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Basic Statistics and Data Mining for Data Science
Rating: 4.3 out of 5(36 ratings)
219 students

Basic Statistics and Data Mining for Data Science

Enter the world of Statistics, Data Analysis and Data Science!
Created byPackt Publishing
Last updated 1/2018
English

What you'll learn

  • Get familiar with the basics of analyzing data
  • Exploring the importance of summarizing individual variables
  • Use inferential statistics
  • Know when to perform the Chi-Square test
  • Differentiate between independent and paired samples t-tests
  • Understand when to use a one-way ANOVA and post-hoc tests
  • Get well-versed with correlations

Course content

7 sections • 25 lectures • 2h 57m total length

  • The Course Overview3:31

    This video provides an overview of the entire course.                      

  • Basic Steps of Data Analysis13:26

    This video discusses the basic steps to analyze data topics.

  • Measurement Level and Descriptive Statistics11:03

    This video discusses the measurement level and descriptive statistics.

  • Assessment

Requirements

  • This course is for developers who are interested in entering the field of data science and are looking for a guide to the statistical concepts.

Description

Data science is an ever-evolving field, with exponentially growing popularity. Data science includes techniques and theories extracted from the fields of statistics, computer science, and most importantly machine learning, databases, and visualization.

This video course consists of step-by-step introductions to analyze data and the basics of statistics. The first chapter focuses on the steps to analyze data and which summary statistics are relevant given the type of data you are summarizing. The second chapter continues by focusing on summarizing individual variables and specifically some of the reasons users need to summarize variables. This chapter also illustrates several procedures, such as how to run and interpret frequencies and how to create various graphs. The third chapter introduces the idea of inferential statistics, probability, and hypothesis testing.

The rest of the chapters show you how to perform and interpret the results of basic statistical analyses (chi-square, independent and paired sample t-tests, one-way ANOVA, post-hoc tests, and bivariate correlations) and graphical displays (clustered bar charts, error bar charts, and scatterplots). You will also learn when to use different statistical techniques, how to set up different analyses, and how to interpret the results.

About the Author

Jesus Salcedo has a Ph.D. in Psychometrics from Fordham University. He is an independent statistical consultant that and has been using SPSS products 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 is an application-oriented course with a practical approach. This course discusses situations in which you would use each statistical technique, the assumptions made by the method, how to set up the analysis, and how to interpret the results. No proofs will be derived, but rather the focus will be on the practical matters of data analysis in support of answering research questions.

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