Data Analytics for Transportation Engineers

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Parth Loya, M.S.

2:03:32

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1. Introduction to Linear Regression using MS Excel

1. Problem for Data Modeling - Parking Duration vs Building Area And Number of Floors.mp4

01:48

2. Check your concepts.html

3. What is a Variable What are Independent And Dependent Variables.html

4.1 Parking Duration Data.xlsx

4. Inserting and Interpreting Trendline in MS Excel.mp4

06:49

5. Check your progress.html

6. Finding Residuals.mp4

07:10

7. Check your progress.html

8. What is Mean Variance and Standard Deviation.mp4

02:11

9. Check your progress.html

10.1 Test Scores.xlsx

10. What are Distributions in Statistics.html

11.1 Variance Lecture Data.xlsx

11. Variance of Residuals Equals Variance of Dependent Variable.mp4

02:28

12. Distribution of Residuals - Is it Normal.mp4

02:49

13. Check your progress.html

14. How to find Confidence Interval for the Observed Values.mp4

03:50

15. Fitted Value and Confidence Interval.html

16. What is Z Score.mp4

05:47

17. Check your concepts.html

18. More about Z score.html

19. Application of Linear Regression Modeling.html

20. Interpreting Linear Regression Model.mp4

01:33

21. Check your progress.html

22.1 Optional Lecture Hypothesis Testing.xlsx

22. Hypothesis Testing.html

23.1 How to Add Data Analysis Tab in Excel.html

23.2 India and Bangladesh.xlsx

23. Two Sided Hypothesis Testing Using Real Example of Road Traffic Injury.mp4

02:41

24.1 India and Sri Lanka.xlsx

24. One Sided Hypothesis Testing Using Real Example of India and Sri Lanka.mp4

01:23

25. Hypothesis Test.html

26. Conclusion of First Section.mp4

01:29

2. Finding Linear Regression Model using R programming

1.1 Website to Download R programming.html

1.2 Website to download R Studio.html

1. Downloading R programming and R Studio.mp4

01:07

2.1 data science course udemy.zip

2.2 Data Science Course Udemy.xlsx

2. Importing and Reading the Data in R Studio.mp4

03:54

3.1 data science course udemy.zip

3. Creating a Linear Regression Model in R.mp4

02:37

4.1 data science course udemy.zip

4.2 histogram of residuals.zip

4. Finding Fitted Values Residuals and Variance And Creating Histograms.mp4

02:52

5. Creating a Simple Linear Regression Model using R.html

6.1 Parking Duration Data Section.xlsx

6. Test whether dependent variable really depends on independent variable or not.mp4

03:10

7. Test whether dependent variable really depends on independent variable or not.html

8. 95% Confidence Interval of Beta 1.html

3. Identifying the Outliers in the Data

1.1 Outliers Section 3.xlsx

1. What are Outliers.mp4

03:36

2. Finding Outliers in R using Stem Leaf Plot.html

3. Finding Outliers in R using Box Plot.mp4

03:06

4. Practice finding outliers.html

5.1 semistudentized residuals outlier.zip

5. Finding Outliers using Semi-Studentized Residuals.mp4

02:20

4. Testing the Assumptions of Linear Regression

1. Introduction.mp4

01:09

2. Normal Q-Q Plot for Residuals.mp4

02:23

3. Correlation Test in R to check Normality of Residuals.html

4. Practice testing Normality of Residuals in R.html

5.1 residual plot against independent variable.zip

5. Plot between Residuals and Independent Variables.mp4

01:40

6.1 residual vs fitted value.zip

6. Plot between Residuals and Fitted Value.html

7. Practice Creating Residual Plots with Independent Variables and Fitted Values.html

5. Linear Regression with Multiple independent Variables

1.1 Data Science Course Udemy.xlsx

1. Selecting the Parameters.mp4

03:26

2. Creating a Multiple Variable Model.html

3. Finding the Best Subset Model.mp4

02:40

4. Creating Multiple Variable Model and finding Best Subsets.html

5. How to Check whether your Model is Correct or Not.html

6. Bonus Section on MNL Modeling

1.1 discrete choice theory .pdf

1. Discrete Choice Theory.mp4

03:34

2.1 Case Study.pdf

2. Case Study of Carpooling in Bengaluru.mp4

04:28

3. Designing the Survey.mp4

11:00

4.1 Sample Data.xlsx

4. How much data to be collected.mp4

02:52

5.1 types of variables .pdf

5. Types of Variables.mp4

05:19

6. Preparing data for analysis.mp4

02:21

7.1 gender.pdf

7. Role of Gender in Choosing Carpooling.mp4

11:16

8.1 age .pdf

8. Role of Age in Choosing to Carpool.mp4

02:46

9.1 belief .pdf

9. Role of Belief in Choosing to Carpool.mp4

02:50

10.1 final mnl model .pdf

10. Final MNL Model.mp4

02:56

7. Conclusion

1.1 Website for more such skill courses.html

1. Closing Remarks.mp4

00:12

Description

Designed for Absolute Beginners in R programming

What You'll Learn?

Important Properties of Residuals
Linear Regression using MS Excel
Hypothesis testing
Linear Regression using R programming
Finding outliers of the data
Testing assumptions of Linear Regressions
Linear Regression in Multiple Variables

Who is this for?

Beginners in R programming

Beginners in Data Science

Beginners in Linear Regression

Transportation Engineers

Civil Engineering Students

What You Need to Know?

No knowledge of Mathematics or Programming is required

More details

Description
This course has been designed for Transportation Engineers, Planners, Traffic Engineers who are absolute beginners in Data Science field. You don't need to have any background in Statistics or coding in order to take this course. In the first section, I have explained all the necessary terms related to linear regression through actual examples using data from a Parking Study. If you are not familiar with it, it is still okay as things are taught from scratch and emphasis has been given to data analysis.
Later, we discuss R programming commands to implement the same thing as taught in Section 1. I have assumed that you have a zero experience in R and hence I have explained even the most basic things.
Then, we deep dive into data analysis part and making sure that we can actually model it using linear regression. We also discuss several assumptions such as normality, linearity and constant variance of the error terms. I have shown how to check whether our model is satisfying those assumptions or not.
Lastly, we discuss models with more than one variables. I have given steps to identify suitable variables for the model.
The philosophy behind this course is to provide you with an introduction. As a Transportation Engineer, you might be curious to learn about Data Science but may not have been able to do so because of hard mathematics and coding requirements. I have broken down complex concepts and explained them here through real life applications from transportation industry to enable you to learn it.
This course helps you become strong in fundamental concepts of Linear Regression and Data Science in general. It is not very heavy in coding or mathematics.
Who this course is for:
Beginners in R programming
Beginners in Data Science
Beginners in Linear Regression
Transportation Engineers
Civil Engineering Students

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I am an experienced Transportation Engineering professional with 6+ years of experience working with various consultancy companies such as Fehr and Peers, Cambridge Systematics in USA as well as ARS Traffic & Transport Technology in India. I have worked as a Senior Traffic Engineer in Mumbai for 2 years on adaptive traffic signals trying to improve their operations to ease the congestion in the city. I have a Master's degree in Transportation Engineering from UC Berkeley with a GPA of 3.73 and a Bachelor's degree in Civil Engineering from IIT Bombay.

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