BDW16 London - Amjad Zaim, Cognitro Analytics: How Deep is Your Learning
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Amjad Zaim, CEO of Cognitro Analytics, discusses the transformative potential of big data and analytics for businesses, emphasizing its role in understanding customer behavior and making strategic decisions. The document outlines Cognitro Analytics' history, its service offerings, and specific case studies demonstrating successful analytics implementations in various industries, particularly in healthcare. Key insights on the challenges and misconceptions surrounding data science, as well as the importance of leadership and culture in driving analytics initiatives, are also highlighted.
BDW16 London - Amjad Zaim, Cognitro Analytics: How Deep is Your Learning
- 1. Amjad Zaim, CEO How Deep Is Your Learning Analytics as an Engine for Business Innovation Amjad Zaim, PhD – CEO Cognitro Analytics
- 2. Amjad Zaim, CEO How Deep Is Your Learning The Big Hype about Big Data
- 3. Amjad Zaim, CEO How Deep Is Your Learning Big Data in the News “Big Data has arrived at Seton Health Care Family, fortunately accompanied by an analytics tool that will help deal with the complexity of more than two million patient contacts a year…” “At the World Economic Forum last month in Davos, Switzerland, Big Data was a marquee topic. A report by the forum, “Big Data, Big Impact,” declared data a new class of economic asset, like currency or gold. “Increasingly, businesses are applying analytics to social media such as Facebook and Twitter, as well as to product review websites, to try to “understand where customers are, what makes them tick and what they want”, says Deepak Advani, who heads IBM’s predictive analytics group.” “Data is the new oil.” Clive Humby “…Big data can play an immeasurable role in helping Middle East firms to compete globally” EMC Survey Reveals Big Data Adoption Trends The Oscar Senti-meter — a tool developed by the L.A. Times, IBM and the USC Annenberg Innovation Lab — analyzes opinions about the Academy Awards race shared in millions of public messages on Twitter.”
- 4. Amjad Zaim, CEO How Deep Is Your Learning Are Organizations Really Uncovering the Information “Gold” !!! Sometimes people aren’t who they appear to be Do you know thiscustomer? Do you know how to market tohim? Do you know what productbundles to offer him and when? Can you predict hisneeds? Can you exceed hisexpectations? Can you adapt to his lifestyle and make him your advocate
- 5. Amjad Zaim, CEO How Deep Is Your Learning Big Data is All About Deriving “New”, “Interesting” and “Relevant” Intelligence Avid Tweeter, Facebooker, blogger, online reviewer and sports enthusiast
- 6. Amjad Zaim, CEO How Deep Is Your Learning Cognitro Analytics is a consulting firm with 10+ years of providing innovative Big Data and Advanced Analytics services 2011 – 20122005 – 2006 Cognitro Analytics team establish the (Vision, Intelligence and Bio- Informatics) research center of the University of Texas 2006 – 2007 Cognitro Analytics moves its offices to New York City to support clients in the East Cost 2016– 2017 Cognitro Analytics receives external funding support from the National Science Foundation Cognitro Analytics receives an LLC incorporation from the State of Delaware 2009 – 20102007 – 2008 Cognitro Analytics successfully patents, packages and rolls out its first predictive analytics software to support its mission Cognitro Analytics International is established to support clients in the MENA and GCC region through a Dubai office
- 7. Amjad Zaim, CEO How Deep Is Your Learning We have offices in NY and Dubai serving clients in the US & internationally with end-to-end analytics offerings Strategy Capability Building & Execution Algorithm & Analytics Development Analytics Library Data Catalog Leadership & Organizational Strategy Analytics Capability Assessment Interactive Customizable Dashboards Decision Support & COE Design Analytics Assessment & Roadmapping Empire State Bldg. 350 Fifth Avenue, 59th floor New York, NY 10118 Tel: +1-844-COGNIT 203 HSBC Bldg. Sheik Zayed Rd. Dubai, UAE International Offices
- 8. Amjad Zaim, CEO How Deep Is Your Learning We have been pushing the analytics rigor to address increasingly complex questions confronting our clients How can we optimize the hospital length of stay based on patient medical history? Who are more likely to be readmitted to the hospital? Who are at risk of developing diabetes? What’s the optimal pricing point that will maximize likelihood of purchase? What are people saying about my products on social media? What are some of the early signs of money laundry? What are some of the early indicators for insurance fraud? Who’s more likely to cheat on their tax returns? How can I score my bank clients based on likelihood to default on their loans? What’s the maximum waiting time in my branch that clients can tolerate? What are some of the early indicators of anti-money laundry? Advanced Big Data Analytics What industries should I prepare my labor force for the next 5 years? What makes students drop out in the 2nd year of college? Who are more likely to be un- employed over the next 2 years? What’s causing my customers to defect to other telecom carriers? How can I predict shopping patterns based on location data? What’s the best over-booking strategy for my airline?
