Data Science Online Training

• Big Data Specialists, Business Analysts and Business Intelligence professionals
• Statisticians looking to improve their Big Data statistics skills
• Developers wanting to learn Machine Learning (ML) Techniques
• Information Architects looking to learn Predictive Analytics
• Those looking to take up the roles of Data Scientist and Machine Learning Expert

There are no particular fundamentals for this Data Science online training Course. If you are interested in mathematics, it is helpful to learn Data Science. You will also get MS Excel self-paced course.

The average salary of a Data Science Developer in the United States is $118,000. The average salary of a
Data Science developer in India is Rs. 620,000.

Most of the companies are hiring data scientists. Here are some of the top companies like Google,
Amazon, Microsoft, IBM, Facebook, WalMart, Visa, Target, Bank of America and others.

• Successful completion of all projects, which will be evaluated by trainers
• Scoring minimum 60% in Data Science screening test

• Understand the Problem
• Learn about the issue at ground, ask the right questions which is at the center of what a Data Scientist does.
• Collect Enough Data
• As the name implies the Data Scientist has to collect enough data in order to make sense of the problem at hand and get a better grip of the issue with respect to the time, money and resources needed.
• The Raw Data Process
• Data can rarely be used in its original form. It needs to be processed and various methods exist to convert it into a usable format.

After the data has been processed and converted into a form that can then be used for the later stages, you need to explore it further so as to get the characteristics of the data and find out more about the obvious trends, correlation and more.

This is where the magic happens. The data scientist deploys the various arsenals in his repository like machine learning, statistics and probability, linear and logistic regression, time-series analysis and more in order to make sense of the data.

1. Data Science Training Overview
1. Objectives of the Course
2. Pre-Requistes of the Course
3. Course Duration
2. Data Science Course Content
1. Introduction to Data Science
2. Data
3. Big Data
4. Data Science Deep Dive
5. Intro to R Programming
6. R Programming Concepts
7. Data Manipulation in R
8. Data Import Techniques in R
9. Exploratory Data Analysis (EDA) using R
10. Data Visualization in R
11. HADOOP
1. Big Data and Hadoop Introduction
2. Understand Hadoop Cluster Architecture
3. Map Reduce Concepts
4. Advanced Map Reduce Concepts
12. Hadoop 2.0 and YARN
13. PIG
14. HIVE
1. Module-9
15. HBASE
1. Module-11
16. SQOOP
17. Flume and Oozie
18. Projects
19. Project in Healthcare Domain
20. Project in Finance/Banking Domain
21. Spark
1. Apache Spark
2. Introduction to Scala
3. Spark Core Architecture
4. Spark Internals
5. Spark Streaming
22. Statistics + Machine Learning
1. Statistics
1. What is Statistics?
23. Machine Learning
1. Machine Learning Introduction
24. Python
1. Getting Started with Python
2. Sequences and File Operations
25. Deep Dive – Functions Sorting Errors and Exception Handling
26. Regular Expressionist’s Packages and Object – Oriented Programming in Python
27. Debugging, Databases and Project Skeletons
28. Machine Learning Using Python
29. Supervised and Unsupervised learning
30. Algorithm