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Video
Explore statistical analysis with SPSS. Topics covered include how to create and analyze charts, build reports, import spreadsheets, create regression models, and export presentation graphics.

ebook
Linear Regression Using R: An Introduction to Data Modeling presents one of the fundamental data modeling techniques in an informal tutorial style. Learn how to predict system outputs from measured data using a detailed stepbystep process to develop, train, and test reliable regression models. Key modeling and programming concepts are intuitively described using the R programming language. All of the necessary resources are freely available online.
 Subjects:
 Mathematics and Statistics
 Keywords:
 Textbooks Linguistics  Statistical methods R (Computer program language) Mathematical linguistics
 Resource Type:
 ebook

ebook
Think Bayes is an introduction to Bayesian statistics using computational methods. The premise of this book, and the other books in the Think X series, is that if you know how to program, you can use that skill to learn other topics. Most books on Bayesian statistics use mathematical notation and present ideas in terms of mathematical concepts like calculus. This book uses Python code instead of math, and discrete approximations instead of continuous mathematics. As a result, what would be an integral in a math book becomes a summation, and most operations on probability distributions are simple loops. I think this presentation is easier to understand, at least for people with programming skills. It is also more general, because when we make modeling decisions, we can choose the most appropriate model without worrying too much about whether the model lends itself to conventional analysis. Also, it provides a smooth development path from simple examples to realworld problems.
 Subjects:
 Computing and Mathematics and Statistics
 Keywords:
 Bayesian statistical decision theory Python (Computer program language) Textbooks
 Resource Type:
 ebook

MOOC
Data science has critical applications across most industries, and is one of the most indemand careers in computer science. Data scientists are the detectives of the big data era, responsible for unearthing valuable data insights through analysis of massive datasets. And just like a detective is responsible for finding clues, interpreting them, and ultimately arguing their case in court, the field of data science encompasses the entire data life cycle. That starts with capturing lots of raw data using data collection techniques, and then building and maintaining data pipelines and data warehouses that efficiently “clean” the data and make it accessible for analysis at scale. This data infrastructure allows data scientists to efficiently process datasets using data mining and data modeling skills, as well as analyze these outputs with sophisticated techniques like predictive analysis and qualitative analysis. Finally, these findings must be presented using data visualization and data reporting skills to help business decision makers. Depending on the size of the company, data scientists may be responsible for this entire data life cycle, or they might specialize in a particular portion of the life cycle as part of a larger data science team
 Subjects:
 Computing
 Keywords:
 Machine learning Data mining Big data
 Resource Type:
 MOOC

MOOC
This course covers a wide variety of topics in machine learning and statistical modeling. While mathematical methods and theoretical aspects will be covered, the primary goal is to provide students with the tools and principles needed to solve the data science problems found in practice. This course also serves as a foundation on which more specialized courses and further independent study can build. This course was designed as part of the core curriculum for the Center for Data Science's Masters degree in Data Science. Other interested students who satisfy the prerequisites are welcome to take the class as well. Note that class is intended as a continuation of DSGA1001 Intro to Data Science, which covers some important, fundamental data science topics that may not be explicitly covered in this DSGA class (e.g. data cleaning, crossvalidation, and sampling bias).
 Course related:
 LGT6801 Guided Study in Logistics I
 Subjects:
 Computing and Mathematics and Statistics
 Keywords:
 Big data Data mining Machine learning Mathematical statistics  Data processing
 Resource Type:
 MOOC