Data Science with R Training

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The training on Data Science with R provides the skills required to work with real data sets and provide an opportunity to use data to provide data-driven strategic and tactical recommendations. This training will provide some insights on techniques such as linear and logistic regression, ANOVA, Segmentation, Ensemble models, SVM and machine learning in big data. In addition to technical skills, the program also allows students to build effective leadership and communication skills to advance their career upon graduation.

The training allows the learner to:

  • Explore R data structures and syntaxes
  • Read and write data from a local file to a cloud-hosted database
  • Work with data, get summaries, and transform them to fit your needs
  • Explore R language fundamentals, including basic syntax, variables, and types
  • Create functions and use control flow
  • Read, write and work with data in R
Target audience
  • Professionals working as Data and Business Analysts
  • Software professionals willing to change career path into analytics field
  • Individuals having an interest in the field of Data Science
  • Graduates willing to make career in Analytics and Data Science

The following are the prerequisites for joining Data Science with R training:

  • Should have any of these degrees in the STEM fields: Master’s /PhD/Graduate Degree
  • Know the fundamentals of programming
  • Know the basics of SQL
  • Familiar with the basic math and statistic concepts

1. Exploratory Data Analysis and Visualization

  • Exploratory Data Analysis
  • Data Collection and Curation
  • Data Visualization

2. R for Data Science

  • Introduction to R
  • Data Manipulation and Visualization with R
  • Data Pre-Processing with R

3. Data Mining

  • Creating Business Value using Data Mining for Actionable Insights
  • Association Rules and Collaborative Filtering

4. Data Analysis for Evidence Based Decision Making

  • Regression Analysis: Estimating Relationships
  • Multiple Regression
  • ANOVA and Experimental Design

5. Industry Applications of Advanced Analytics Models

  • Implementing Predictive Analytics Models for Industry Use Cases
  • Implementing Machine Leraning Models for Industry Use Cases

6. Big Data Analytics with Spark

  • Introduction to Big Data
  • Machine Learning in Spark

7. Project Management in Analytics

  • Managing Impactful Analytics Projects for Internal Clients
  • Best in Class Project Management to Delight your External Clients
  • Successful Project Outsourcing

8. Information to Insight

  • Advanced Visualization
  • Storytelling with Data

9. Career Management

  • Career Advancement and Growth

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