Predictive Modeling using SAS & R Online Training

First Online Training Batch
We are starting our first Instructor-led online training batch from July 15, 2017. We are offering courses on SAS , R and Predictive Modeling. In this program you will get an access to live lectures plus recorded videos from any part of the world via web conference mode. Also you can chat or even ask their questions verbally over the VoIP in real time to get their doubts cleared.
  1. Practical SAS Programming -  Learning SAS by Case Studies
  2. Predictive Modeling with SAS -  Modeling with Hands-on Examples plus Domain Knowledge
  3. Data Science using R - Practical Data Science Course (Incld. R Programming, Data Science and Domain Knowledge)

  4. Batch : 15th July, Saturday     Mode : Live Instructor-led


Practical SAS Programming
$325
(Rs 20,000)
  • Base and Advanced SAS Programming
  • Instructor-led live class + Recorded videos
  • Duration : 10 Weeks (100 hours)
  •          Live Projects                  
  • Case Studies
  • Hands-on Examples
  • Weekly Assignments
  • Certification
  • Job Placement Assistance
  • Limited Seats Available
  • Weekend Classes
  • Money Back Guarantee
All Users (Except India) Indian Users


Predictive Modeling using SAS
$400
(Rs 25,000)
  • Predictive Modeling + Intro to SAS Programming
  • Instructor-led live class + Recorded videos
  • Duration : 12 Weeks (120 hours)
  • Live Projects + Domain Knowledge
  • Case Studies
  • Hands-on Examples
  • Weekly Assignments
  • Certification
  • Job Placement Assistance
  • Limited Seats Available
  • Weekend Classes
  • Money Back Guarantee
All Users (Except India) Indian Users


R Programming + Data Science with R 
$480
(Rs 30,000)
  • R Programming + Predictive Modeling
  • Instructor-led live class + Recorded videos
  • Duration : 13 Weeks (130 hours)
  • Live Projects + Domain Knowledge
  • Case Studies
  • Hands-on Examples
  • Weekly Assignments
  • Certification
  • Job Placement Assistance
  • Limited Seats Available
  • Weekend Classes
  • Money Back Guarantee
All Users (Except India) Indian Users



Combo Deals - Spend Less, Learn More
Pay only $800 (Rs 50,000) on purchase of all three courses
Pay only $625 (Rs 40,000) on purchase of 'Predictive Modeling with SAS' and 'Data Science with R' courses
Pay only $550 (Rs 35,000) on purchase of 'Practical SAS Programming' and 'Predictive Modeling with SAS' courses
What is Instructor-led live program?
It is an interactive training program. Learners will get an access to live lectures via live webinar mode and can chat or even ask their questions verbally over the VoIP in real time to get their doubts cleared. Also you can go through video recording if you miss a class.

When do I need to pay full course fee?
Now you just need to pay $15 (Rs 1,000) to reserve your seat. Hit 'Pay & Reserve your seat' button above in the table. You can make payment of the remaining balance until 15th July, 2017 to access to live lectures and recorded videos. We will send you an email in the first week of July to make the final payment.
Money Back Guarantee : If you do not like our training, you can ask for 100% course fees refund after your first live session. No question asked refund policy!

What is the duration of these programs?
These are weekend programmes comprising 100-130 hours. The course duration is as follows -
  1. Practical SAS Programming - 100 hours (At least 50 hours live training + 5 hours video based training + ~60 hours of Practice and Self Study)
  2. Predictive Modeling with SAS - 120 hours (At least 60 hours live training + 6 hours video based training + ~70 hours of Practice and Self Study)
  3. Data Science with R - 130 hours (At least 70 hours live training + 7 hours video based training + ~80 hours of Practice and Self Study)

If I opt for all the 3 courses, will classes be scheduled at the same time?
All classes will be scheduled on weekend but not at the same time. It'll be one by one. For example if class A gets over at 5. Next class will start at 6.

How We are different from other institutes?
Here are some of the features of ListenData that makes us better than other training institutes.
  1. Explain Advanced Statistical and Machine Learning Algorithms in Simple English. We make classes more logical and understandable than just telling concepts.
  2. Practical Application of Techniques using Real-world Datasets. No sample or cleaned dataset.
  3. Domain Knowledge - It is the most important element of a predictive modeling project. People who lack in domain knowledge find it difficult to crack interviews in spite of having knowledge of predictive modeling.
  4. Hands-on Model Development and Validation
  5. Strategies to implement predictive model
  6. New algorithms to solve problems efficiently
  7. Explain complex topics via visual lessons

Who should do these courses?
These courses are ideal for candidates who want to make a career in analytics.
  1. Any candidate pursuing graduation / post graduation or already graduate can apply for this course. No particular specialization is required prior to applying for these courses. You can be from any educational background like Engineering, Economics, Statistics, Mathematics, Commerce, Business Management, Operational Research etc.
  2. Anyone who is planning a career shift to analytics. It does not matter if you are a network engineer or financial analyst. You can go ahead with these courses as they do not require any prior knowledge of programming or statistics.

