The Data Science Training enables you to gain knowledge of the entire Life Cycle of Data Science, analyzing and visualizing different data sets, different Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes.

1. Data Science Training
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2. Description
Data science is a "concept to unify statistics, data
analysis and their related methods" to "understand and
analyze actual phenomena" with data. It employs
techniques and theories drawn from many fields within
the broad areas of mathematics, statistics, information
science, and computer science from the subdomains of
machine learning, classification, cluster analysis, data
mining, databases, and visualization. The Data Science
Training enables you to gain knowledge of the entire Life
Cycle of Data Science, analyzing and visualizing different
data sets, different Machine Learning Algorithms like K-
Means Clustering, Decision Trees, Random Forest, and
Naive Bayes.

3. Objectives
• Gain insight into the 'Roles' played by a Data
Scientist
• Analyze several types of data using R
• Describe the Data Science Life Cycle
• Work with different data formats like XML,
CSV etc.
• Learn tools and techniques for Data
Transformation
• Discuss Data Mining techniques and their
implementation

4. Objectives
• Analyze data using Machine Learning algorithms in
R
• Explain Time Series and it’s related concepts
• Perform Text Mining and Sentimental analyses on
text data
• Gain insight into Data Visualization and
Optimization techniques
• Understand the concepts of Deep Learning

5. Why learn Data Science
Data science incorporates tools from multi disciplines
to gather a data set, process and derive insights from
the data set, extract meaningful data from the set,
and interpret it for decision-making purposes. The
disciplinary areas that make up the data science field
include mining, statistics, machine learning, analytics,
and some programming. Data mining applies
algorithms in the complex data set to reveal patterns
which are then used to extract useable and relevant
data from the set. Statistical measures like predictive
analytics utilize this extracted data to gauge events
that are likely to happen in the future based on what
the data shows happened in the past.

6. This course is appropriate for:
• Developers aspiring to be a 'Data Scientist‘
• Analytics Managers who are leading a team of
analysts
• Business Analysts who want to understand
Machine Learning (ML) Techniques
• Information Architects who want to gain
expertise in Predictive Analytics
• 'R' professionals who want to captivate and
analyze Big Data
• Analysts wanting to understand Data Science
methodologies

7. Prerequistes
There is no specific pre-requisite for this training program,
however basic understanding of R can be beneficial.