Course Overview
If you wish to enter the world of statistics and data mining, then look no further because this practical video course will walk you through the basics as well as the advanced concepts in a step-by-step manner.
Course Objectives:
- 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 Topics
Course Overview – Course Length – 5:51 hours
Data science is an ever-evolving field, with an exponentially growing popularity. It includes techniques and theories based on the fields of statistics, computer science, and most importantly machine learning, databases, and visualization. If you wish to enter the world of statistics and data mining, then look no further because this practical video course will walk you through the basics as well as the advanced concepts in a step-by-step manner.
The highlights of this Learning Path are:
• Learn when to use different statistical techniques, how to set up different analyses, and how to interpret the results.
• Apply statistical and data mining techniques to analyze and interpret results using CHAID, linear regression, and neural networks.
This Learning Path begins with explaining the steps to analyze data and identify which summary statistics are relevant to the type of data you are summarizing. You will then learn several procedures, such as how to run and interpret frequencies and how to create various graphs. You will also be introduced to the idea of inferential statistics, probability, and hypothesis testing.
Next, 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, post-hoc tests, and bivariate correlations and graphical displays such as clustered bar charts, error bar charts, and scatter plots. You will then learn how to use different statistical techniques, set up different analyses, and interpret the results.
Moving ahead, this Learning Path shows the comparing and contrasting between statistics and data mining and then provides an overview of the various types of projects data scientists usually encounter. Next, you will be introduced to the three methods (statistical, decision tree, and machine learning) with which you can perform predictive modeling. Finally, you will explore segmentation modeling to learn the art of cluster analysis and will work with association modeling to perform market basket analysis.
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.
