Mastering Data Analysis with R

With its popularity as a statistical programming language rapidly
increasing with each passing day, R is increasingly becoming the
preferred tool of choice for data analysts and data scientists who want
to make sense of large amounts of data as quickly as possible. R has a
rich set of libraries that can be used for basic as well as advanced
data analysis tasks. If you have a basic understanding of data analysis
concepts and want to take your skills to the next level, this video is
for you. Spanning over four hours, it contains carefully selected
advanced data analysis concepts such as: cluster analysis; time-series
analysis; Association mining; PCA (Principal Component Analysis);
handling missing data; sentiment analysis; spatial data analysis with R
and QGIS; advanced data visualization with R and ggplot2.

Throughout
the video, readers will use the various topics they’ve learned about to
analyze real-world datasets from various industry sectors. By the end
of the tutorial, readers will have a thorough understanding of advanced
data analysis concepts and how to implement them in R.

About the Author :

Dr. Bharatendra Rai is Professor of Business Statistics and
Operations Management in the Charlton College of Business at UMass
Dartmouth. He received his Ph.D. in Industrial Engineering from Wayne
State University, Detroit. His two master’s degrees include
specializations in quality, reliability, and OR from Indian Statistical
Institute and another in statistics from Meerut University, India. He
teaches courses on topics such as Analyzing Big Data, Business Analytics
and Data Mining, Twitter and Text Analytics, Applied Decision
Techniques, Operations Management, and Data Science for Business. He has
over twenty years’ consulting and training experience, including
industries such as automotive, cutting tool, electronics, food,
software, chemical, defense, and so on, in the areas of SPC, design of
experiments, quality engineering, problem solving tools, Six-Sigma, and
QMS. His work experience includes extensive research experience over
five years at Ford in the areas of quality, reliability, and six-sigma.
His research publications include journals such as IEEE Transactions on
Reliability, Reliability Engineering & System Safety, Quality
Engineering, International Journal of Product Development, International
Journal of Business Excellence, and JSSSE. He has been keynote speaker
at conferences and presented his research work at conferences such as
SAE World Conference, INFORMS Annual Meetings, Industrial Engineering
Research Conference, ASQs Annual Quality Congress, Taguchi’s Robust
Engineering Symposium, and Canadian RAMS.

Dr. Rai has won awards for Excellence and exemplary teamwork at Ford
for his contributions in the area of applied statistics. He also
received an Employee Recognition Award by FAIA for his Ph.D.
dissertation in support of Ford Motor Company. He is certified as ISO
9000 lead assessor from British Standards Institute, ISO 14000 lead
assessor from Marsden Environmental International, and Six Sigma Black
Belt from ASQ.