Data Science using SAS & R

A comprehensive business analytics and data science training using SAS and R

Learning tools without techniques is half the job done. So to help candidates emerge as 'Industry Ready' professional, this Business Analytics training encompasses basic statistical concepts to advanced analytics and predictive modelling techniques, along with machine learning, using most widely used analytics tools, like Excel, R, SAS (including Proc SQL). This analytics certification course is for all those aspirants who want to switch into the field of data science and business analytics.

This analytics course has emerged from our most coveted flagship program SAS+ Business Analytics certification and evolved over last 4 years based on the changing industry requirements. Unlike most other analytics courses, this business analytics training is aimed to provide you job oriented Data Science and Business Analytics skills.

Crafted and delivered by a team of industry experts, this comprehensive Business Analytics course has all the components required to give you a strong foundation and head-start into the field of Analytics!

Course duration: 160 hours (Atleast 80 hours live training + 6 hours video based training + Practice and Self-study, with ~8hrs of weekly self-study)

Who Should do this course?

Candidates from various quantitative backgrounds, like Engineering, Finance, Maths, Statistics, Business Management who want to head start their career in analytics.

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Course Duration 160 hours
Classes 30
Tools Excel, SAS, R
Learning Mode Live/Video Based
Next Batch04th June, 2017 (Gurgaon)
25th June, 2017 (Bangalore)

Introduction to Data Science - Advanced Analytics

  • Relevance in industry & need of the hour
  • Types of analytics – Marketing, Risk, Operations, etc
  • Business & Technology drivers for analytics
  • Analytics Tool Kit - Popular tools & Techniques in the Industry
  • Future of analytics & critical requirement
  • Types of problems and business objectives in various industries
  • Different phases of Analytics Project

Excel - Basic (video-based)

  • Introduction to Excel
  • Working with Formulas and functions
  • Formating & Conditional Formating
  • Filtering, sorting, paste special etc
  • Fuctions(Logical & Text, Mathematical, Statistical etc)
  • Data Manipulation & Data Aggregation
  • Data Analysis using functions

Excel - Advanced (video-based)

  • Analyzing Data using Pivots
  • Descriptive Statistics
  • Creating Charts & Graphics
  • Data analytics tool (What -if analysis, Goal seek, Data Table, Solver)
  • Protecting Workbooks, worksheets and formulas

SAS - Introduction - Data importing - Understanding

  • Introduction to SAS, GUI
  • Concepts of Libraries, PDV, data execution etc
  • Building blocks of SAS (Data & Proc Steps - Statements & options)
  • Debugging SAS Codes
  • Importing different types of data & connecting to data bases
  • Data Understanding(Meta data, variable attributes(format, informat, length, label etc))
  • SAS Procedures for data import /export / understanding(Proc import/Proc contents/Proc print/Proc means/Proc feq)

SAS - Data Manipulation

  • Data Manipulation steps(Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type converstions, renaming, formatting, etc)
  • Data manipulation tools (Operators, Functions, Procedures, control structures, Loops, arrays etc)
  • SAS Functions (Text, numeric, date, utility functions)
  • SAS Procedures for data manipulation (Proc sort, proc format etc)
  • SAS Options (System Level, procedure level)

SAS - Exploratory Data Analysis & Data visualization

  • Introduction exploratory data analysis
  • Descriptive statistics, Frequency Tables and summarization
  • Univariate Analysis (Distribution of data & Graphical Analysis)
  • Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
  • SAS Procedures for Data Analysis(proc freq/Proc means/proc summary/proc tabulate/Proc univariate etc)
  • SAS Procedures for Graphical Analysis (Proc Sgplot, proc gplot etc)

SAS - Reporting - Output Exporting

  • Introduction to Reporting
  • SAS Reporting Procedures (Proc print, Proc Report, Proc Tabulate etc)
  • Exporting data sets into different formats (Using proc export)
  • Concept of ODS (output delivery system)
  • ODS System - Exporting output into different formats