- 9. Amjad Zaim, CEO How Deep Is Your Learning What is the DNA of a “Real” Data Scientist? But the biggest question we’ve been able to address is the single most important factor in the data science journey Is there a Risk of a Financial Crisis? Is There A National Security Threat? How is the National Healthcare System Performing? What Are the Signs of a Heathy Economy? What Are Driving Economic Indicators?
- 10. 10 Is there a Data Science “Unicorn”? ComplexityofData Descriptive Predictive Prescriptive Portfolio, product or economic prediction Enabling smart decisions based on data Mining data to provide business insights Time Series Forecasting Bayesian Inference Factor Analysis Sensitivity Analysis Principal Component Analysis Regression Analysis Graphical Modeling Multivariate Statistics Linear Discriminant AnalysisGeospatial Analysis Experimental Design ANOVA Optimization Monte Carlo Simulation Classification (e.g., SVM) Boundary Value Solutions Classification Integer Programming Operations Research Monte-Carlo Simulation Clustering Parameter Search Agent Based Modeling Neural Networks Genetic Algorithms Image Processing Computer Vision Discrete Choice Models Big Data Processing ERP Database Reporting System Dynamics Discrete Event Simulation High Volume, Variety, Velocity, and Veracity Structural Relational Limited to None Deep Learning Source: BAH Analysis NON-EXHAUSTIVE
- 11. Amjad Zaim, CEO How Deep Is Your Learning Case Study: Measuring the “Cognitive Myopia” Personal Attributes Expertise Level, Education Level, College Degree, Years of Experience Environmental Attributes Consultant, Employee, Academic/Researc h Behavioral Attributes Learning Methodology, Skills- Development Approach 150 data scientists involved in developing analytical model from 27 different banks. Case Study: Measuring the “Cognitive Myopia”
- 12. Amjad Zaim, CEO How Deep Is Your Learning • Available Algorithms • User-Interface Quality • Computing environment • Output Visualization Software Application • Algorithm Choice • Model Parameters • Predictive Power • Generalization Capability • Model Interpretability Data Mining Model • Target Variable • Output Accuracy • Deployment Methodology Output Solution • Sampling Methodology • Testing Data Subset • Training Data Subset Sampling, Testing and Training • Data Size • Variable Dimensionality • Data Granularity • Data Quality Input Data • Business Goal (Maximizing Sales) • Data Mining Goal (Classification, Segmentation, Clustering) Business Problems Measuring Data Mining Myopia Data Scientist • Expert Level • Scientific Background • Analytics environment • Analytics Intuition What Influences Analytical Modeling? Case Study: Measuring the “Cognitive Myopia” Case Study: Measuring the “Cognitive Myopia”
- 13. Amjad Zaim, CEO How Deep Is Your Learning Measuring Data Mining Myopia Most commonly used algorithm amongst Data Scientists • Regression and decision tree are the most commonly used models overall. 0 50 100 150 200 • Average practitioners use more simple built-in models than what advanced users and researchers use 0 50 100 150 200 Regression Decision Tree Nueral Nets Bayesian SVM Rule Induction Genetic Algorithm Ensemble Modeling Practitioner Advanced Researcher Case Study: Measuring the “Cognitive Myopia”
- 14. Amjad Zaim, CEO How Deep Is Your Learning Measuring Data Mining Myopia Some indicators that cognitive myopia exists •Modelers who are consultants (Environmental attribute) with engineering/computer science background (personal attribute) apply, validate, and deploy a wider variety of analytical models. •Modelers with post-graduate degrees in business/economics (personal attribute) and 7+ years of practical analytics experience (personal attribute) develop more successful models…(perhaps specific to banking ???). •Modelers who are employees (Environmental attribute) with more than 10 years of experience (personal attribute) have not changed their analytical approach in the past 3 years. Case Study: Measuring the “Cognitive Myopia”