Every training institute promises job. Why should i trust you?
Let's be honest! It's a universal fact that no college or training institute can provide 100% job guarantee. If they are claiming 100% job guarantee, they are luring learners by false promises. Even IITs do not hit 100% score. Some Facts - Only 66% of IITians landed a job offer via campus recruitment in 2016-17, as against 79% in 2015-16 and 78% in 2014-15, according to HRD ministry.

Let me list down the common reasons why people don't get jobs in analytics industry even after completing training from some colleges / institutes -
  1. No hands-on experience
  2. No domain knowledge
  3. No theoretical knowledge of statistical concepts
  4. Poor analytical skill
The objective of this program is to cover the above first three points in detail. In addition we provide job placement assistance to all students. We will keep you informed about current openings in analytics industry. We are in constant contact with job consultancy firms and a solid network of analytics professionals.

The decline of SAS Jobs and rise of R?
I have been working in SAS for close to 7 years and worked with 4 organizations (Instability in career! :D ). Whenever I look for a job change, I do not see any decline of SAS jobs in the market. Don't trust me, go to job portals and search 'SAS'! List of Companies using SAS It is a big hit in banks, insurance, telecom and pharmaceutical companies. SAS is still a world leader in advanced analytics and has over 40,000 customers worldwide. It has been tagged 'leader' consistently in advanced analytics platform as per Gartner 2015 and 2016 reports. It is one of the most sought after skill in job market. Learning SAS will help you to scale up your skills, which in turns leads to boost your career.

At the same time, R has gained popularity. It is a language of choice for data scientists. It makes advanced statistical techniques and machine learning algorithms easy to implement. It is being used as a primary tool in IT, ecommerce, startups, HR, service and product based companies and secondary tool in banks, insurance and telecom companies. List of Companies using R

Final Comment - You should not get into language wars and should focus on learning both the languages as jobs are evolving very fast. Companies prefer candidates who know both SAS & R.

In case if I miss any live session?
Every class is recorded. We will provide you recording of every session.

I never studied Programming or Statistics during graduation. Can I still apply for this course?
Yes, these courses are designed to keep in mind the needs of non-programmers/non-statisticians. Only prerequisite is hard work and zeal for learning.

Is my registration fees refundable?
100% refundable. Incase you want to opt out of the course for any reason, you can ask for 100% refund within 7 days of registration. If you want to continue, it would be automatically adjusted on total fees. In other words, you pay $15 (Rs 1000) less of the amount of total fees.

About Instructor
Deepanshu founded ListenData with a simple objective - Make analytics easy to understand and follow. He has close to 7 years of experience in data science and predictive modeling. He has worked with companies like Aon, Cognizant, Genpact, RBS. He has handled global clients in various domains like retail and commercial banking, Telecom, HR and Automotive. He has worked extensively in various data science projects such as Customer Attrition, Customer Lifetime Value Model, Propensity Model, Opinion / Sentiment Mining, Geo Analytics, Credit risk scorecard, Portfolio Optimization, Pricing Analytics, Cross sell/Up sell campaign models, Survey Analytics, Customer Segmentation, Market Benchmarking, Employee Attrition, Employee Engagement etc.

Any Questions?
Please feel free to write me at deepanshu.bhalla@outlook.com OR Join me on linkedin

Predictive Modeling using SAS & R Training




Curriculum - Practical SAS Programming
  1. Introduction to SAS
  2. How SAS works
  3. Import Raw Data Files - Basics
  4. Import Raw Data Files - Special Cases
  5. Importing / Exporting Data with Procedures
  6. Exploring Data - Various Methods
  7. Data Subsetting
  8. Data Manipulation - Basics
  9. Data Manipulation - Intermediate
  10. Data Manipulation - Advanced
  11. Do Loops and Arrays
  12. Merging Data
  13. Appending Data
  14. Character & Numeric Functions
  15. Date Functions
  16. Reporting - Creating tabular reports
  17. Proc SQL - Part I
  18. Proc SQL - Part II
  19. Proc SQL - Part III
  20. SAS Macros - Basics
  21. SAS Macros - Intermediate
  22. SAS Macros - Advanced
  23. SAS Macros - Debugging Tips
  24. Efficient SAS Programming Tips
  25. Connect to Databases using SAS
  26. Interview Preparation - Scenario Based Questions
  27. Live Project




