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PluralSight – Building Statistical Summaries With R

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PluralSight – Building Statistical Summaries With R-JGTiSO
English | Size: 360.13 MB
Category: Programing


This course covers inferential statistics techniques. Learn advanced techniques to compare means across categories, predictive models for regression and classification, and A/B testing to perform randomized experiments on two versions of a variable. [Read more…]

Pluralsight – Interpreting Data with Statistical Models

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Pluralsight – Interpreting Data with Statistical Models
English | Size: 230.16 MB
Category: Tutorial


Data is everywhere, from the newspaper you read on the subway to the report you are using to analyze yesterday’s stock market performance. In this course, Interpreting Data with Statistical Models, you will gain the ability to effectively understand how to tackle problems that appear at your work, understand which is the right statistical analysis to use, and how to interpret the results to obtain insights. First, you will learn the very basics of statistics. Next, you will discover hypothesis testing to compare variables. Finally, you will explore how to make multiple comparisons and detect functional relationships with ANOVA and Regression. When you’re finished with this course, you will have the skills and knowledge of data analysis and statistical models needed to make your data speak for itself. [Read more…]

Packt Publishing – Fundamentals of Statistical Modeling and Machine Learning Techniques

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Packt Publishing – Fundamentals of Statistical Modeling and Machine Learning Techniques
English | Size: 386.61 MB
Category: Tutorial


Complex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This video will teach you all it takes to perform complex statistical computations required for Machine Learning. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. We will discuss the application of frequently used algorithms on various domain problems, using both Python and R programming. We will use libraries such as scikit-learn, NumPy, random Forest and so on. By the end of the course, you will have mastered the required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problem. [Read more…]

Packt – Deep Dive into Statistical Modeling with R

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Packt – Deep Dive into Statistical Modeling with R
English | Size: 421.49 MB
Category: Tutorial


R is a data analysis tool, graphical environment, and programming language. Without any prior experience in programming or statistical software, this video tutorial will help you quickly become a knowledgeable user of R. Now is the time to take control of your data and start producing superior statistical analysis with R. [Read more…]

Packt Publishing – Deep Dive into Statistical Modeling with R

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Packt Publishing – Deep Dive into Statistical Modeling with R
English | Size: 421.50 MB
Category: CBTs


R is a data analysis tool, graphical environment, and programming language. Without any prior experience in programming or statistical software, this video tutorial will help you quickly become a knowledgeable user of R. Now is the time to take control of your data and start producing superior statistical analysis with R.
[Read more…]

Coursera – Data Analysis and Statistical Inference

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Coursera – Data Analysis and Statistical Inference

English | Size: 1.28 GB (1,376,028,414 Bytes)
Category: CBTs


Recognize the importance of data collection, identify limitations in data collection methods, and determine how they affect the scope of inference.

Use statistical software (R) to summarize data numerically and visually, and to perform data analysis.
Have a conceptual understanding of the unified nature of statistical inference.
Apply estimation and testing methods (confidence intervals and hypothesis tests) to analyze single variables and the relationship between two variables in order to understand natural phenomena and make data-based decisions. [Read more…]