Brochure - Analytics Training

Data Engineers

Analytics Consultants

Data Scientists

Data Creatives

For those who choose to focus on the technical problem of managing big data. Training on Big data, database

technologies & data handling.

For those who choose to focus on the organization and how analytics project yield value. Training on analytics

foundation, analytics solution building, industry specific analytics solutions & analytics best practices.

For those who choose to focus on all aspects of analytics solution building. Training on data integration, statistics, advanced analytics, visualization & analytics tool building.

For those who choose to focus on building analytical solutions. Training on statistics, predictive modeling, machine learning and industry specific analytics frameworks.

Data is everywhere but there is a growing scarcity of talent with skills to make sense of it and generate value. Consumers are sharing more information than ever before on multiple channels and organizations are struggling to use it for improved customer experience and decision making.

At Simplify Analytics we conduct training sessions for future Data Engineers, Data Scientists, Analytics Consultants & Data Creatives.

EMPOWERING PROFESSIONALS WITH ANALYTICS SKILL

TYPES OF TRAINING

We have 4 training courses. These training courses have multiple modules to choose from and highly customizable to participant needs

BUSINESS ANALYTICS TRAINING

Introduction to analytics, statistics foundation, various predictive model building & analytics frameworks.

Implementation using R or SAS. All topics with real life case study, real data & hands on exercises.

MACHINE LEARNING TRAINING

Introduction to machine learning, fundamentals of model building using machine learning, support vector

machines, random forest, naïve bayes, neural networks, implementation using R.

TOOL TRAINING

Training on R / SAS / Tableau / QlikView / MySQL / MS SQL Server / Excel

BIG DATA TRAINING

Introduction to big data & Hadoop, Installation & setup, map reduce concept, pig, hive, impala, spark, Hadoop

administration, data mart to model building

HOW WE INTEND TO ADD VALUE TO OUR TRAINING PROGRAM

MAKING IT RELEVANT FOR THE PARTICIPANTS• We strive hard to make the content and pedagogy relevant for participants. In a corporate setup training session is attended by participants across

levels & functions. Hence one set of training course structure doesn’t fulfil everyone’s end objective • We intend to have different set of modules for different groups of participants. While some fundamental modules can be common to all, there can be

separate modules for freshers with coding background, freshers with non-coding background, senior business users and for hands-on new analytics professionals.

CASE STUDY BASED APPROACH• Most of the analytics training are heavy on statistical foundation with little emphasis on actual analytics solution building. Most of the cases

are based on dummy data which rarely brings out the nuances of real life model building. Simplify Analytics provides consulting as well as training services. This allows us to bring real life rich case studies and real life analytics solution building.

• All our session will have thorough coverage of fundamentals will follow up real life case studies with real data. We can also use organizations real data to build sample analytics solutions during training. We will discuss the real life challenges and how to overcome them. We use ADEPT (Analogy, Diagram, Example, Plain-English description and then a Technical description) technique to simplify complex concepts.

FOUNDATION CODES• Post analytics training session the participants generally find it very difficult to get started. More often than not they spend enormous

amount of time in building standard parts of a solution• We will provide detailed foundation codes with each case study. These codes will be sufficient for any participants to build their own

analytics solutions with minimal changes to the code.

MUTIPLE ASPECTS OF ANALYTICS SOLUTION LIFECYCLE• Usually analytics training programs focus on only one aspect of analytics like – statistical model building or tool training. They generally do not focus on

data management or visualization or dashboarding. They are usually conducted as separate training programs.• We will cover all aspects of an analytics solution build including data management fundamentals, data management tool training, analytics solution

building, analytics tool, visualization and application development.

BUILD YOUR OWN TRAINING PROGRAM• We have 9 learning areas and 95 modules across all these learning areas.• One can build a training a program by picking any number of modules across these learning areas. We can create highly flexible training programs to

suit participating team specific needs.

TRAINER PROFILES

Educational backgroundTop institutes like – IITs, ISI,

DSE & IIM.

Work experienceWorld’s leading consulting firms –

Accenture Management Consulting, BCG & McKinsey

AwardsWinner of Dunnhumby India

Hackathon, Winner of Pizza Hut Data Science competition

Student feedback (out of 5)

• Knowledge of subject : 4.9• Course content quality : 4.4• Ability to share practical examples : 4.6• Ability to clear doubts : 4.1

Training experience

• Over 1000+ hours of training• Online and classroom training.• Corporate training at Fortune 500 companies• Analytics programs & guest lectures at leading

B-Schools

SAMPLE COURSE STRUCTURE - Data Analytics with R . 6 DAY/48 HOURS COURSE.

Day 1: Introduction to R- environment• The Workspace • Input/ Output • Useful Packages (Base & other packages) in R • Graphic User Interfaces (R studio) • Customizing Startup• Batch Processing and reusing Results

Day 1: Data Structures• Data structures in R• Vectors, matrices & arrays• Lab: Playing with data types• Factors, Lists, Dataframes• Lab: Practice subsetting• Bracketing• Vector calculation• Arithmetic & logical operators• Lab: Subsetting with logical vectors• Missing, indefinite & infinite values• Subsetting with logical vectors• Module review questions

Day 1: Programming logic• R programming logic• Control flow basics• Lab: Make some loops • Custom functions• Lab: Write a function• More on custom functions• Lab: Writing a summary stats function• Module review questions

