LEARNING PATH: Statistics and Data Mining for Data Science
- Offered byUDEMY
LEARNING PATH: Statistics and Data Mining for Data Science at UDEMY Overview
Duration | 6 hours |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Go to Website  |
Credential | Certificate |
LEARNING PATH: Statistics and Data Mining for Data Science at UDEMY Course details
- 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
- 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 t
LEARNING PATH: Statistics and Data Mining for Data Science at UDEMY Curriculum
Basic Statistics and Data Mining for Data Science
Preview
Basic Steps of Data Analysis
Measurement Level and Descriptive Statistics
Reasons for Summarizing Individual Variables
Obtaining Frequencies and Summary Statistics
Data Distributions
Visualizing Data
Preview
Statistical Outcomes
Chi-square Test Theory and Assumptions
Chi-square Test of Independence Example
Post-hoc Test Example
Clustered Bar Charts
Independent Samples T-Test Theory and Assumptions
Independent Samples T-Test Example
Paired Samples T-Test Theory and Assumptions
Preview
T-Test Error Bar Charts
One-way ANOVA Theory and Assumptions
One-way ANOVA Example
Post-hoc Test Example
ANOVA Error Bar Charts
Pearson Correlation Coefficient Theory and Assumptions
Pearson Correlation Coefficient Example
Scatterplots
Test Your Knowledge
Advanced Statistics and Data Mining for Data Science
Preview
Comparing and Contrasting Statistics and Data Mining
Comparing and Contrasting IBM SPSS Statistics and IBM SPSS Modeler
Types of Projects
Predictive Modeling Purpose, Examples, and Types
Characteristics and Examples of Statistical Predictive Models
Linear Regression Purpose, Formulas, and Demonstration
Linear Regression Assumptions
Characteristics and Examples of Decision Trees Models
CHAID Purpose and Theory
CHAID Demonstration
CHAID Interpretation
Characteristics and Examples of Machine Learning Models
Neural Network Purpose and Theory
Neural Network Demonstration
Comparing Models
Cluster Analysis Purpose Goals, and Applications
Cluster Analysis Basics
Cluster Analysis Models
K-Means Demonstration
K-Means Interpretation
Using Additional Fields to Create a Cluster Profile
Association Modeling Theory Examples and Objectives
Association Modeling Theory Basics and Applications
Demonstration Apriori Setup and Options
Demonstration Apriori Rule Interpretation
Demonstration Apriori with Tabular Data
Test Your Knowledge
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