Advanced SAS (Proc SQL - Macros) - Optimizing SAS Codes

  • Introduction to Advnaced SAS - Proc SQL & Macros
  • Understanding select statement (From, where, group by, having, order by etc)
  • Proc SQL - Data creation/extraction
  • Proc SQL - Data Manipulation steps
  • Proc SQL - Summarizing Data
  • Proc SQL - Concept of sub queries, indexes etc
  • SAS Macros - Creating/defining macro variables
  • SAS Macros - Defining/calling macros
  • SAS Macros- Concept of local/global variables
  • SAS Macros - Debugging techniques

R-Introduction - Data Importing/Exporting

  • Introduction R/R-Studio - GUI
  • Concept of Packages - Useful Packages (Base & other packages) in R
  • Data Structure & Data Types (Vectors, Matrices, factors, Data frames,  and Lists)
  • Importing Data from various sources
  • Database Input (Connecting to database)
  • Exporting Data to various formats)
  • Viewing Data (Viewing partial data and full data)
  • Variable & Value Labels –  Date Values

R - Data Manipulation

  • Data Manipulation steps(Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type converstions, renaming, formating etc)
  • Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc)
  • R Built-in Functions (Text, numeric, date, utility functions)
  • R User Defined Functions
  • R Packages for data manipulation(base, dplyr, plyr, reshape,car, sqldf etc)

R - Data Analysis - Visualization

  • Introduction exploratory data analysis
  • Descriptive statistics, Frequency Tables and summarization
  • Univariate Analysis (Distribution of data & Graphical Analysis)
  • Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
  • Creating Graphs- Bar/pie/line chart/histogram/boxplot/scatter/density etc)
  • R Packages for Exploratory Data Analysis(dplyr, plyr, gmodes, car, vcd, Hmisc, psych, doby etc)
  • R Packages for Graphical Analysis (base, ggplot, lattice etc)

Basic Statistics - Hypothesis Testing - Statistical Methods

  • Basic Statistics - Measures of Central Tendencies and Variance
  • Building blocks - Probability Distributions - Normal distribution - Central Limit Theorem
  • Inferential Statistics -Sampling - Concept of Hypothesis Testing
  • Statistical Methods - Z/t-tests (One sample, independent, paired), Anova, Correlations and Chi-square

Predictive Modeling - Introduction- Steps

  • Introduction to Predictive Modeling
  • Types of Business problems - Mapping of Techniques
  • Different Phases of Predictive Modeling

Data Preparation for Predictive Modeling - Factor Analysis

  • Need of Data preparation
  • Data Audit Report and its importance
  • Data Preparation steps - Consolidation/aggregation - Outlier treatment - Flat Liners - Missing values- Dummy creation - Variable Reduction
  • Variable Reduction Techniques - Factor & PCA Analysis

Predictive Modeling - Segmentation

  • Introduction to Segmentation
  • Types of Segmentation (Subjective Vs Objective, Heuristic Vs. Statistical)
  • Heuristic Segmentation Techniques (Value Based, RFM Segmentation and Life Stage Segmentation)
  • Behavioral Segmentation Techniques (K-Means Cluster Analysis)
  • Cluster evaluation and profiling
  • Interpretation of results - Implementation on new data

Predictive Modeling - Decision Trees

  • Decision Trees - Introduction - Applications
  • Types of Decision Tree Algorithms
  • Decision Trees - Validation
  • Overfitting - Best Practices to avoid
  • Implementation of Solution

Predictive Modeling - Linear Regression

  • Linear Regression - Introduction - Applications
  • Assumptions of Linear Regression
  • Building Linear Regression Model
  • Understanding standard metrics (Variable significance, R-square/Adjusted R-Square, Global hypothesis etc)
  • Validation of Linear Regression Models (Re running Vs. Scoring)
  • Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc)
  • Interpretation of Results - Business Validation - Implementation on new data

Predictive Modeling - Logistic Regression

  • Logistic Regression - Introduction - Applications
  • Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models
  • Building Logistic Regression Model
  • Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification etc)
  • Validation of Logistic Regression Models (Re running Vs. Scoring)
  • Standard Business Outputs (Decile Analysis, ROC Curve)
  • Probability Cut-offs, Lift charts, Model equation, drivers etc)
  • Interpretation of Results - Business Validation - Implementation on new data