- 15. Amjad Zaim, CEO How Deep Is Your Learning 0 5 10 15 20 25 30 35 Undergrad Education Post-Graduate Attended Workshops Online Research Social Media Practitioner Advanced Users Researchers Which behavioral attributes are most related to model effectiveness – deployment So who should you hire as a data scientist ??? Case Study: Measuring the “Cognitive Myopia”
- 16. Amjad Zaim, CEO How Deep Is Your Learning But it’s more than just data scientists ! The Journey of Analytics Transformation Transform and Digitize Inform and Contextualize Embed and Institutionalize Innovate and Evangelize Reporting Discovery Monitoring Prediction Business Value and Decision-Support Low High Integrated, clean and accurate data Analytical tool with data exploration and visualization Real-time Analytics data collection and visualization capabilities Advanced predictive modeling tools What happened yesterday? OLAP (search query, Drill- down Analysis) Why did it happen yesterday? (correlation, association, factor analysis) What is happening now? (dashboard, scorecards, Self-Service BI) What will happen tomorrow? (Classification, Forecasting) DescriptionPre-Requisites Prescription Simulation and scenarios optimization How to Influence tomorrow outcomes? (Uplift Modeling, Optimization ) ? ? Data Queries Descriptive Analytics Business Intelligence Predictive Analytics Prescriptive Analytics Analytics Transformation Chain Pushing the Analytics Envelop
- 17. Amjad Zaim, CEO How Deep Is Your Learning The Big Picture: National E-health Case Study Patient EMR, HR, Pharmacy, Referrals 27 hospital and clinics 5.7M digital patients Medical Records, HR, and Financials 53 Terabytes of data every 1) The USAID-Funded project helped the Palestinian MOH standup a country-wide national Health Information System Transform and Digitize Inform and Contextualize Incubate and Institutionalize Innovate and Evangelize Comprehensive Patients digital footprints with all clinical transactions National E-health Case Study
- 18. Amjad Zaim, CEO How Deep Is Your Learning 2) Light does of analytics was initially applied to demonstrate the use of data in making strategic impact ` Predictive Resource Optimization and Capacity Planning What are the main regions suffering from a supply gap? Which regions are expected to face gaps in the future? What are the main regions and specialties with an oversupply of healthcare facilities Which prevalent medical conditions are not being effectivelytreated MOH Geo-spatial Clustering Facilities Beds Physicians Nurses Inpatients Outpatients ResourcesMetrics HR Metrics Predictive Resources and Capacity Planning Transform and Digitize Inform and Contextualize Incubate and Institutionalize Innovate and Evangelize Analytics Warmup Two hospitals with optimized locations built via donors funds National E-health Case Study
- 19. Amjad Zaim, CEO How Deep Is Your Learning 2) Analytics was then rolled out and incubated to multiple units within the MOH Predictive Readmission Actors Admission and Discharge BeneficiaryAttributes Claim Date Claim Amount Provider ID Diagnosis Code Procedure Code Date/Time Stamp Pharmacy Code Prescribed Medication Patient 1 Patient 2 Patient 3 Patient 4 Patient 7 Patient 6 Patient 9 Patient 8 Low Risk High RiskMedium Risk Patient 5 A scoring engine was used to understand areas of health risk and to predict and mitigate the risk of costly re-admission trough referrals programs Transform and Digitize Inform and Contextualize Incubate and Institutionalize Innovate and Evangelize Data Science At Work Model was used to improve referrals by 35% to hospitals outside the MOH network National E-health Case Study
