MASTER DATA ANALYTICS

& PYTHON

Overview

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Build a strong foundation in data analytics and coding with Python with the Applied Statistics for Data Analytics with Python zyBook.

Specifically, you will learn more about the concepts needed to understand and utilise data techniques. This includes descriptive statistics, probability, and inferential statistics.

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Studies show that students learn 118% more with the minimal text model in only one lesson.

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On average, zyBook users improved grades up to ⅔ and read 74% more than users of regular textbooks.

What is a zyBook?

In short, zyBooks are interactive, digital textbooks.

 

By incorporating available technology, zyBooks make learning fun, interactive and engaging – all while drastically reducing the time spent on learning theoretical concepts.

Course Details

The Applied Statistics for Data Analytics with Python zyBook covers data analytics concepts with minimal text, maximum interactivity.

Module 1: Data Visualisation


1.1 What is data? 1.2 What is data visualisation? 1.3 Python for data visualisation 1.4 Data frames 1.5 Bar charts 1.6 Pie charts 1.7 Scatter plots 1.8 Line charts




Module 2: Descriptive Statistics


2.1 Survey sampling 2.2 Measures of center 2.3 Measures of variability 2.4 Box plots 2.5 Histograms 2.6 Violin plots




Module 3: Probability and Counting


3.1 Introduction to probability 3.2 Addition rule and complements 3.3 Multiplication rule and independence 3.4 Conditional probability and Bayes' Theorem 3.5 Combinations and permutations




Module 4: Probability Distributions


4.1 Introduction to random variables 4.2 Properties of discrete probability distributions 4.3 Binomial distribution 4.4 Hypergeometric distribution 4.5 Poisson distribution 4.6 Properties of continuous probability distributions 4.7 The normal distribution 4.8 The Student's t-Distribution 4.9 The F-distribution 4.10 Chi-square distribution




Module 7: Time Series


7.1 What is a time series? 7.2 Time series patterns and stationarity 7.3 Moving average and exponential smoothing forecasting 7.4 Forecasting using regression




Module 6: Linear Regression


6.1 Introduction to simple linear regression (SLR) 6.2 SLR assumptions 6.3 Correlation and coefficient of determination 6.4 Interpreting SLR models 6.5 Testing SLR parameters 6.6 Multiple regression 6.7 Categorical predictors and non-linear relationships




Module 5: Inferential Statistics


5.1 Confidence intervals 5.2 Confidence intervals for population means 5.3 Confidence intervals for population proportions 5.4 Hypothesis tests 5.5 One-sample hypothesis tests for population means 5.6 One-sample z-test for population proportions 5.7 Two-sample hypothesis tests for population means 5.8 Two-sample z-test for population proportions 5.9 Analysis of variance (ANOVA) 5.10 Chi-square tests for categorical variables




Module 11: Appendix


11.1 t-distribution table 11.2 z-distribution table 11.3 Chi-squared distribution table




Module 10: Ethics


10.1 Misleading statistics 10.2 Abuse of the p-value 10.3 Data privacy 10.4 Ethical guidelines




Module 9: Data Mining


9.1 What is data mining? 9.2 Data preparation 9.3 Analysing results 9.4 Supervised learning 9.5 Unsupervised learning




Module 8: Monte Carlo Methods


8.1 What is a Monte Carlo simulation? 8.2 Building simulations 8.3 Optimisation and forecasting





Pricing

Each purchase comes with...

Highly effective reading materials

Interactive figures & tables

Practice questions

1 year access

What Our Students Say

“I really enjoyed zyBooks for use in my Python class. It has surely aided my success in class and helped me build some confidence in my first year at university.”
 

Isaac C.

Cal State University, Long Beach

Recommended Combinations

The Applied Statistics for Data Analytics with Python zyBook will pair well with the following:

Frequently Asked Questions

Module 1: Data Visualisation


1.1 What is data? 1.2 What is data visualisation? 1.3 Python for data visualisation 1.4 Data frames 1.5 Bar charts 1.6 Pie charts 1.7 Scatter plots 1.8 Line charts




Module 2: Descriptive Statistics


2.1 Survey sampling 2.2 Measures of center 2.3 Measures of variability 2.4 Box plots 2.5 Histograms 2.6 Violin plots




Module 3: Probability and Counting


3.1 Introduction to probability 3.2 Addition rule and complements 3.3 Multiplication rule and independence 3.4 Conditional probability and Bayes' Theorem 3.5 Combinations and permutations




Module 4: Probability Distributions


4.1 Introduction to random variables 4.2 Properties of discrete probability distributions 4.3 Binomial distribution 4.4 Hypergeometric distribution 4.5 Poisson distribution 4.6 Properties of continuous probability distributions 4.7 The normal distribution 4.8 The Student's t-Distribution 4.9 The F-distribution 4.10 Chi-square distribution




Module 7: Time Series


7.1 What is a time series? 7.2 Time series patterns and stationarity 7.3 Moving average and exponential smoothing forecasting 7.4 Forecasting using regression




Module 6: Linear Regression


6.1 Introduction to simple linear regression (SLR) 6.2 SLR assumptions 6.3 Correlation and coefficient of determination 6.4 Interpreting SLR models 6.5 Testing SLR parameters 6.6 Multiple regression 6.7 Categorical predictors and non-linear relationships




Module 5: Inferential Statistics


5.1 Confidence intervals 5.2 Confidence intervals for population means 5.3 Confidence intervals for population proportions 5.4 Hypothesis tests 5.5 One-sample hypothesis tests for population means 5.6 One-sample z-test for population proportions 5.7 Two-sample hypothesis tests for population means 5.8 Two-sample z-test for population proportions 5.9 Analysis of variance (ANOVA) 5.10 Chi-square tests for categorical variables




Module 11: Appendix


11.1 t-distribution table 11.2 z-distribution table 11.3 Chi-squared distribution table




Module 10: Ethics


10.1 Misleading statistics 10.2 Abuse of the p-value 10.3 Data privacy 10.4 Ethical guidelines




Module 9: Data Mining


9.1 What is data mining? 9.2 Data preparation 9.3 Analysing results 9.4 Supervised learning 9.5 Unsupervised learning




Module 8: Monte Carlo Methods


8.1 What is a Monte Carlo simulation? 8.2 Building simulations 8.3 Optimisation and forecasting