Day 4 & Day 5: Classification Techniques• Basics of linear regression, understanding OLS technique, model diagnostics, interpretation of results, model evaluation, model implementation• Lab: Linear regression Case Study• Basics of logistic regression, understanding MLE technique, model diagnostics, interpretation of results, model evaluation, model implementation• Lab: Logistic regression Case Study on cross sell• Lab: Logistic regression Case Study on churn• Lab: Credit risk score building, its interpretation and implementation • Bonus lesson on Machine Learning: Random Forest Classification technique• Bonus lesson on Machine Learning: Lab: Random Forest case study• Module review questionsBonus lesson to be conducted if the class is able to finish the prior topics before scheduled time

Day 2: Packages• Introduction to packages• Loading packages• Lab: Downloading a package• Getting info about package• Hacking functions• Updating packages• Lab: Writing your own library• Module review questions

Day 2: Data manipulation• Sort & rank operations• Lab: Order & identify some data• Data sub setting & aggregation• Lab: Practicing aggregating data• Long versus wide format• Merging data sets• Lab: Practice merging data• Handling missing data &• Removing duplicates• apply(), lapply(), tapply(), by(), replicate()• Lab: Calculate average velocities• Lab: Demonstrate CLT with replicate• Module review questions

Day 2: Visualization• Basic use of plots• Lab: Get familiar with plots• Visualizing data• Lab: Graph performance data• Introduction to ggplot2• Lab: Play with ggplot2• Dot Plots – Bar Plots – Line Charts – Pie Charts – Boxplots – Scatterplots • Lab: Chart exploration• Lab: Plotting on a map

Day 6: Time Series Forecasting • Time Series Introduction • Forecasting using moving averages• Forecasting using exponential smoothing• Goodness Metrics: MSE, MAPE, RMSE• Lab: Forecasting the same data using multiple methods and selecting the best model• Forecasting using ARIMA• Lab: Sales forecasting using ARIMA• Lab: Forecasting case study with external variables (Deciding forecasting level, variable selection, variable manipulation, modeling process, model evaluation)

SAMPLE COURSE STRUCTURE - BUSINESS ANALYTICS TRAINING

Number of

sessionsModule Description

1

Introduction to analytics

Evolution of analytics, types of analytics, business domains in analytics. Current challenges and issues impacting clients. How to utilize diverse data sources to provide analytics solutions. Introduction to data

quality concepts. Introduction to advanced data analytics concepts. Basic Big Data concepts and the wide variety of alternative database technologies including columnar databases, distributed storage and

processing systems, in-memory storage systems, and graph databases.

2 Statistics and analytics

Introduction: Overview of key concepts of statistical analysis, data mining and research design that provide a necessary foundation for analytics solution building.

Descriptive stats & EDA: Overview of descriptive statistics and EDA so one can explore the data before building formal models. It will also help us uncover problems and outliers and get a better understanding

of how data was generated and sampled.

Basic analytical techniques 1: Statistical tests with one variable to analyze the data and realize significance of our results.

Basic analytical techniques 2: Statistical test to compare 2 or more sample statistics in order to draw appopriate conclusions.

Advanced analytical techniques: Statistical tools to identify, analyze and quantify relationships.

Model building techniques: Introduction to predictive model building and steps to take to build valid and robust models.

1 R for analytics The module will equip you to build your own analytics solution using R. This will cover the basics of R platform, data structures, subsetting & aggregation, control-flow & functions, sql integration.

1

Data management for

analytics

Data management forms the foundation of any analytics solution building. The course will provide the following information -

Database modeling: Introduction to database design process & dimension modeling.

Data integration: Extraction, transformation & loading of data on-premise & on the cloud.

Data quality: Data in real life is seldom in the form that can be used directly for analytics solution building. This module will give you an introduction to how to measure data quailty and steps you can take to

increase data quality.

Introduction to SQL for analytics: Learn basic SQL commands to manipulate data and build analytical datamart.

1

Data visualization

The Introduction to Data Visualization course will provide you with an understanding and pragmatic perspective of data visualization and a view on the three basic questions: What is Data Visualization? Why is

Data Visualization needed? How to approach Data Visualization?

Storytelling with data: Storytelling is a powerful way to share data insights. We will learn the essential elements to tell our stories from data.

Visualization using R included.

1 Linear Regression Learn the fundamentals of linear regression, interpretation of model statistics and steps to building a good linear regression model. R case study included.

2 Logistic Regression Learn the fundamentals of logistic regression, interpretation of model statistics and steps to building a good logistic regression model. R case study included.

1 Clustering Learn multiple clustering techniques and Principle Component Analysis (PCA). R case study included.

2 Time series analysis Learn basic concepts of time series modeling, multiple modeling techniques of time series, ARIMA modeling, model estimation & interpretation. R case study included.

1 Decision trees Learn CHAID and CART techniques. R case study included.

1 Text mining This module will provide the basic components, concepts & tasks involved in text mining. It will also include a case study using R.

1 Optimization Introduction to linear programming problems and non-linear programming problems. Case studies to solve optimization problem using R.

1 Marketing analytics This module provides the knowledge of main concepts and components to measure and increase ROI on marketing spends. R case study included.

1 Operation analytics This module provides main concepts of operation analytics. We will also cover introduction, real world problems and techniques to inventory optimization and demand forecasting.

1 Retail analytics An introduction to retail analytics. It will cover the concepts of category management, space and assortment optimization, pricing and promotion analytics. R case study on market basket included.

1Customer analytics

An introduction to loyalty management. It will help us understand the key concepts to understanding the customers better, proactively grow their value and improve targeted communication. R case study on

segmentation included.

1 Telecom analytics It includes key concepts in telecom analytics like churn management, VAS cross-sell/up-sell, high value customer prediction, etc. R case study on cross-sell included.

Modules for Industry Expert Track

Modules for Advanced Analytics Track

Compulsary modules

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