Statistics + Data processing
Label
Statistics + Data processing
Name
Statistics + Data processing
Sub focus
Actions
Incoming Resources
- Applied probability & statistics, for computer science, data science & machine learning, Dr. Mohammad Nauman
- Statistical application development with R and Python, power of statistics using R and Python, Prabhanjan Narayanachar Tattar
- Beginning R, an introduction to statistical programming, Joshua F. Wiley, Larry A. Pace
- 25 recipes for getting started with R, Paul Teetor
- R jin nang miao ji, R cookbook /, Paul Teetor zhu ; Zhang Xiajing yi
- An introduction to SAS® university edition, Ron Cody
- Machine learning 101 with Scikit-Learn and StatsModels, 365 Careers
- Learning R, Richard Cotton
- Getting started with SAS Enterprise miner 14.1
- Essential statistics using SAS university edition, Geoff Der, Brian S. Everitt
- R programming fundamentals, deal with data using various modeling techniques, Kaelen Medeiros
- R in action, data analysis and graphics with R and Tidyverse, Robert I. Kabacoff
- Statistical programming With SAS/IML software, Rick Wicklin
- A gentle introduction to statistics using SASʼ Studio in the cloud, Ron Cody
- Statistical analysis with R, by Joseph Schmuller, PhD
- R All-in-One, by Joseph Schmuller
- Statistics and mathematics for data science and data analytics
- Statistical Computing in C++ and R, Eubank, Randall
- Statistical analysis using Excel LiveLessons, by Conrad Carlberg, Volume 1
- Statistical analysis with Excel for dummies, by Joseph Schmuller
- Gnuplot in action, understanding data with graphs, Philipp K. Janert
- Statistik mit R, eine praxisorientierte Einführung in R, Joachim Zuckarelli
- Minitab cookbook, over 110 practical recipes to explore the vast array of statistics in Minitab 17, Isaac Newton
- R, kurz & gut, Jörg Staudemeyer, Ralf C. Staudemeyer
- Statistical analysis using Excel LiveLessons, by Conrad Carlberg, Volume 2
- The art of R programming, tour of statistical software design, Norman Matloff
- What's new in SAS 9.3
- SAS 9.4 Output Delivery System, 4th Edition, Institute, SAS
- Dēta saiensu no tame no tōkeigaku nyūmon, yosoku, bunrui, tōkei moderingu, tōkeiteki kikai gakushū to R/Python puroguramingu, Peter Bruce, Andrew Bruce, Peter Gedeck cho ; Kurokawa Toshiaki ; Ōhashi Shin'ya gijutsu kanshū = Practical statistics for data scientists : 50+ essential concepts using R and Python / Peter Bruce, Andrew Bruce, and Peter Gedeck
- Associations and correlations
- Developing credit risk models using SAS Enterprise Miner and SAS/STAT, theory and applications, Iain L.J. Brown
- SAS for R users, a book for budding data scientists, Ajay Ohri
- Base SAS 9.4 procedures guide
- R programming by example, practical, hands-on projects to help you get started with R, Omar Trejo Navarro
- Shu ju ke xue zhong de shi yong tong ji xue, Practical statistics for data scientists /, Bide Bulusi, Andelu Bulusi, Bide Gedeke zhu ; Chen Guangxin yi
- R projects for dummies, Joseph Schmuller
- Data scientists at work, Sebastian Gutierrez
- Statistical programming in SAS, A. John Bailer
- Statistik mit R Schnelleinstieg, R einfach lernen in 14 Tagen, Bjoern Walther
- R in action, data analysis and graphics with R, Robert I. Kabacoff
- Statistical analysis with Excel for dummies, Joseph Schmuller
- Python for data analysis, step-by-step with projects, Just into Data
- Access data analysis cookbook, by Ken Bluttman, Wayne S. Freeze
- Machine learning in R, automated algorithms for business analysis : applying K-Means clustering, decision trees, random forests, and neural networks, with Michael Grogan
- Getting started with SAS Enterprise Miner for machine learning, learning to perform segmentation and predictive modeling, with Jeff Thompson
- 120 quick Stata tips, Franz Buscha
- Access, analiza danych : receptury, Ken Bluttman, Wayne S. Freeze
- R 4 data science quick reference, a pocket guide to APIs, libraries, and packages, Thomas Mailund
- A Python data analyst's toolkit, learn Python and Python-based libraries with applications in data analysis and statistics, Gayathri Rajagopalan
- R in action, data analysis and graphics with R, Robert I. Kabacoff
Outgoing Resources
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