| Brief | Book | |
|---|---|---|
| With an exciting new look, math diagnostic tool, and a research roadmap to navigate projects, this new edition of Andy Field’s award-winning text offers a unique combination of humor and step-by-step instruction to make learning statistics compelling and accessible to even the most anxious of students. The Fifth Edition takes students from initial theory to regression, factor analysis, and multilevel modeling, fully incorporating IBM SPSS Statistics© version 25 and fascinating examples throughout. |
||
Showing posts with label Statistics. Show all posts
Showing posts with label Statistics. Show all posts
Friday, August 21, 2020
Discovering Statistics Using IBM SPSS Statistics..
Sunday, August 16, 2020
Practical Statistics for Data Scientists...Using R and Python
| Book | Brief | |
|---|---|---|
| Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. |
||
Saturday, August 15, 2020
Practical Time Series Analysis...with Statistics and Machine Learning
| Brief | Book | |
|---|---|---|
| Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and machine learning techniques will increase.
Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challenges in time series, using both traditional statistical and modern machine learning techniques. Author Aileen Nielsen offers an accessible, well-rounded introduction to time series in both R and Python that will have data scientists, software engineers, and researchers up and running quickly. |
||
Saturday, August 1, 2020
IBM Analytics Series 4 of 5
Click on any of the book titles to gain more insight about the extraordinary benefits
Subscribe to:
Posts (Atom)