- 20. Amjad Zaim, CEO How Deep Is Your Learning Transform and Digitize Inform and Contextualize Incubate and Institutionalize Innovate and Evangelize Health Decision Support Center National E-Health Committee Health Analytics Competency Center Patient EMR, HR, Pharmacy, Referrals • Provide performance benchmarking ad-hoc reports as well as on-demand data services. • Drive the integration of health data from HIS into the national health quality performance • Support the donor community w insights to make evidence-based decisions yo support of expansion and the development of healthcare • Provide performance benchmarking ad-hoc reports as well as on-demand data services. • Provide the health community with information on health quality and safety. • Generate insights used to inform policy making • Coach and train data analysts 3) Analytics and Data Governance were then setup to ensure stability and sustainability Scaling Up MOH taking ownership of their own data National E-health Case Study
- 21. Amjad Zaim, CEO How Deep Is Your Learning Transform and Digitize Inform and Contextualize Incubate and Institutionalize Innovate and Evangelize Controlling C-Sections increases compliance with international best practices and introduces a cost saving of over $1.5M 4) Analytics was combined to Hospital Data and Geolocation data “Innovating Analytics” Pregnancy is the 2nd leading contributor to people movements after cancer National E-health Case Study
- 22. Amjad Zaim, CEO How Deep Is Your Learning Transform and Digitize Inform and Contextualize Incubate and Institutionalize Innovate and Evangelize Post-Visit Local Clinics Vitamins Medication Doctor Communication Exchange messages Pregnancy Planning Ovulation Calendar Shopping Search Official complaints Social media/blog reviews Doctor visits Emergency visits Reviews Rating Perceptions Satisfaction Pregnancy PeriodPre-pregnancy Information Product Health tips 4) Generated Insights led to the creation of GraviLog, a health smartphone application that created a new wealth of data “Innovating with Analytics” “Health Analytics Driving Healthy Innovation”
- 23. Amjad Zaim, CEO How Deep Is Your Learning In turn…data emerging form GraviLog has the potential to be fused with doctors and hospital data for a wide variety of health predictions ….and the journey continues Predicting pre-mature labor Predicting the timing of the delivery Predicting high risk deliveries Food & Diet Medication Exercises and Lifestyle Doctor Communication Major complaints Pregnancy complications Previous pregnancies Medical history Transform and Digitize Inform and Contextualize Incubate and Institutionalize Innovate and Evangelize
- 24. Amjad Zaim, CEO How Deep Is Your Learning The 3 ROI’s Return on Investment Return on Insights Return on Innovation
- 25. Amjad Zaim, CEO How Deep Is Your Learning Lessons Learned: 1) For innovation, technology is only minor to governance, competency and culture Technology (Infrastructure & Tools) Data Governance Analytics Competency Culture (Decision Making & Actions)
- 26. Amjad Zaim, CEO How Deep Is Your Learning Lessons Learned: 4) It’s all about Leadership “If we have data, let’s look at data. If all we have are opinions, let’s go with mine” Jim Barksdale, ex-CEO of Netscape
- 27. Amjad Zaim, CEO How Deep Is Your Learning But it won’t happen overnight…It needs to one part of the analytics transformation journey!
