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Development Data Science

LEARNING PATH: Statistics and Data Mining for Data Science

Dive deep into the statistical and data mining techniques to get useful insights out of your data
Rating: 4.1 out of 54.1 (7 ratings)
66 students
Created by Packt Publishing
Last updated 6/2018
English
English [Auto]
30-Day Money-Back Guarantee

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

2 sections • 52 lectures • 5h 51m total length

  • Preview03:31
  • Basic Steps of Data Analysis
    13:26
  • Measurement Level and Descriptive Statistics
    11:03
  • Reasons for Summarizing Individual Variables
    05:00
  • Obtaining Frequencies and Summary Statistics
    12:18
  • Data Distributions
    09:44
  • Visualizing Data
    14:21
  • Preview08:05
  • Statistical Outcomes
    03:19
  • Chi-square Test Theory and Assumptions
    13:51
  • Chi-square Test of Independence Example
    04:51
  • Post-hoc Test Example
    03:54
  • Clustered Bar Charts
    02:48
  • Independent Samples T-Test: Theory and Assumptions
    09:38
  • Independent Samples T-Test Example
    05:48
  • Paired Samples T-Test: Theory and Assumptions
    05:49
  • Preview02:18
  • T-Test Error Bar Charts
    02:35
  • One-way ANOVA Theory and Assumptions
    08:47
  • One-way ANOVA Example
    05:04
  • Post-hoc Test Example
    08:18
  • ANOVA Error Bar Charts
    02:48
  • Pearson Correlation Coefficient Theory and Assumptions
    10:11
  • Pearson Correlation Coefficient Example
    04:03
  • Scatterplots
    06:26
  • Test Your Knowledge
    5 questions

  • Preview03:01
  • Comparing and Contrasting Statistics and Data Mining
    11:20
  • Comparing and Contrasting IBM SPSS Statistics and IBM SPSS Modeler
    11:38
  • Types of Projects
    03:58
  • Predictive Modeling: Purpose, Examples, and Types
    04:37
  • Characteristics and Examples of Statistical Predictive Models
    02:12
  • Linear Regression: Purpose, Formulas, and Demonstration
    10:00
  • Linear Regression: Assumptions
    05:53
  • Characteristics and Examples of Decision Trees Models
    02:27
  • CHAID: Purpose and Theory
    02:49
  • CHAID Demonstration
    05:45
  • CHAID Interpretation
    09:38
  • Characteristics and Examples of Machine Learning Models
    02:23
  • Neural Network: Purpose and Theory
    04:29
  • Neural Network Demonstration
    07:29
  • Comparing Models
    05:45
  • Cluster Analysis: Purpose Goals, and Applications
    05:48
  • Cluster Analysis: Basics
    12:10
  • Cluster Analysis: Models
    04:12
  • K-Means Demonstration
    08:35
  • K-Means Interpretation
    07:52
  • Using Additional Fields to Create a Cluster Profile
    06:18
  • Association Modeling Theory: Examples and Objectives
    07:24
  • Association Modeling Theory: Basics and Applications
    07:58
  • Demonstration: Apriori Setup and Options
    07:59
  • Demonstration: Apriori Rule Interpretation
    04:50
  • Demonstration: Apriori with Tabular Data
    06:50
  • Test Your Knowledge
    5 questions

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
Tech Knowledge in Motion
Packt Publishing
  • 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.

With an extensive library of content - more than 4000 books and video courses -Packt's mission is to help developers stay relevant in a rapidly changing world. From new web frameworks and programming languages, to cutting edge data analytics, and DevOps, Packt takes software professionals in every field to what's important to them now.

From skills that will help you to develop and future proof your career to immediate solutions to every day tech challenges, Packt is a go-to resource to make you a better, smarter developer.

Packt Udemy courses continue this tradition, bringing you comprehensive yet concise video courses straight from the experts.



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