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

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.