Data Science based on R programming

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About Course

Data Science in its simpler terms is about generating critical business value from the data by various creative ways. It can also be defined as a mix of data research, algorithms and technology in order to solve complex analytical issues. Data is being by generated by Companies at an exponential pace. The usable Data form can be different for different section of people working in an organization. Data Science helps us to explore the data to the granular form and find the needed insights. Data Science is about being analytical or inquisitive wherein asking new questions, doing new explorations and keep learning is a part of job for Data Scientists.

Our Data Science Training Course in Pune helps you to understand and acquire indepth Data Analytics skills and techniques using R and Python languages. With our experienced and professional trainers, and hands on Data Science Course, we make sure that candidates are well versed with the techniques and take the maximum benefit from our course. The Data Analytics Course with R ensures your job with Big MNC’s and our placement team assures you with jobs as well by providing you the placement calls

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What Will You Learn?

  • Understand what Data Science is and the skill sets needed to be a data scientist.
  • Understand the basic terms what Statistical Inference means and probability distributions.
  • Understand the Data Science Process and how its components interact.
  • Understand basic machine learning algorithms (Linear Regression, k-Nearest Neighbours (k-NN), k-means, Naive Bayes) for predictive modeling. And why Linear Regression and k-NN are poor choices for Filtering Spam.
  • Why Naive Bayes is a better alternative.
  • Identify common approaches used for Feature Generation. Identify basic Feature Selection algorithms (Filters, Wrappers, Decision Trees, Random Forests) and use in applications.
  • Identify and explain fundamental mathematical and algorithmic ingredients that constitute a Recommendation Engine (dimensionality reduction, singular value decomposition, principal component analysis). Build their own recommendation system using existing components.
  • Work effectively (and synergically) in teams on data science projects.