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SAS, R & Predictive Modeling Training in Delhi NCR

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SAS Training in Delhi NCR
We are starting our first classroom training batch from July 15, 2017 in Delhi NCR (Delhi / Gurgaon). We are offering courses on SAS , R and Predictive Modeling.
  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  

Practical SAS Programming
Rs 20,000
  • Base and Advanced SAS Programming
  • Classroom Training + Videos
  •          Live Projects                  
  • Case Studies
  • Hands-on Examples
  • Weekly Assignments
  • Certification
  • Job Placement Assistance
  • Limited Seats Available


Venue : To be decided
Weekend Classes

Predictive Modeling using SAS
Rs 25,000
  • Predictive Modeling + Intro to SAS Programming
  • Classroom Training + Videos
  • Live Projects + Domain Knowledge
  • Case Studies
  • Hands-on Examples
  • Weekly Assignments
  • Certification
  • Job Placement Assistance
  • Limited Seats Available


Venue : To be decided
Weekend Classes

R Programming + Data Science with R 
Rs 30,000
  • R Programming + Predictive Modeling
  • Classroom Training + Videos
  • Live Projects + Domain Knowledge
  • Case Studies
  • Hands-on Examples
  • Weekly Assignments
  • Certification
  • Job Placement Assistance
  • Limited Seats Available


Venue : To be decided
Weekend Classes


Combo Deals - Spend Less, Learn More
Get Rs 10,000 off on registration for any of the two courses
Get Rs 20,000 off on registration for all of the three courses
**Offer ends 15th June, 2017

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 Analytics 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 Experience
  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.

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. It is a big hit in banks, insurance, telecom and pharmaceutical companies. SAS is still a world leader in advanced analytics. 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. List of Companies using SAS

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.

Is my registration fees refundable?
It would be automatically adjusted on total fees. In other words, you pay Rs 1000 less of the amount of total fees. Incase you want to opt out of the course, you can ask for refund within 7 days of registration.

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

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 Tips - 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 and Validation
  57. Time Series Forecasting - Theory
  58. Time Series Analysis with R
  59. Special Cases - Handle rare event model
  60. Case Studies - Attrition / Churn Model (BFSI / Telecom)
  61. Case Studies - Customer Segmentation
  62. Case Studies - Probability of Default
  63. Case Studies - HR Drivers Analysis
  64. Case Studies - Sales Forecasting
  65. Case Studies - Time Series Forecasting
  66. Interview Tips - Common Interview Questions


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