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.
- Practical SAS Programming - Learning SAS by Case Studies
- Predictive Modeling with SAS - Modeling with Hands-on Examples plus Domain Knowledge
- 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
Curriculum
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
Curriculum
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
Curriculum
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.
- Explain Advanced Analytics and Machine Learning Algorithms in Simple English. We make classes more logical and understandable than just telling concepts.
- Practical Application of Techniques using Real-world Datasets. No sample or cleaned dataset.
- 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.
- Hands-on Model Development and Validation Experience
- Strategies to implement predictive model
- New algorithms to solve problems efficiently
- 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.
- 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.
- 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.
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
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SAS & R Training |
Curriculum - Practical SAS Programming
- Introduction to SAS
- How SAS works
- Import Raw Data Files - Basics
- Import Raw Data Files - Special Cases
- Importing / Exporting Data with Procedures
- Exploring Data - Various Methods
- Data Subsetting
- Data Manipulation - Basics
- Data Manipulation - Intermediate
- Data Manipulation - Advanced
- Do Loops and Arrays
- Merging Data
- Appending Data
- Character & Numeric Functions
- Date Functions
- Reporting - Creating tabular reports
- Proc SQL - Part I
- Proc SQL - Part II
- Proc SQL - Part III
- SAS Macros - Basics
- SAS Macros - Intermediate
- SAS Macros - Advanced
- SAS Macros - Debugging Tips
- Efficient SAS Programming Tips
- Connect to Databases using SAS
- Interview Tips - Scenario Based Questions
- Live Project
Curriculum - Predictive Modeling using SAS
- Introduction to Statistics & Modeling
- Marketing Analytics : Applications
- Predictive Modeling in Financial Services Industry
- Predictive Modeling in HR
- SAS Programming - Basics
- SAS Programming - Intermediate
- Descriptive Statistics with SAS
- Hypothesis Testing with SAS
- Correlation Analysis with SAS
- Steps of Predictive Modeling
- Data Preparation in Predictive Modeling
- Variable Selection Methods in Predictive Modeling
- Segmentation - Introduction
- Segmentation - Cluster Analysis : Theory
- Segmentation - Cluster Analysis : Data Preparation
- Segmentation - Cluster Analysis : k-means and Hierarchical
- Segmentation - Cluster Analysis : Cluster Performance
- Principal Component Analysis (PCA) - Theory
- Running and Understanding PCA with SAS
- Linear Regression - Theory
- Linear Regression - Assumptions and Treatment
- Linear Regression - Important Metrics
- Linear Regression - Variable Selection Methods
- Linear Regression - Model Development
- Linear Regression - Model Validation
- Linear Regression - Model Performance
- Linear Regression - Model Scoring
- Linear Regression - Model Implementation
- Logistic Regression - Theory
- Logistic Regression - Assumptions and Treatment
- Logistic Regression - Important Metrics
- Logistic Regression - Variable Selection Methods
- Logistic Regression - Model Development
- Logistic Regression - Model Validation
- Logistic Regression - Model Performance
- Logistic Regression - Model Implementation
- Decision Tree - How it works
- Decision Tree - Model Development
- Decision Tree - Model Validation
- Decision Tree - Model Performance
- Decision Tree - Model Implementation
- Time Series Forecasting - Theory
- Time Series Analysis with SAS
- Special Cases - Handle rare event model
- Case Studies - Attrition / Churn Model (BFSI / Telecom)
- Case Studies - Customer Segmentation
- Case Studies - Probability of Default
- Case Studies - HR Drivers Analysis
- Case Studies - Sales Forecasting
- Case Studies - Time Series Forecasting
- Interview Tips - Common Interview Questions
Curriculum - R Programming + Data Science with R
- Introduction to R
- Introduction to RStudio
- Data Structures in R
- Importing / Exporting Data in R
- Data Exploration
- Data Manipulation with dplyr package - Basics
- Data Manipulation with dplyr package - Intermediate
- Data Manipulation with dplyr package - Advanced
- Character and Numeric Functions in R
- Data & Time Functions in R
- Data Visualization in R
- Loops in R (Apply Family of Functions & For Loop)
- R Functions - Part I
- R Functions - Part II
- Introduction to Data Science
- Marketing Analytics : Applications
- Predictive Modeling in Financial Services Industry
- Predictive Modeling in HR
- Hypothesis Testing with R
- Correlation Analysis with R
- Steps of Predictive Modeling
- Data Preparation in Predictive Modeling
- Variable Selection Methods in Predictive Modeling
- Segmentation - Introduction
- Segmentation - Cluster Analysis : Theory
- Segmentation - Cluster Analysis : Data Preparation
- Segmentation - Cluster Analysis : k-means and Hierarchical
- Segmentation - Cluster Analysis : Cluster Performance
- Principal Component Analysis (PCA) - Theory
- Running and Understanding PCA with R
- Linear Regression - Theory
- Linear Regression - Assumptions and Treatment
- Linear Regression - Important Metrics
- Linear Regression - Variable Selection Methods
- Linear Regression - Model Development
- Linear Regression - Model Validation
- Linear Regression - Model Performance
- Linear Regression - Model Scoring
- Linear Regression - Model Implementation
- Logistic Regression - Theory
- Logistic Regression - Assumptions and Treatment
- Logistic Regression - Important Metrics
- Logistic Regression - Variable Selection Methods
- Logistic Regression - Model Development
- Logistic Regression - Model Validation
- Logistic Regression - Model Performance
- Logistic Regression - Model Implementation
- Decision Tree - How it works
- Decision Tree - Model Development
- Decision Tree - Model Validation
- Decision Tree - Model Performance
- Decision Tree - Model Implementation
- Machine Learning - Basics
- Random Forest - How it works
- Random Forest vs. Decision Tree
- Random Forest - Model Development and Validation
- Time Series Forecasting - Theory
- Time Series Analysis with R
- Special Cases - Handle rare event model
- Case Studies - Attrition / Churn Model (BFSI / Telecom)
- Case Studies - Customer Segmentation
- Case Studies - Probability of Default
- Case Studies - HR Drivers Analysis
- Case Studies - Sales Forecasting
- Case Studies - Time Series Forecasting
- Interview Tips - Common Interview Questions