Data Science Training

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Course Details – Data Science Training – Bangalore

Data Science Training Timings

  • Course Duration: Data Science Training – 60 days
  • Flexible Timings – Week Days or Weekends
    • Weekdays – Monday to Friday
    • Weekends – Saturday & Sunday
  • Online Training
  • Corporate Training
  • Location: Dzital Cloud, #93/3, 1st Cross, Tulsi Theatre Road, Marathahalli, Bangalore

100 % Guaranteed JOB Assistance

  • We prefer Practical Training than Theoretical.
  • We also Provide In Depth Real Time Training
  • Resume Preparation as per the Corporate Standards.
  • Interview Question and Answers Will be Provided.
  • Practice Tests will be Conducted.
  • Mock Interviews By Experts.
  • Hand on Sessions on Real Time Projects
  • Updated Python Course Contents as per IT Industry Standards.
  • We give Personal Attention on each student in a Classes for Better Understanding of  Python Programming Language Sessions.

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Data Science Training & Scope!

What is Data Science?

Data science is a “concept to unify statistics, data analysis and their related methods” to “understand and analyse 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 Certification 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.

After the completion of the course, you should be able to:

  • 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
  • Analyze data using Machine Learning algorithms in R
  • Explain Time Series and its 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

What are the Topics in Data Science Course Learning?

  • What is Data Science?
  • What does Data Science involve?
  • Era of Data Science
  • Business Intelligence vs Data Science
  • Life cycle of Data Science
  • Tools of Data Science
  • Introduction to Big Data and Hadoop
  • Introduction to Machine Learning

Machine Learning

Goal – Get an introduction to Machine Learning as part of this Module. You will discuss the various categories of Machine Learning and implement Supervised Learning Algorithms.

Objectives – At the end of this module, you should be able to:

  • Define Machine Learning
  • Discuss Machine Learning Use cases
  • List the categories of Machine Learning
  • Illustrate Supervised Learning Algorithms


  • What is Machine Learning?
  • Machine Learning Use-Cases
  • Machine Learning Process Flow
  • Machine Learning Categories
  • Supervised Learning
  • Linear Regression
  • Logistic Regression

Deep Learning

Goal – Get introduced to the concepts of Reinforcement learning and Deep learning in this Module. These concepts are explained with the help of Use cases. You will get to discuss Artificial Neural Network, the building blocks for artificial neural networks, and few artificial neural network terminologies.

Objectives – At the end of this module, you should be able to:

  • Define Reinforced Learning
  • Discuss Reinforced Learning Use cases
  • Define Deep Learning
  • Understand Artificial Neural Network
  • Discuss basic Building Blocks of Artificial Neural Network
  • List the important Terminologies of ANN’s


  • Reinforced Learning
  • Reinforcement learning Process Flow
  • Reinforced Learning Use cases
  • Deep Learning
  • Biological Neural Networks
  • Understand Artificial Neural Networks
  • Building an Artificial Neural Network
  • How ANN works
  • Important Terminologies of ANN’s

Statistical Inference

Goal – In this Module, you should learn about different statistical techniques and terminologies used in data analysis.

Objectives – At the end of this Module, you should be able to:

  • Define Statistical Inference
  • List the Terminologies of Statistics
  • Illustrate the measures of Center and Spread
  • Explain the concept of Probability
  • State Probability Distributions


  • What is Statistical Inference?
  • Terminologies of Statistics
  • Measures of Centers
  • Measures of Spread
  • Probability
  • Normal Distribution
  • Binary Distribution


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