Data Science with R training course has been designed to prepare you for a job in the analytics space. R is the most used programming language today in the data science and analytics field. Since it’s an open source in nature and very powerful, R is becoming the language of choice of data scientists around the world. R is first step in the field of data science and a must-have for every data scientist.
This course is structured for candidates who are new to the field of analytics. This course will make you skilled at understanding the problem, designing the analysis, and applying predictive modelling techniques using R to derive business insights from data.
R programming certification training course is clearly focused on the key concepts of business analytics and R. By the end of the training, candidates will be able to:
- Work on data exploration, data visualization, and predictive modeling techniques with ease
- Understand analytics and how it assists with decision making
- Work with sureness using the R language
- Understand and work on statistical concepts like linear and logistic regression, cluster analysis, and forecasting
- Develop a structured approach to use statistical techniques and the R language
- Perform data analysis to take business decisions
Who should attend R Course?
There are no prerequisites for this course—if you are new to data science, this is the best course to start with. The demand for skilled data scientists across all industries makes this course suited for participants at all levels of experience. We recommend this data science training especially for the following professionals:
- Software professionals looking for a career switch into data science and analytics
- Professionals working in data and business analytics
- Graduates looking to build a career in analytics and data science
- Professionals who would like to implement data science in their fields
What projects will be completed during the course?
You will work on four real-life, industry-based projects spread over four case studies.
Healthcare: the intervention must be integrated back into the same system and workflow where the trend originally occurred.
Insurance: Predictive analytics is widely used in insurance businesses, especially for the biggest companies.
Retail: Optimizing product placements on shelves or optimization of inventory to be kept in the warehouses using industry examples. Through this project, participants learn the daily cycle of product optimization from the shelves to the warehouse. This gives them an insight of the regular happenings in the retail sector.
Internet: Internet analytics is the group, modelling, and analysis of user data in large-scale online services, such as social networking, e-commerce, search, and advertisement. Here, we explore functions of such online services that have become universal over the last few years. Specifically, we look at social & information networks, recommender systems, clustering and community detection, dimensionality reduction, stream computing, and online ad auctions.
Additional practice projects have been provided for practice
The following projects are designed to help learners master the R language.
Music Industry: To understand listener preferences, these details are recorded online. This data is not only used for recommending music that the listener is likely to enjoy but also to drive a focused marketing strategy that sends out advertisements for music that a listener may wish to buy. Using the demographic data, predict the music preferences of the user for targeted advertising.
Finance: You’ll predict success and failure based on user demographic data; in this case for defaulting on a loan or not defaulting. You’ll perform logistic regression by considering the loan’s features and the characteristics of the borrower as explanatory variables.
Unemployment: Analyze the monthly, seasonally-adjusted unemployment rates for U.S. employment data for all 50 states, covering the period of January 1976 through August 2010. The requirement is to cluster the states into groups that are alike using a feature vector.
Airline: Flight delays are frequently experienced when flying from the Washington DC area into the New York City area. By using logistical regression, you’ll identify flights that are likely to be delayed. The provided data set helps with a number of variables including airports and flight times.
AIG certification process
- Candidates to complete any one project out of the four provided during the course. Your project will be reviewed by our lead trainer.
- Pass the online examination with a minimum score of 80%.
When you have completed the course, you will receive an experience certificate stating that you have 3 months experience in implementing Projects using R.
Note: It is mandatory that you fulfil both the criteria i.e. completion of any one Project and clearing the online exam with minimum score of 80%, to become a Certified Data Scientist
- Mentoring Sessions: Live Interaction with a subject matter expert to help participants with queries regarding project implementation and the course in general
- Guidance on forum: Industry experts to respond to participant queries on forum regarding technical concepts, projects and case-studies.
- Project Assistance: Queries related to solving & completing Projects, case-studies which are part of Data Scientist with R programming course offered by AIG
- Technical Assistance: Queries related to technical, installation, administration issues in Data Scientist with R programming training. In case of critical issues, support will be rendered through a remote desktop.
- R Programming: Queries related to R programming while solving & completing Projects, case-studies which are part of the Data Scientist Certification offered by AIG.
15 days training with daily 2 hours