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Data Science Online Training

About Data Science Training Course

Data Science Training Online from Krish Software Solutions provides you detailed learning in data science, data analytics, project life cycle, data acquisition, analysis, statistical methods and machine learning. You will learn and expertise to organize Recommenders using R programming, data analysis, data transformation, experimentation and evaluation.

What you will learn in this Data Science Course?

  • Introduction to Data Science in real world, Project Life cycle, and Data Acquisition
  • Study the tools and techniques of Experimentation, Evaluation and Project Deployment
  • Understand Machine Learning Algorithms
  • Learn the concept of Prediction and Analysis Segmentation through Clustering
  • Learn the basics of Big Data and ways to integrate R with Hadoop
  • Get trained about the roles and responsibilities of a Data Scientist
  • Explore steps to install IMPALA
  • Live Projects on Data science, analytics and Recommender Systems
  • Work on data mining, data structures, data manipulation.

Who should take this Data Science Online Course?

  • 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

What are the fundamentals for learning Data Science?

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.

What is the Average Salary for a Data Science in India & US?

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.

What are the Top Companies hiring Data Scientist Professionals?

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

What does a Data Scientist do??

  • 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.

Explore the Data

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.

Analyze the Data

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.

Course Content

  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