Introduction and Overview
Business Analytics increase their Total Addressable Market (TAM) to $82B for calendar year (CY) 2018, fueling an 11% CAGR in their total addressable market from CY 2013 to 2018.
The global text analytics market has a potential to reach $6.5B by 2020, attaining a CAGR of 25.2% from 2014 to 2020. Customer Relationship Management (CRM), predictive analytics and brand reputation are the top three projected applications.
The role of a Certified Data Analytics (R) Specialist (CDAS)
As a data analytics professional, you will be working as a strategy oriented role or can work as a very specialized deep learning scientist. The ‘strategy oriented’ role has a stronger component of the business, while the ‘deep learning’ role has a much stronger component of analytics. Therefore, a data analytics professional generally has a trade-off between these two components and you can switch between roles that have different proportions of the two components. The value which you create for yourself is a positively correlated function of business understanding and analytics.
Certified Data Analytics (R) Specialist (CDAS) course by GSTF in Singapore is designed for professionals who aspire to learn an open source R tool for Analytics. Certified R Analytics Specialist covers the concept of Business Analytics and its strategic importance to any organization. You will understand how cutting-edge businesses use data to optimize marketing, maximize revenue, make operations efficient, and make hiring and management decisions so that you can apply these strategies to your own company or business. Participants will learn how to address business needs through the use of analytics, how some organizations have done it and what has been done to achieve them. Conducted interactively with case studies and real business problems, participants can also expect to learn the basic principles, concepts, techniques and tools used in business analytics. Also, covers different types of business analytics with real life use cases including descriptive analytics and predictive analytics.
“Modern enterprises are drowning in data and starving information”
32 Hours (4 Days)
70 -100 % CITREP+ Funding available. Conditions Apply!
Participants are preferred to have min. 2 years of experience in software development, business domain or data/business analysis.
As part of the written examination, each participant will be assessed individually on the last day of the training for their understanding of the subject matter and ability to evaluate, choose and apply them in specific context and also the ability to identify and manage risks. The assessment focuses on higher levels of learning in Bloom’s taxonomy: Application, Analysis, Synthesis and Evaluation. This written examination will primarily consist of 40 multiple choice questions spanning various aspects as covered in the program. It is an individual, competency-based assessment.
- Understand Data Analytics and its impact in enterprises with several real use cases and examples
- Gain a solid foundation in the statistical and analytical methods that make up the backbone of data science
- Get skilled in R Programming from beginner to advance level (includes Data Wrangling Techniques, Text Analytics, Word Cloud etc.)
- Learn key Data Mining & Predictive Modelling techniques and implement using R Script /Rattle Package
- Gain complete understanding of Big Data Landscape and how NoSQL databases playing key role in Analytics
- Get Skilled in NoSQL DB : MongoDB & learn to query on Document based (JSON/BSON) Data.
Unit 1: Introduction of Business Analytics
- Introduction to Business Analytics
- Types of Business Analytics
- Business Analytics tools
Unit 2: Introduction to R
- What is R?
- Installation Procedure of R Cmdr. and RStudio interface.
- R Libraries
- Create and Execute R Scripts
- Installing a Packages
- Working with R Objects
- Obtain help while using R
Unit 3: Data Structures, Operators and Functions in R
- Data Frames
- Types of Operators
- Built-in Functions
Unit 4: Exploring and Visualizing Data in R
- Import Data into R
- Analyze and Visualize data in R
- Pie chart
- Bar plot
Unit 5: NoSQL Database with R for Data Analysis
- Introduction to NoSQL Databases
- Types of NoSQL Databases
- Installation of MongoDB
- Import data into MongoDB
- Install RMongo package
- Write R Script
- Perform Data Analysis
Unit 6: Big Data Analysis in R
Unit 7: Data Mining in R
- Introduction to Data Mining, Machine Learning.
- Clustering and its Application.
- Clustering Techniques.
Unit 8: Predictive Modelling in R
- Introduction to Predictive Modelling.
- Linear Regression.
- Logistic Regression.
- Neural Networks
- Support Vector Machines
- K-nearest neighbor Classification
- Decision trees
Unit 9: R Applications
- Medical Image Analysis
- Natural Language Processing
- Credit Risk Analysis
- Time series modeling and geospatial analysis
- Statistical Analysis
Unit 10: R Case Studies
- R for Reporting
- R for Data Analysis
- R for flood forecasting
Visit our GSTF course schedule to check our class availability.