Real world application of data analytics using R and Excel

Introduction To Data Analytics Applications

This course focuses on the application of data analytics in real world situations. Software tools such as R and Excel are used in the course. Software solutions using R and Excel are also provided.

Buy $39.00

Course curriculum

1. Introduction
1. Using Analytics For Decision Making
2. Data Analytics Overview
3. Types of Data Analytics
4. Data vs. Information
5. The Role of Databases
6. The Role Of Statistics In Developing Analytics
7. Data Types
8. Basic Business Analytics
9. Exploring: Data Analyst vs Data Scientist
10. Chapter Summary
1. Describing Data With Frequency Tables
2. Describing Data With Bar Charts and Pie Charts
3. Constructing Frequency Distributions
4. Graphic Presentation of a Distribution Using Histograms and Frequency Polygons
5. Chapter Summary
6. Software Solutuions
1. Describing Data With Numerical Measures
2. Calculating the Weighted Mean
3. Calculating The Geometric Mean
4. Calculating Range (A measure of dispersion)
5. Variance As A Measure Of Dispersion
6. Population Standard Deviation As A Measure Of Dispersion
7. Chebyshev’s Theorem And Standard Deviation
8. The Mean and Standard Deviation of Grouped Data
9. Chapter Summary
10. Software Solutions
1. Describing Data With Plots
2. Describing Data With Plots
3. Describing Data With Stem and Leaf Graphs
4. Measures of Position
5. Box Plots
6. Analysis of Skewness In Data Sets and Plots
7. Describing Relationship between Two Variables With Scatterplots
8. Contingency Tables
9. Chapter Summary
10. Software Solutions
1. Introduction
2. The Six-Step Procedure for Testing a Hypothesis
3. One-Tailed and Two-Tailed Hypothesis Tests
4. Hypothesis Testing for a Population Mean: Known Population Standard Deviation
5. A One-Tailed Hypothesis Test
6. p-Value in Hypothesis Testing
7. More On p-Values
8. Hypothesis Testing for a Population Mean: Population Standard Deviation Unknown
9. Type II Error
10. Chapter Summary
11. Software Solutions
12. Resources For Further Investigation
1. Two Sample Hypothesis Testing
2. Introduction
3. Two-Sample Tests of Hypothesis: Independent Samples
4. Additional Exercises
5. Comparing Population Means with Unknown Population Standard Deviations
6. Additional Exercises
7. Unequal Population Standard Deviations
8. Additional Exercises
9. Two-Sample Tests of Hypothesis: Dependent Samples
10. Comparing Dependent and Independent Samples
11. Additional Exercises
12. Chapter Summary
13. Software Solutions
1. Correlation And Regression
2. What is Correlation Analysis?
3. What is regression analysis?
4. The Correlation Coefficient
5. Testing the Significance of the Correlation Coefficient
6. Additional Correlation Exercises
7. Regression Analysis
8. Additional Regression Exercises
9. Testing the Significance of the Slope
10. Evaluating a Regression Equation’s Ability to Predict
11. Transforming Data
12. Chapter Summary
13. How To: Using Excel To Calculate Regression
14. Software Solutions
15. Additional Chapter Exercises
1. Multiple Variable Regression Analysis
2. Evaluating a Multiple Regression Equation
3. Coefficient of Multiple Determination
4. Inferences in Multiple Linear Regression
5. Evaluating Individual Regression Coefficients
6. Evaluating the Assumptions of Multiple Regression
7. Multicollinearity
8. Qualitative Independent Variables
9. Stepwise Regression
10. Chapter Summary
11. Software Solutions
1. Predictive Analytics: Definition, Importance, and Common Techniques
2. Step By Step Approach To Analytics Problem Solving
3. Time Series and Forecasting
4. Time series analysis examples
5. Note: Changes in Excel 2016 And Beyond
6. Time Series Analysis Types
7. Components of Time Series Data
8. Measures of Accuracy
9. Testing for Randomness
10. The Runs Test
11. Autocorrelation
12. Regression-Based Trend Models
13. The Random Walk Model
14. Moving Averages Forecasts
15. Exponential Smoothing Forecasts
16. Seasonal Models
17. Time Series Files
18. Chapter 9 Case Study

About this course

$39.00
106 lessons
0 hours of video content

Introduction To Data Analytics Applications

Course curriculum

Introduction

Chapter 1

Chapter 2

Chapter 3

Chapter 4

Chapter 5

Chapter 6

Chapter 7

Chapter 8

Chapter 9

About this course