Data Science with R Programming - Simplilearn
Starting FromRM 1550.7940 Hours
HRDF SBL Claimable
Certificate of Attendance available
180 days of access from date of purchase
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The Data Science with R training has been designed to prepare you for a job in the analytics space. R is the most used programming language today in data science and analytics space. It is open source in nature and very powerful, hence quickly becoming the language of choice of data scientists around the world. With a vibrant community, number of statistical packages and data visualization tools R is a must have tool for every data scientist. With the immense skill gap for analytics professionals this data science certification is the first step in this field.
This R course has been structured for someone new to the field of analytics. This data science training will make you an expert at understanding the problem, designing the analysis and applying predictive modelling techniques using R to derive business insights from data.
The booming demand for skilled data scientists across industries makes this course suited for all individuals at all level of experience. We recommend this data science training specially the following professionals:
- Software professionals looking for a career switch in the field of analytics
- Professionals working in field of Data and Business Analytics
- Graduates looking to build a career in Analytics and Data Science
- Anyone with a genuine interest in the field of Data Science
- Experienced professionals who would like to harness data science in their fields
Data Scientist with R programming certification has a clear focus on the vital concepts of business analytics and R. By the end of the training, participants will be able to:
- Work on data exploration, data visualization, and predictive modeling techniques with ease.
- Gain fundamental knowledge on analytics and how it assists with decision making.
- Work with confidence using the R language.
- Understand and work on statistical concepts like linear & logistic regression, cluster analysis, and forecasting.
- Develop a structured approach to use statistical techniques and R language.
- Perform sharp data analysis to make business decisions.
- Analytics v/s Analysis
- What is Analytics?
- Popular Tools
- Role of Data Scientist
- Analytics Methodology
- Problem Definition
- Summarizing Data
- Data Collection
- Data Dictionary
- Outlier Treatment
- Descriptive Statistics
- Probability Theory
- Tests of Significance
- Non-parametric Testing
- Data Exploration
- Data Visualization
- Diagnostic Analytics
- Linear Regression
- Logistic Regression
- Cluster Analysis
- Time Series Analysis