Essential Statistics for Non-STEM Data Analysts Get to Grips with the Statistics and Math Knowledge Needed to Enter the World of Data Science with Python.
Description
Put your data science knowledge to work with this practical guide to statistics. You'll understand the working mechanism of each method used and find out how data science algorithms function. This book will help you learn the statistical techniques required for key model building and functioning using Python.
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Format
Language
English
ISBN
9781838987565, 1838987568
Notes
General Note
Description based upon print version of record.
General Note
Learning about joint and conditional distribution.
Description
Put your data science knowledge to work with this practical guide to statistics. You'll understand the working mechanism of each method used and find out how data science algorithms function. This book will help you learn the statistical techniques required for key model building and functioning using Python.
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O'Reilly O'Reilly Online Learning: Academic/Public Library Edition
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Citations
APA Citation, 7th Edition (style guide)
Li, R. (2020). Essential Statistics for Non-STEM Data Analysts: Get to Grips with the Statistics and Math Knowledge Needed to Enter the World of Data Science with Python . Packt Publishing, Limited.
Chicago / Turabian - Author Date Citation, 17th Edition (style guide)Li, Rongpeng. 2020. Essential Statistics for Non-STEM Data Analysts: Get to Grips With the Statistics and Math Knowledge Needed to Enter the World of Data Science With Python. Birmingham: Packt Publishing, Limited.
Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)Li, Rongpeng. Essential Statistics for Non-STEM Data Analysts: Get to Grips With the Statistics and Math Knowledge Needed to Enter the World of Data Science With Python Birmingham: Packt Publishing, Limited, 2020.
Harvard Citation (style guide)Li, R. (2020). Essential statistics for non-STEM data analysts: get to grips with the statistics and math knowledge needed to enter the world of data science with python. Birmingham: Packt Publishing, Limited.
MLA Citation, 9th Edition (style guide)Li, Rongpeng. Essential Statistics for Non-STEM Data Analysts: Get to Grips With the Statistics and Math Knowledge Needed to Enter the World of Data Science With Python Packt Publishing, Limited, 2020.
Note! Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy. Citation formats are based on standards as of August 2021.
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Grouped Work ID
962d353c-fa46-93bf-65eb-51215b5df0bd-eng
Grouping Information
| Grouped Work ID | 962d353c-fa46-93bf-65eb-51215b5df0bd-eng |
|---|---|
| Full title | essential statistics for non stem data analysts get to grips with the statistics and math knowledge needed to enter the world of data science with python |
| Author | li rongpeng |
| Grouping Category | book |
| Last Update | 2025-01-24 12:33:29PM |
| Last Indexed | 2025-05-03 03:24:52AM |
Book Cover Information
| Image Source | syndetics |
|---|---|
| First Loaded | Aug 5, 2023 |
| Last Used | Mar 21, 2025 |
Marc Record
| First Detected | Mar 21, 2023 11:10:08 AM |
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| Last File Modification Time | Dec 17, 2024 08:13:31 AM |
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| 505 | 0 | |a Cover -- Title Page -- Copyright and Credits -- About Packt -- Contributors -- Table of Contents -- Preface -- Section 1: Getting Started with Statistics for Data Science -- Chapter 1: Fundamentals of Data Collection, Cleaning, and Preprocessing -- Technical requirements -- Collecting data from various data sources -- Reading data directly from files -- Obtaining data from an API -- Obtaining data from scratch -- Data imputation -- Preparing the dataset for imputation -- Imputation with mean or median values -- Imputation with the mode/most frequent value -- Outlier removal | |
| 505 | 8 | |a Data standardization -- when and how -- Examples involving the scikit-learn preprocessing module -- Imputation -- Standardization -- Summary -- Chapter 2: Essential Statistics for Data Assessment -- Classifying numerical and categorical variables -- Distinguishing between numerical and categorical variables -- Understanding mean, median, and mode -- Mean -- Median -- Mode -- Learning about variance, standard deviation, quartiles,percentiles, and skewness -- Variance -- Standard deviation -- Quartiles -- Skewness -- Knowing how to handle categorical variables and mixed data types | |
| 505 | 8 | |a Frequencies and proportions -- Transforming a continuous variable to a categorical one -- Using bivariate and multivariate descriptive statistics -- Covariance -- Cross-tabulation -- Summary -- Chapter 3: Visualization with Statistical Graphs -- Basic examples with the Python Matplotlib package -- Elements of a statistical graph -- Exploring important types of plotting in Matplotlib -- Advanced visualization customization -- Customizing the geometry -- Customizing the aesthetics -- Query-oriented statistical plotting -- Example 1 -- preparing data to fit the plotting function API | |
| 505 | 8 | |a Example 2 -- combining analysis with plain plotting -- Presentation-ready plotting tips -- Use styling -- Font matters a lot -- Summary -- Section 2: Essentials of Statistical Analysis -- Chapter 4: Sampling and Inferential Statistics -- Understanding fundamental concepts in sampling techniques -- Performing proper sampling under different scenarios -- The dangers associated with non-probability sampling -- Probability sampling -- the safer approach -- Understanding statistics associated with sampling -- Sampling distribution of the sample mean -- Standard error of the sample mean | |
| 505 | 8 | |a The central limit theorem -- Summary -- Chapter 5: Common Probability Distributions -- Understanding important concepts in probability -- Events and sample space -- The probability mass function and the probability density function -- Subjective probability and empirical probability -- Understanding common discrete probability distributions -- Bernoulli distribution -- Binomial distribution -- Poisson distribution -- Understanding the common continuous probability distribution -- Uniform distribution -- Exponential distribution -- Normal distribution | |
| 520 | |a Put your data science knowledge to work with this practical guide to statistics. You'll understand the working mechanism of each method used and find out how data science algorithms function. This book will help you learn the statistical techniques required for key model building and functioning using Python. | ||
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