LEARNING PATH: Statistics and Data Mining for Data Science هذه الدورة تدرس باللغة الإنجليزية

تفاصيل الدورة

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.