[vc_row][vc_column][vc_tta_accordion][vc_tta_section title=”What are the course objectives?” tab_id=”1501589497095-e430a12f-08d3″][vc_column_text]The Data Science with R training course has been designed to impart an in-depth knowledge of the various data analytics techniques which can be performed using R. The course is packed with real-life projects, case studies, and includes R CloudLabs for practice.
Mastering R language: The course provides an in-depth understanding of the R language, R-studio, and R packages. You will learn the various types of apply functions including DPYR, gain an understanding of data structure in R, and perform data visualizations using the various graphics available in R.
Mastering advanced statistical concepts: The course also includes the various statistical concepts like linear and logistic regression, cluster analysis, and forecasting. You will also learn hypothesis testing.
As a part of the course, you will be required to execute real-life projects using CloudLab. The compulsory projects are spread over four case studies in the domains of healthcare, retail, and Internet. R CloudLab has been provided to ensure a practical and hands-on experience. Additionally, we have four more projects for further practice.[/vc_column_text][/vc_tta_section][vc_tta_section title=”What skills will you learn?” tab_id=”1501589497182-57d425f0-63c0″][vc_column_text]
- Gain a foundational understanding of business analytics
- Install R, R-studio, and workspace setup. You will also learn about the various R packages
- Master the R programming and understand how various statements are executed in R
- Gain an in-depth understanding of data structure used in R and learn to import/export data in R
- Define, understand and use the various apply functions and DPLYP functions
- Understand and use the various graphics in R for data visualization
- Gain a basic understanding of the various statistical concepts
- Understand and use hypothesis testing method to drive business decisions
- Understand and use linear, non-linear regression models, and classification techniques for data analysis
- Learn and use the various association rules and Apriori algorithm
- Learn and use clustering methods including K-means, DBSCAN, and hierarchical clustering
[/vc_column_text][/vc_tta_section][vc_tta_section title=”Who should take this course?” tab_id=”1501589570866-6e7f3b0a-aaca”][vc_column_text]
- IT professionals looking for a career switch into data science and analytics
- Software developers 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
- Anyone with a genuine interest in the data science field
- Experienced professionals who would like to harness data science in their fields
[/vc_column_text][/vc_tta_section][vc_tta_section title=”What is CloudLab?” tab_id=”1501589598049-70394c04-87bf”][vc_column_text]CloudLab is a cloud-based R lab offered along with the course to ensure a hassle-free execution of the project work included.
With CloudLab, you do not need to install and maintain R on a virtual machine. Instead, you’ll be able to access a preconfigured environment—on CloudLab via your browser.
You can access CloudLab from the Simplilearn LMS (Learning Management System) for the duration of the course.[/vc_column_text][/vc_tta_section][vc_tta_section title=”What projects are included in this course?” tab_id=”1501589597827-9a984e8f-b527″][vc_column_text]The course includes eight real-life, industry-based projects. R CloudLab has been provided for a hassle-free execution of these projects. Successful evaluation of one of the following four projects is a part of the certification eligibility criteria.
Healthcare: Predictive analytics can be used in healthcare to mediate hospital readmissions. In healthcare and other industries, predictors are most useful when they can be transferred into action. But historical and real-time data alone are worthless without intervention. More importantly, to judge the efficacy and value of forecasting a trend and ultimately changing behavior, both the predictor and the intervention must be integrated back into the same system and workflow where the trend originally occurred.
Insurance: Use of predictive analytics has increased greatly in insurance businesses, especially for the biggest companies, according to the 2013 Insurance Predictive Modeling Survey. While the survey showed an increase in predictive modeling throughout the industry, all respondents from companies that write over $1 billion in personal insurance employ predictive modeling compared to 69% of companies with less than that amount of premium.
Retail: Analytics is used in 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 collection, modeling, and analysis of user data in large-scale online services, such as social networking, e-commerce, search, and advertisement. In this class, we explore a number of key functions of such online services that have become ubiquitous over the last couple of years. Specifically, we look at social and information networks, recommender systems, clustering and community detection, dimensionality reduction, stream computing, and online ad auctions.
Four additional projects have been provided to help learners master the R language.
Music Industry: To understand listener preferences, the 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 the U.S. employment data of all 50 states, covering the period from 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 to 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.[/vc_column_text][/vc_tta_section][/vc_tta_accordion][/vc_column][/vc_row][vc_row][vc_column][vc_custom_heading text=”FAQs” font_container=”tag:h3|text_align:center” google_fonts=”font_family:Roboto%20Slab%3A100%2C300%2Cregular%2C700|font_style:400%20regular%3A400%3Anormal”][vc_tta_accordion][vc_tta_section title=”What are the System Requirements?” tab_id=”1508823077370-26367fa9-e6d6″][vc_column_text]You will need to download R from the CRAN website and RStudio for your operating system. These are both open source and the installation guidelines are presented in the data science course.[/vc_column_text][/vc_tta_section][vc_tta_section title=”Who are our instructors and how are they selected?” tab_id=”1508823077442-1f9dbab0-d5b7″][vc_column_text]All of our highly qualified trainers are industry experts with at least 10-12 years of relevant teaching experience. Each of them has gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating remain on our faculty for data science online training.[/vc_column_text][/vc_tta_section][vc_tta_section title=”What training formats are used for this course?” tab_id=”1508823153469-70e293be-1135″][vc_column_text]We offer this data science with R certification course in the following formats:
Live Virtual Classroom or Online Classroom: With online classroom training, you have the option to attend the course remotely from your desktop via video conferencing. This format reduces productivity challenges and decreases your time spent away from work or home.
Online Self-Learning: In this mode, you’ll receive lecture videos that you can view at your own pace.[/vc_column_text][/vc_tta_section][vc_tta_section title=”What if I miss a class?” tab_id=”1508823200118-47db30c4-1ce3″][vc_column_text]We record the class sessions and provide them to participants after the session is conducted. If you miss a class, you can view the recording before the next class session.[/vc_column_text][/vc_tta_section][vc_tta_section title=”Can I cancel my enrollment? Will I get a refund?” tab_id=”1508823264431-bcbf2572-7d0b”][vc_column_text]Yes, you can cancel your enrollment if necessary. We will refund the course price after deducting an administration fee. To learn more, you can view our Refund Policy.[/vc_column_text][/vc_tta_section][/vc_tta_accordion][/vc_column][/vc_row]
- Introduction to Business Analytics
- Introduction to R Programming
- R Programming
- R Data Structure
- Data Visualization
- Statistics for Data Science-I
- Statistics for Data Science-II
- Regression Analysis
- Business Analytics with Excel
- Statistics Essential for Data Science