Data Science Online Training

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Why Data Science as a career option?

According to prediction by a research by MGI and McKinsey’s Business Technology Office in 2013 there would be a shortage of talent necessary for organizations to take advantage of big data. By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.

The prediction of the research has come true. In years to come, the shortage of skilled manpower in data science will increase exponentially. So, as future Data Scientists, you have a great future ahead!

Data Science

Why should you take up the Data Scientist course?
The average annual salary of Data Scientists as per Indeed is approximately US$122,801 in the United States.

  • Data Scientist is the best job in the 21st century – Harvard Business Review
  • The number of jobs for all data professionals in the United States will increase to 2.7 million – IBM
  • Global Big Data market achieves US$122 billion in sales in 6 years – Frost & Sullivan

The demand for Data Scientists far exceeds the supply. This is a serious problem in a data-driven world that we are living in today. As a result, most organizations are willing to pay high salaries for professionals with appropriate Data Science skills.
Data science training online will help you become proficient in Data Science, R programming, Data Analysis, Big Data, and more. Thus, you can easily accelerate your career in this evolving domain and take it to the next level.

Data Science With Python

  1. Introduction to the Data Science in Python Bootcamp
  2. Introduction to Python Pre-Requisites for Data Science
  3. Introduction to Numpy
  4. Introduction to Pandas
  5. Data Pre-Processing/Wrangling
  6. Introduction to Data Visualizations
  7. Statistical Data Analysis-Basic
  8. Statistical Inference & Relationship Between Variables
  9. Machine Learning for Data Science
  10. Unsupervised Learning in Python
  11. Supervised Learning
  12. Artificial Neural Networks (ANN) and Deep Learning (DL)
  • Theory Behind ANN and DNN
  • Perceptrons for Binary Classification
  • Getting Started with ANN-binary classification
  • Multi-label classification with MLP
  • Regression with MLP
  • MLP with PCA on a Large Dataset
  • Start With Deep Neural Network (DNN)
  • Start with H20
  • Default H2O Deep Learning Algorithm
  • Specify the Activation Function
  • H2O Deep Learning For Predictions

13.Miscellaneous Lectures & Information

Highlights of the course

  • Online, Classroom and On-Premise
  • Knowledge of core Java is must
  • Suitable for students and programmers

Course Duration

  • Normal: One Month (online and classroom)
  • Accelerated: One Week (online and classroom)
  • On Premise: 3-5 days (accelerated)

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