Learning Path Statistics And Data Mining For Data Science

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
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.
Overview
Section 1: Basic Statistics and Data Mining for Data Science
Lecture 1 The Course Overview
Lecture 2 Basic Steps of Data Analysis
Lecture 3 Measurement Level and Descriptive Statistics
Lecture 4 Reasons for Summarizing Individual Variables
Lecture 5 Obtaining Frequencies and Summary Statistics
Lecture 6 Data Distributions
Lecture 7 Visualizing Data
Lecture 8 Hypothesis Testing and Probability
Lecture 9 Statistical Outcomes
Lecture 10 Chi-square Test Theory and Assumptions
Lecture 11 Chi-square Test of Independence Example
Lecture 12 Post-hoc Test Example
Lecture 13 Clustered Bar Charts
Lecture 14 Independent Samples T-Test: Theory and Assumptions
Lecture 15 Independent Samples T-Test Example
Lecture 16 Paired Samples T-Test: Theory and Assumptions
Lecture 17 Paired Samples T-Test Example
Lecture 18 T-Test Error Bar Charts
Lecture 19 One-way ANOVA Theory and Assumptions
Lecture 20 One-way ANOVA Example
Lecture 21 Post-hoc Test Example
Lecture 22 ANOVA Error Bar Charts
Lecture 23 Pearson Correlation Coefficient Theory and Assumptions
Lecture 24 Pearson Correlation Coefficient Example
Lecture 25 Scatterplots
Section 2: Advanced Statistics and Data Mining for Data Science
Lecture 26 The Course Overview
Lecture 27 Comparing and Contrasting Statistics and Data Mining
Lecture 28 Comparing and Contrasting IBM SPSS Statistics and IBM SPSS Modeler
Lecture 29 Types of Projects
Lecture 30 Predictive Modeling: Purpose, Examples, and Types
Lecture 31 Characteristics and Examples of Statistical Predictive Models
Lecture 32 Linear Regression: Purpose, Formulas, and Demonstration
Lecture 33 Linear Regression: Assumptions
Lecture 34 Characteristics and Examples of Decision Trees Models
Lecture 35 CHAID: Purpose and Theory
Lecture 36 CHAID Demonstration
Lecture 37 CHAID Interpretation
Lecture 38 Characteristics and Examples of Machine Learning Models
Lecture 39 Neural Network: Purpose and Theory
Lecture 40 Neural Network Demonstration
Lecture 41 Comparing Models
Lecture 42 Cluster Analysis: Purpose Goals, and Applications
Lecture 43 Cluster Analysis: Basics
Lecture 44 Cluster Analysis: Models
Lecture 45 K-Means Demonstration
Lecture 46 K-Means Interpretation
Lecture 47 Using Additional Fields to Create a Cluster Profile
Lecture 48 Association Modeling Theory: Examples and Objectives
Lecture 49 Association Modeling Theory: Basics and Applications
Lecture 50 Demonstration: Apriori Setup and Options
Lecture 51 Demonstration: Apriori Rule Interpretation
Lecture 52 Demonstration: Apriori with Tabular Data
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.


Homepage

https://www.udemy.com/course/learning-path-ibm-spss-data-science-with-ibm-spss/