Predictive Modeling - Time Series Forecasting

  • Forecasting - Introduction - Applications
  • Time Series Components( Trend, Seasonality, Cyclicity and Level) and Decomposition
  • Classification of Techniques(Pattern based - Pattern less)
  • Basic Techniques - Averages, Smoothening etc
  • Advanced Techniques - AR Models, ARIMA etc
  • Understanding Forecasting Accuracy - MAPE, MAD, MSE etc

Introduction to Machine Learning

  • Statistical learning vs. Machine learning
  • Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
  • Concept of Overfitting and Under fitting (Bias-Variance Trade off) & Performance Metrics
  • Types of Cross validation(Train & Test, Bootstrapping, K-Fold validation etc)

Regression & Classification Model Building

  • Recursive Partitioning(Decision Trees)
  • Ensemble Models (Random Forest, Bagging & Boosting)
  • K-Nearest neighbours
  • Introduction to Artificial Neural Networks & Support Vector Machines

London Olympics Media Analytics

Data summarization and report generation using the concepts learnt in SAS module.

Laptop Sales Analysis

Data mining the sales transaction data using SAS to find key sales trends.

Credit Card Customers Segmentation

Build an enriched customer profile using intelligent KPIs. Apply advanced algorithms like factor and cluster analysis for data reduction and customer segmentation based on the behavioral data.

Key Drivers for Customer Spending

The objective of this case study is to understand what's driving the total spend of credit card(Primary Card + Secondary card) and identify the key spend drivers . This will require candidates to apply OLS/ linear regression and follow end-to-end model building process

Proactive Attrition Management

Build a logistic regression based predictive model for a telecom service provider to identify churn indicators to predict and proactively manage the customer attrition.

Predicting Loan Default

Apply the logistic regression to identify the risky customers with high likelihood to default on loan repayment.

Time Series Forecasting

Use time series analysis to forecast the outbound passenger movement for next four quarters..

Online Retail Analysis

Use Proc SQL for mining the online retail data to answer the key business questions

Access to 72 hours instructor led live classes of 24x3 hours each, spread over 12 weekends

Video recordings of the class sessions for self study purpose

Weekly assignment, reference codes and study material in PDF format

Module wise case studies/ projects

Specially curated study material and sample question for SAS Global Certification

Career guidance and career support post the completion of some selected assignments and case studies

What if I miss a class?

Don’t worry. You will always get a recording for the class in your Learning Management System (LMS) account. Have a look at that and reach out to the faculty in case of doubts. All our live classes are recorded for self-study purpose and future reference, and these can also be accessed through our LMS. Hence, in case you miss a class, you can refer to the video recording and then reach out to the faculty during their doubts clearing time or ask your question in the beginning of the subsequent class.

You can also repeat any class you want in the next one year after your course completion.

For how long are the recordings available to me?

1 year post your course completion. If needed, you can also repeat any number of classes you want in the next one year after course completion.

Virtually the recordings are available to you for lifetime, but for judicious use of IT resources, the access to these recordings get deactivated post 6 months, which can be extended upon requests.

Can I download the recordings?

No. Our recordings can be accessed through your account on LMS or stream them live online at any point of time though.

Recordings are integral part of AnalytixLabs intellectual property by Suo Jure. The downloading/distribution of these recordings in anyway is strictly prohibited and illegal as they are protected under copyright act. Incase a student is found doing the same, it will lead to an immediate and permanent suspension in the services, access to all the learning resources will be blocked, course fee will be forfeited and the institute will have all the rights to take strict legal action against the individual.

What if I share my LMS login details with a friend?

The sharing of LMS login credentials is unauthorized, and as a security measure, if the LMS is accessed by multiple places, it will flag in the system and your access to LMS can be terminated.

Will I get a certificate in the end?

