Log in
Log inBook a demo

Basic Statistics and Data Mining for Data Science

COURSE
Packt Admin
3 hrs

Basic Statistics and Data Mining for Data Science

COURSE
Packt Admin
3 hrs
Included in GO1 PremiumStarting from $12 per user for teamsLearn moreTry for free
Included in GO1 PremiumStarting from $12 per user for teamsLearn moreTry for free

Course Overview 

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. Style and Approach: 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.

Target Audience 

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.

Learning Objectives 

  • 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 

Business Outcomes 

  • This comprehensive video tutorial will ensure that you build on your knowledge of statistics and learn how to apply it in the field of data science
  • Youll learn when to use different statistical techniques, how to set up different analyses, and how to interpret the results
  • This video course follows a step-by-step approach to ensure that you get the basics right 
Learning
Section 1: The Basics of Analyzing Data
1.1 The Course Overviewvideo
1.2 Basic Steps of Data Analysisvideo
1.3 Measurement Level and Descriptive Statisticsvideo
Assessmentquiz
Section 2: Summarizing Individual Variables
2.1 Reasons for Summarizing Individual Variablesvideo
2.2 Obtaining Frequencies and Summary Statisticsvideo
2.3 Data Distributionsvideo
2.4 Visualizing Datavideo
Assessmentquiz
Section 3: Understanding Inferential Statistics
3.1 Hypothesis Testing and Probabilityvideo
3.2 Statistical Outcomesvideo
Assessmentquiz
Section 4: Digging into Chi-square Tests of Independence
4.1 Chi-square Test Theory and Assumptionsvideo
4.2 Chi-square Test of Independence Examplevideo
4.3 Post-hoc Test Examplevideo
4.4 Clustered Bar Chartsvideo
Assessmentquiz
Section 5: Performing T-Tests
5.1 Independent Samples T-Test: Theory and Assumptionsvideo
5.2 Independent Samples T-Test Examplevideo
5.3 Paired Samples T-Test: Theory and Assumptionsvideo
5.4 Paired Samples T-Test Examplevideo
5.5 T-Test Error Bar Chartsvideo
Assessmentquiz
Section 6: Exploring ANOVA
6.1 One-way ANOVA Theory and Assumptionsvideo
6.2 One-way ANOVA Examplevideo
6.3 Post-hoc Test Examplevideo
6.4 ANOVA Error Bar Chartsvideo
Assessmentquiz
Section 7: Working with Correlation
7.1 Pearson Correlation Coefficient Theory and Assumptionsvideo
7.2 Pearson Correlation Coefficient Examplevideo
7.3 Scatterplotsvideo
Assessmentquiz