LEARNING PATH: Statistics and Data Mining for Data Science
What you'll learn
- Get familiar with the basics of analyzing data
- Exploring the importance of summarizing individual variables
- Use inferential statistics and know when to perform the Chi-Square test
- Get well-versed with correlations
- Differentiate between the various types of predictive models
- Master linear regression and explore the results of a decision tree
- Understand when to perform cluster analysis and work with neural networks
Course content
- Preview03:31
- 13:26Basic Steps of Data Analysis
- 11:03Measurement Level and Descriptive Statistics
- 05:00Reasons for Summarizing Individual Variables
- 12:18Obtaining Frequencies and Summary Statistics
- 09:44Data Distributions
- 14:21Visualizing Data
- Preview08:05
- 03:19Statistical Outcomes
- 13:51Chi-square Test Theory and Assumptions
- 04:51Chi-square Test of Independence Example
- 03:54Post-hoc Test Example
- 02:48Clustered Bar Charts
- 09:38Independent Samples T-Test: Theory and Assumptions
- 05:48Independent Samples T-Test Example
- 05:49Paired Samples T-Test: Theory and Assumptions
- Preview02:18
- 02:35T-Test Error Bar Charts
- 08:47One-way ANOVA Theory and Assumptions
- 05:04One-way ANOVA Example
- 08:18Post-hoc Test Example
- 02:48ANOVA Error Bar Charts
- 10:11Pearson Correlation Coefficient Theory and Assumptions
- 04:03Pearson Correlation Coefficient Example
- 06:26Scatterplots
- 5 questionsTest Your Knowledge
Requirements
- Basic knowledge of data science is assumed
Description
Data Science is an ever-evolving field. Data Science includes techniques and theories extracted from statistics, computer science, and machine learning. This video learning path will be your companion as you master the various data mining and statistical techniques in data science.
The first part of this course introduces you to the concept of data science, and explains the steps to analyse data and identify which summary statistics are relevant to the type of data you are summarizing. You will also be introduced to the idea of inferential statistics, probability, and hypothesis testing. You will then learn you will learn how to perform and interpret the results of basic statistical analyses such as chi-square, independent and paired sample t-tests, one-way ANOVA, etc. as well as using graphical displays such as bar charts and scatter plots.
The latter part of this course provides an overview of the various types of projects data scientists usually encounter. You will be introduced to the three methods (statistical, decision tree, and machine learning) with which you can perform predictive modelling. You will explore segmentation modelling to learn the art of cluster analysis, and will work with association modelling to perform market basket analysis using real-world examples.
By the end of this Learning Path, you will gain a firm knowledge on data analysis, data mining, and statistical analysis and be able to implement these powerful techniques on your data with ease.
Meet Your Expert(s):
We have the best works of the following esteemed author to ensure that your learning journey is smooth:
Jesus Salcedo has a PhD in Psychometrics from Fordham University. He is an independent statistical and data-mining consultant that has been analyzing data for over 20 years. He is a former SPSS Curriculum Team Lead and Senior Education Specialist who has written numerous SPSS training courses and trained thousands of users.
Who this course is for:
- This course is for developers, budding data scientists as well as data analysts who are interested in entering the field of data science and are looking for a guide to understanding the basic as well as advanced statistical and data mining concepts.
Instructor
![Packt Publishing](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64"%3E%3C/svg%3E){kind=avatar}
- 3.9 Instructor Rating
- 59,147 Reviews
- 354,624 Students
- 1,418 Courses
Packt has been committed to developer learning since 2004. A lot has changed in software since then - but Packt has remained responsive to these changes, continuing to look forward at the trends and tools defining the way we work and live. And how to put them to work.
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