Curriculum - Predictive Modeling using SAS
  1. Introduction to Statistics & Modeling
  2. Marketing Analytics : Applications
  3. Predictive Modeling in Financial Services Industry
  4. Predictive Modeling in HR
  5. SAS Programming - Basics
  6. SAS Programming - Intermediate
  7. Descriptive Statistics with SAS
  8. Hypothesis Testing with SAS
  9. Correlation Analysis with SAS
  10. Steps of Predictive Modeling
  11. Data Preparation in Predictive Modeling
  12. Variable Selection Methods in Predictive Modeling
  13. Segmentation - Introduction
  14. Segmentation - Cluster Analysis : Theory
  15. Segmentation - Cluster Analysis : Data Preparation
  16. Segmentation - Cluster Analysis : k-means and Hierarchical
  17. Segmentation - Cluster Analysis : Cluster Performance
  18. Principal Component Analysis (PCA) - Theory
  19. Running and Understanding PCA with SAS
  20. Linear Regression - Theory
  21. Linear Regression - Assumptions and Treatment
  22. Linear Regression - Important Metrics
  23. Linear Regression - Variable Selection Methods
  24. Linear Regression - Model Development
  25. Linear Regression - Model Validation
  26. Linear Regression - Model Performance
  27. Linear Regression - Model Scoring
  28. Linear Regression - Model Implementation
  29. Logistic Regression - Theory
  30. Logistic Regression - Assumptions and Treatment
  31. Logistic Regression - Important Metrics
  32. Logistic Regression - Variable Selection Methods
  33. Logistic Regression - Model Development
  34. Logistic Regression - Model Validation
  35. Logistic Regression - Model Performance
  36. Logistic Regression - Model Implementation
  37. Decision Tree - How it works
  38. Decision Tree - Model Development
  39. Decision Tree - Model Validation
  40. Decision Tree - Model Performance
  41. Decision Tree - Model Implementation
  42. Time Series Forecasting - Theory
  43. Time Series Analysis with SAS
  44. Special Cases - Handle rare event model
  45. Case Studies - Attrition / Churn Model (BFSI / Telecom)
  46. Case Studies - Customer Segmentation
  47. Case Studies - Probability of Default
  48. Case Studies - HR Drivers Analysis
  49. Case Studies - Sales Forecasting
  50. Case Studies - Time Series Forecasting
  51. Interview Tips - Common Interview Questions



Curriculum - R Programming + Data Science with R
  1. Introduction to R
  2. Introduction to RStudio
  3. Data Structures in R
  4. Importing / Exporting Data in R
  5. Data Exploration
  6. Data Manipulation with dplyr package - Basics
  7. Data Manipulation with dplyr package - Intermediate
  8. Data Manipulation with dplyr package - Advanced
  9. Character and Numeric Functions in R
  10. Data & Time Functions in R
  11. Data Visualization in R
  12. Loops in R (Apply Family of Functions & For Loop)
  13. R Functions - Part I
  14. R Functions - Part II
  15. Introduction to Data Science
  16. Marketing Analytics : Applications
  17. Predictive Modeling in Financial Services Industry
  18. Predictive Modeling in HR
  19. Hypothesis Testing with R
  20. Correlation Analysis with R
  21. Steps of Predictive Modeling
  22. Data Preparation in Predictive Modeling
  23. Variable Selection Methods in Predictive Modeling
  24. Segmentation - Introduction
  25. Segmentation - Cluster Analysis : Theory
  26. Segmentation - Cluster Analysis : Data Preparation
  27. Segmentation - Cluster Analysis : k-means and Hierarchical
  28. Segmentation - Cluster Analysis : Cluster Performance
  29. Principal Component Analysis (PCA) - Theory
  30. Running and Understanding PCA with R
  31. Linear Regression - Theory
  32. Linear Regression - Assumptions and Treatment
  33. Linear Regression - Important Metrics
  34. Linear Regression - Variable Selection Methods
  35. Linear Regression - Model Development
  36. Linear Regression - Model Validation
  37. Linear Regression - Model Performance
  38. Linear Regression - Model Scoring
  39. Linear Regression - Model Implementation
  40. Logistic Regression - Theory
  41. Logistic Regression - Assumptions and Treatment
  42. Logistic Regression - Important Metrics
  43. Logistic Regression - Variable Selection Methods
  44. Logistic Regression - Model Development
  45. Logistic Regression - Model Validation
  46. Logistic Regression - Model Performance
  47. Logistic Regression - Model Implementation
  48. Decision Tree - How it works
  49. Decision Tree - Model Development
  50. Decision Tree - Model Validation
  51. Decision Tree - Model Performance
  52. Decision Tree - Model Implementation
  53. Machine Learning - Basics
  54. Random Forest - How it works
  55. Random Forest vs. Decision Tree
  56. Random Forest - Model Development
  57. Random Forest - Model Validation
  58. Random Forest - How it works
  59. Gradient Boosting - How it works
  60. Gradient Boosting - Model Development
  61. Gradient Boosting - Model Validation
  62. Support Vector Machine - How it works
  63. Support Vector Machine - Model Development
  64. Support Vector Machine - Model Validation
  65. Ensemble Stacking / Blending
  66. Time Series Forecasting - Theory
  67. Time Series Analysis with R
  68. Special Cases - Handle rare event model
  69. Text Mining Basics & Applications
  70. Case Studies - Attrition / Churn Model (BFSI / Telecom)
  71. Case Studies - Customer Segmentation
  72. Case Studies - Probability of Default
  73. Case Studies - HR Drivers Analysis
  74. Case Studies - Sales Forecasting
  75. Case Studies - Time Series Forecasting
  76. Interview Tips - Common Interview Questions

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