R is a data analysis software as well as a programming language. Data scientists, statisticians and analysts use R for statistical analysis, data visualization and predictive modeling. R is open source and allows integration with other applications and systems. Compared to other data analysis platforms, R has an extensive set of data products. Problems faced with data are cleared with R’s excellent data visualization feature.
The first section in this course deals with how to create R functions to avoid the unnecessary duplication of code. You will learn how to prepare, process, and perform sophisticated ETL for heterogeneous data sources with R packages. An example of data manipulation is provided, illustrating how to use the ‘dplyr’ and ‘data.table’ packages to efficiently process larger data structures. We also focus on ‘ggplot2’ and show you how to create advanced figures for data exploration.
- Make use of statistics and programming to understand data mining concepts and their application
- Use R programming to apply statistical models to data
- Use various libraries available in R CRAN (comprehensive R archives network) in data mining
- Apply data management steps to handle large datasets
- Get to know various data visualization libraries available in R to represent data
Functions In RModule 2:
Data Extracting, Transforming, And LoadingModule 3:
Data Pre-Processing And PreparationModule 4:
Data ManipulationModule 5:
Visualizing Data With Ggplot2
Making Interactive ReportsModule 7:
Simulation From Probability DistributionsModule 8:
Statistical Inference In RModule 9:
Rule And Pattern Mining With RModule 10:
Time Series Mining With R
Supervised Machine LearningModule 12:
Unsupervised Machine Learning
Yu-Wei, Chiu (David Chiu) is the founder of LargitData (www.LargitData.com), a startup company that mainly focuses on providing big data and machine learning products. He has previously worked for Trend Micro as a software engineer, where he was responsible for building big data platforms for business intelligence and customer relationship management systems. In addition to being a start-up entrepreneur and data scientist, he specializes in using Spark and Hadoop to process big data and apply data mining techniques for data analysis. Yu-Wei is also a professional lecturer and has delivered lectures on big data and machine learning in R and Python, and given tech talks at a variety of conferences.
In 2015, Yu-Wei wrote Machine Learning with R Cookbook, Packt Publishing. In 2013, Yu-Wei reviewed Bioinformatics with R Cookbook, Packt Publishing. For more information, visit his personal website at www.ywchiu.com.
A test will be conducted at the end of the course. On completion of the test with a minimum of 70% marks, training.com will issue a certificate of successful completion from NIIT.
Five re-attempts will be provided in case the candidate scores less than 70%.
A Participation certificate will be issued if the candidate does not score 70% after five attempts.
Basic knowledge of data analysitcs and basic programming background with Math as one of the subjects.
Who should go for this Course?
Anyone aspring a great career in Data Science involving complex data analytics and data mining techniques would find this course a must to pursue. Professionals working on Data Mining Projects can also pursue this course to help them gain an extra edge over complex data mining algorithms development using R.
Where can I find my session schedule?
The session schedule will be available in the training.com Student portal - Learning Plan section. You can login to your training.com account to view the same.
What is your refund policy?
Upon registering for the course, if for some reason you are unable or unwilling to participate in the course further, you can apply for a refund. You can initiate the refund any time before start of the second session of the course by sending an email to email@example.com , with your enrolment details and bank account details (where you want the amount to be transferred). Once you initiate a refund request, you will receive the amount within 21 days after confirmation and verification by our team. This is provided if you have not downloaded any courseware after registration.
Why is it called Self Paced course?
Self Paced courses are comprised of several learning videos into a course structure broken down into Learning Modules and Sessions. The learner is required to go through the videos topic-wise in the structure sequence of the course to learn the concepts. Being Self Paced, there is no intervention of any external faculty or additional mentor in learning.
Being a self paced course, how will my attendance be tracked and marked?
you login into your training.com account to watch the videos, attendance for it will be marked automatically.
How will I get certified in a self-paced course?
Assessments are not applicable in self-paced courses. You should spend at least 70% of course duration to attend and watch the self-paced content. Once you complete the course you will be awarded a verified NIIT eCertificate. The certificate will reach you to your email ID registered with us within 7 working days.