Data is a widely discussed topic. A large number of organisations are misleading enterprises and organisations relating to Big Data. Big Data is actually the 2 nd Industrial Revolution for most industries. In this course you will learn what Big Data is, how it fits into your organisation and what you can do to gain value from it. What you will learn What is Big Data and Data Science? The 4 enablers of Big Data The 7 pillars of Big Data How to create value from Big Data What is the role of Data Science? Who Should Attend After completing the course you will be able to: Explain the difference between business intelligence and actionable insights Understand the role of each of the 7 pillars of Big Data and apply them in your business Explain the 4 enablers (Data, Technologies, Analytics Value Chain, Return on Investment) Business leaders, Big Data and Data Science enthusiasts, decision-makers, career changers Dr. Satyam Priyadarshy ALL RIGHTS RESERVED. 3

4 Big Data Technologies Hadoop and Others Duration 6 Hours Hadoop is one of the key components of next generation massively scalable distributed storage and processing platforms. There are different versions of Hadoop available, and navigating the various associated tools requires some knowledge of the ecosystem. This course helps you with that. What will you learn? What is HDFS? What is Hadoop? What are different flavors of Hadoop? What is Hive, Hbase, Pig, Zookeeper, Kafka, Spark? How they interact for providing value for Big Data and Data Sceince Who Should Attend? After completing the course you will be able to: How different Hadoop components work? Role of HDFS, Hadoop, Hive, Pig, Zookeeper, Hbase, Kafka, Spark How to write a Hadoop program? How to interact with HDFS? Technology and business professionals, data architects, data base administrators who want to move into the world of Big Data, Data Scientists, System Administrators Dr. Satyam Priyadarshy ALL RIGHTS RESERVED. 4

5 Introduction to Data Science Duration 6 Hours Data Science is one of the enablers for creating value from Big Data. By using data science, one can create meaningful, actionable insights from data. It is a critical aspect of business innovation. What you will learn The need for Data Science and how it fits with your Big Data strategy Where Data Science fits in the analytics value chain in creating insights from Big Data After completing the course you will be able to: Understand various elements of data science (history, implementation, etc.) Gain in depth understanding of extracting actionable insights, predictions and value from big data Who Should Attend Anyone interested in data science. Current and future data scientists, decision makers and leaders Dr. Satyam Priyadarshy ALL RIGHTS RESERVED. 5