Yes. All our course are certified. As part of the course, students get weekly assignments and module-wise case studies. Once all your submissions are received and evaluated, the certificate shall be awarded.

Do you help in placements?

We follow a comprehensive and a self-sustaining system to help our students with placements. This is a win-win situation for our candidates and corporate clients. As a pre-requisite for learning validation, candidates are required to submit the case studies and project work provided as a part of the course (flexible deadline). Support from our side is continuous and encompasses help in profile building, CV referrals through our ex-students, HR consultants and companies directly reaching out to us.

We will provide guidance to you in terms of what are the right profiles for you based on your education and experience, interview preparation and conducting mock interviews, if required. The placement process for us doesn’t end at a definite time post your course completion, but is a long relationship that we will like to build.

To avoid any kind of ambiguity, we strongly suggest that you go through our course brochures.

Do you guarantee placements?

No institute can guarantee placements, unless they are doing so as a marketing gimmick! It is on a best effort basis.

In professional environment, it is not feasible for any institute to do so, except for a marketing gimmick. For us, it is on a best effort basis but not time – bound – in some cases students reach out to us even after 3 years for career support.

Do you have a classroom option?

Yes we have classroom option for Delhi-NCR candidates. However, most of our students end up doing instructor led live online classes, including those who join classroom in the beginning. Based on the student feedback, the learning experience is same both in classroom and instructor led live online fully interactive mode.

How do I attend the online classes? Are they interactive or self-paced?

We provide both the options and for instructor led live online classes we use the gold standard platform used by the top universities across the globe. These video sessions are fully interactive and students can chat or even ask their questions verbally over the VoIP in real time to get their doubts cleared.

What do I need to attend the online classes?

To attend the online classes, all you need is a laptop/PC with a basic internet connection. Students have often shared good feedback of attending these live classes through their data card or even their mobile 3G connection, though we recommend a basic broadband connection.

For best user experience, a mic-headphone is recommended to enhance the voice quality, though the laptop’s in-built mic works fine and you can ask your question over the chat as well.

How can I reach out to someone if I have doubts post class?

Through the LMS, students can always connect with the trainer or even schedule one-to-one time over the phone or online. During the course we also schedule periodic doubts-clearing classes though students can also ask doubts of a class in the subsequent class.

LMS also has a discussion forum where a lot of your doubts might get easily answered.

Incase you are having a problem still, repeat the class and schedule one-to-one time with the trainer.

What is your refund policy?

  • Instructor Led Live online or Classroom - Within 7 days of registartion date and latest 3 days before batch start
  • Video-based - 2 days

Can I pay in installments?

Yes. While making the fee payment, most of the courses have the installment option.

I am having difficulty coping up with my classes. What can I do?

For all the courses, we also provide the recordings of each class for their self-reference as well as revision in case you miss any concept in the class. In case you still have doubts after revising through the recordings, you can also take one-to-one time with the faculty outside classes during. Furthermore, if students want to break their courses in different modules, they get one year time to repeat any of the classes with other batches.

What are the system requirements for the software?

There is no particular system requirement for this course since the tools required for this course (Excel, SAS and R) can easily be installed on almost every laptop with basic configuration available these days. However, if possible, it is recommended to have 64-bit operating system.

Probably the best institute out there for any data science or big data courses. The faculty has a bag full of experience and knows the industry well. As a result, the course is designed keeping the requirements and challenges in mind one mights face in the industry. The course starts from the very base, so a beginner in programming/data science will face no problems getting on board. The teachers go at a very steady pace and will help you understand anywhere you face any problem. If you are looking to get a SAS or any other certification, they have a lot of material as well as sample tests for you to practice and they will guide you throughout. Placement training is rigorous and they prepare you to face any kind of question that you might face. Their experience in the industry is definitely a major plus. I got a SAS certification and a couple good offers after completing the course. It does boil down to your own hard work but you need the means and direction to work towards, and you will find that here. I would highly recommend AnalytixLabs to anyone looking to start a career in Data Science.

- Shubham Shah (Analyst, PAYBACK)

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