Data Science Training
Training schedule will be announced shortly.
Who can attend
Data science is the study of the generalizable extraction of knowledge from data, yet the key word is science. It incorporates varying elements and builds on techniques and theories from many fields, including signal processing, mathematics, probability models, machine learning, statistical learning, computer programming, data engineering, pattern recognition and learning, visualization, uncertainty modelling, data warehousing, and high performance computing with the goal of extracting meaning from data and creating data products. Data Science is not restricted to only big data, although the fact that data is scaling up makes big data an important aspect of data science.
24 hours instructor led online/classroom training
Practical assignments during the training
lifetime access to the Learning Management System (LMS)
Understand the Data Analysis life cycle
Certificate on successful completion of project
Industry wise case studies to better understand the applications
- Introduction to Data Science
- Roles and Responsibilities of a Data Scientist
- Architecture and Methodologies used to solve Big Data problems
- Data Manipulation Using R
- Machine Learning Techniques Using R
- What is Hadoop all about?
- Integrating R with Hadoop
- Introduction to Mahout
- Implementing Algorithms
- Some more Mahout Algorithms and Parallel Processing Using R
This course is best suited to
- Data analysts
- Statisticians with basic knowledge of Apache Hadoop: HDFS, MapReduce, Hadoop Streaming, and Apache Hive.
- SAS/SPSS Professionals looking to gain understanding in Big Data Analytics
- Business Analysts wanting to understand Machine Learning (ML) Techniques
- Information Architects wanting to gain expertise in Predictive Analytics
- Hadoop Professionals who want to learn R and ML techniques
On successful completion of the course, candidates will be able to:
- Understand the responsibilities of a Data Scientist
- Analyse Big Data using R, Hadoop and Machine Learning.
- Learn about the processes involved in the Data Analysis Life Cycle
- Learn how to use data formats including XML, CSV and SAS, SPSS
- Transform data using best practices and tools
- Learn to implement various Data Mining techniques
- Understand the use of machine learning algorithms in R
- Analyse data using Hadoop Mappers and Reducers
- Learn the basics of Apache Mahout
- Follow best practices in data visualization and optimization techniques