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Sofiane Mesbah, Instructor - Scaling Advanced Analytics

Sofiane Mesbah

Sofiane has expertise across key sectors and idustries. During his career he has had exposure to a broad range of engagement in big data. Sofiane has worked for Medialgeria, Exl Services, EY (formerly Ernst & Young), Acxiom, Oakam Limited, HSBC, Lloyds TSB, and T-online. He's currently involved in a tech startup in an emerging market.

Instructor: Sofiane Mesbah

An In-depth Advanced Analytics Training using SAS.

  • Master statistical learning algorithms from start to finish and learn how to scale using data driven programming.
  • Have access to course specific exclusive algorithms to tailor and scale predictive modeling for 100+ factors.
  • Instructor has 10 years of hands-on experience serving companies like HSBC, Ernst & Young, Acxiom, and Llyods.

Course Description

Big data remains simple because it scales the processing power across several computers, but big analytics will be more challenging because each dimension is analyzed differently. In this course, you will learn a framework to generate easy to understand algorithm. This will enable you to scale advanced analytics work for high dimension data set.

What am I going to get from this course?

  • Large scale data preparation
  • Automate data process
  • Large scale reporting tasks
  • Mass reporting easily automated
  • Large scale statistical modelling
  • Predictive modelling with high dimension datasets
  • Master big analytics

Prerequisites and Target Audience

What will students need to know or do before starting this course?

  • SAS licence (note that 'SAS university edition' is free and can be used as well)
  • Preferably intermediate SAS programming skills
  • Preferably intermediate statistical modelling skills

Who should take this course? Who should not?

  • Statisticians
  • SAS programmers
  • Predictive modelers
  • Data scientists
  • Data miners


Module 1: Key concepts

Lecture 1 Scaling Advanced Analytics

This introduction section will explain purpose of the course, define target audience, and present key concepts that will be used.

Module 2: Data Driven programming

Lecture 2 How Data Driven Programming Works

This section explains the programming, the methodology that enable to tackle complex data sets, and scale analytics tasks across high volume of variables. Upon the completion of this section student should be able to use efficient inception method to prepare large scale analytics.

Quiz 1 Excel processing

Design a sas script that can process an excel list. Non technical staff should be able to use the excel file to select variables they need for the analysis. The list will enable to select numerical and categorical variables separately. The SAS script will produce descriptive statistics for each type of variables

Lecture 3 Macro inception type

We will explain how single macro, macro vector and macro array will help for initialisation stage

Lecture 4 Inception table

Tables can be used for inception, this video will explain main methods for tables inception.

Module 3: Data preparation algorithms

Lecture 5 Outliers Removal

This module will use inception methods used earlier along with loops to apply methodology for data preparation purpose. Upon completing this part the student should be able automate data preparations steps for statistical modeling with massive data sets. This lecture will demonstrate data driven programming to tailor an outliers removal algorithm.

Quiz 2 Label Allocation

The excel file varlabels.xlsx contain variables labels. Process this file to automate the allocation of labels for each variable.

Quiz 3 Outliers for Left Skewed Variables

The script studied in section 3 related to outlier removal for left skewed variables. Use market dataset instead of airline dataset Adapt the algorithm to deal with right skewed variables as well

Lecture 6 Binning

Binning enable to transform a numerical into categorical variables and is often required to run learning algorithms. the following video shows an algorithms that does that sequentially for any volume of variables. This is one of the most difficult part, you may skip this video for the end.

Lecture 7 Distinct and Missing values

Variables with too many level or missing values will cause stability issues. A simple approach is used here to tackle these issues

Lecture 8 Balanced Distribution

Categorical predictor with a balanced distribution will lead to more stable statistical models. The lecture explain approach taken to detect automatically these distributions.

Module 4: Dimension reduction

Lecture 9 Bivariate Dimension Reduction

Sometimes redundant information is caused by similar variables. This module will use data driven method to enable dimension reduction techniques with massive datasets. The following lecture explains algorithms used to detect bivariate relationship.

Lecture 10 Multivariate Dimension Reduction

Multivariate relationship detected method is explained and simple script shows how to use proc Varclus. .

Module 5: Regression adjustment algorithms

Lecture 11 Exeptional Data Points

Vast amount of variable means adapting the data modeling process can be time consuming. Examples shown will enable student to adapt, tailor regression algorithms to enhance modeling performance, and adapt modeling policies. This lecture will explain how to remove exceptional data during the regression process

Quiz 4 Clustering for Regression

The purpose of this exercise is to select a set of variable and clusters them. The best variable within each clusters will selected using sequences of logistic regression for each cluster.

Lecture 12 Ods Output as Inception

This lecture shows how we can use 'ods output' and combine it with data driven programming to remove automatically variable contributing to multi collinearity. The purpose is to enable data scientists to use these programming concept to develop and tailor easily it's own modeling algorithms.


7 Reviews

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Jonathan W

May, 2017

An excellent and beneficial course as I understand. As an analytics professional I gained more knowledge how to scale analytics using SAS. It is a collaborative learning experience for me understanding the usage of SAS as a statistician. It generated interest in me to learn SAS. It has really helped me to learn scale advanced analytics work for high dimension data set, automated mass reporting, large scale statistical modeling and predictive modeling. It was made easy in this course to understand algorithms too. Hats off to the instructor.

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Berkay A

May, 2017

As each dimension is analyzed differently, the course excellently taught me to learn a framework to generate easy to understand algorithm.

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Ruze R

May, 2017

Professionally I gained much more leverage by learning large scale data preparation and automating its process and large scale reroting tasks efficiently from the course. The insstructor was great in explaing very well whatever topic he was taking.

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Fred T

July, 2017

This is a good course with rich information on the application of advanced analytics. It was easy to learn algorithms in this course as well.

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Dagvasuren G

July, 2017

I liked this course. The lecture videos addressed a lot my questions and I was delighted to use everything I picked up in the final project.

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Jin N

July, 2017

It is a good course to learn how to use advanced analytics. Training through this, I could successfully use analytics for a certain business problem I'm dealing with. I picked up a deeper understanding of measuring analytics with SAS. The instructor was good at explaining very carefully whatever point he was going with.

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Tino N

July, 2017

A very good and useful course as I found out. I enjoyed the collaborative training activities to learn SAS. It got me excited to learn more on SAS. It has certainly helped me understand how to scale advanced analytics work.