- 28. 28 To harness the full power of data, you needs to upgrade its analytics capabilities into a more Predictive State Cognitro Analytics Big Data Analytics Services Reporting Discovery Monitoring Prediction Low High What happened yesterday? OLAP (search query, Drill-down Analysis) Why did it happen yesterday? (correlation, association, factor analysis) What is happening now? (dashboard, scorecards, Self-Service BI) What will happen tomorrow? (Classification, Forecasting) Description Prescription How to Influence tomorrow outcomes? (Uplift Modeling, Optimization ) ?? Data Queries Descriptive Analytics Business Intelligence Predictive Analytics Prescriptive Analytics You Today You Tomorrow Data/Information Provider Decision Support Center
- 29. Amjad Zaim, CEO How Deep Is Your Learning We developed a comprehensive set of next-generation analytic offerings & end-to-end data science solutions Cognitro Analytics Big Data Analytics Services Engage Empower EnergizeEnable Educate Offering Capability Assessment Executive workshop Training & Scaling Capstone Project Competency Model Use Case Prioritization Data Identification, Acquisition & Gaps Management & Governance Pilots & Prototypes Review State Assess Maturity Manage Transformation Inform Strategy & Roadmap Data Analytics Stand-Up & Scale Up Realization of Total Data Value Igniting Data Science Talent “What Can Data Do for me” “How much is my data worth” “How to build my talent base?” Models, Algorithms & Analytic Techniques Data Visualization Technology, Tools and Infrastructure Insight to Action & RFP Support Enablement through Data Science “How to solve the previously impossible” Impact, Outcomes, & Storytelling Insights to Innovation Data Monetization Organizational Constructs Community Outreach “Why open data and insights?” Solutions (Services + Products) Cognitro Analytics Offerings
- 30. Amjad Zaim, CEO How Deep Is Your Learning Stage 1 Analytically Impaired Stage 2 Localized Analytics Stage 3 Analytical Aspirations Stage 4 Analytical Adopters Stage 5 Analytical Frontrunners Inconsistent data, poor data quality, or poorly organized data Useable data organized in functional or process silos Organization has nascent centralized data repository Integrated and accurate central data warehouse Relentless search for new data and metrics Lack of analytics Analytics focus is dispersed across multiple targets, which may not be strategically important Analytical efforts coalesce around a set of unified targets Analytical activity centered around defined key domains Analytics support the firm’s and strategy and further the firm’s mission No clear procedure for data access and dissemination of information Informal understanding of data usage and access policies Inconsistent but existing documentation of policies and standards Clear and complete documentation of data policies, e.g. data privacy, access, etc. Automated data access procedures Lack of technology Data, technology, and expertise in disparate clusters across organizations Early stages of an enterprise-wide approach Key data, technology and analysts are centralized or networked All key analytical resources centrally managed No awareness or interest Interest exists only at the function or process level Leaders recognize the importance of analytics Leadership provides support for analytics Leadership actively encourages and supports analytics and exploration Few analytical skills Isolated pockets of analysts with little communication Analysts clustered in key target areas Highly capable analysts in central or networked organization Professional analysts with strong training and support for amateur analysts For fresh-starters, we can support in assessing standing, building strategy and championing and evangelizing analytics from within Engage Empower EnergizeEnable Educate Data Analytics Stand-Up & Scale Up Where is your organizationtoday? Review State Assess Maturity Manage Transformation Inform Strategy & Roadmap Assessing Analytics Standing Maturity AssessmentState Assessment Data Governance Analytics Governance & Leadership Policy and Security Technology Culture People 1 2 Analytics Strategy & Roadmap Build decision support unit Customer Analytics Proof of Concept Implement Advanced Analytics Use Cases Train data scientists on machine learning and text analytics 1 2 3 4 4 Adoption & Transformation Executive Workshops Study Tours Analytics ROI 3 1 2 3 4 Cognitro Analytics Big Data Analytics Services
- 31. Amjad Zaim, CEO How Deep Is Your Learning We can also help organization build analytics momentum, qualify data, setup governance and test-drive analytics Engage Empower EnergizeEnable Educate Realization of Total Data Value Management & Governance Use Case Prioritization Pilots & Prototypes Analytics Blueprinting Qualifying Analytics Use Case Selection Data Availability Is the data available? How easy is it to collect? Capability Maturity Does organization possessthecapability (e.g. skills, tools) to implement? Business Need Is it required by many internal stakeholders? Business Value What is the businessvalueof theuse case? Levelof Impact Levelof Maturity Enrich Integrate and Transform Data Reveal trends Identify correlations Learn patterns Classify Signals Predict Risks Forecast Resources Explore Discover Predict Proof of Concept Advanced Analytics Delivery Team Project Setup • Understand the industry and business challenges • Coordinates requirements gathering Data and Analytics Governance • Deliveringthe right data products to the right people • Locates and obtainsrequired data Data Gap Assessment • Discover and bridge the data qualitydivide A B D D Data Science Team • Matching analyticsskills to the environment Analytics Initiation Blueprinting ActivitiesPlanningand Relationship Expertise and Manpower Implementationand Execution 4 3 Technologyand Resources 1 2 3 4 1 2 Cognitro Analytics Big Data Analytics Services
- 32. Amjad Zaim, CEO How Deep Is Your Learning Engage Empower EnergizeEnable Educate Enablement through Data Science Models and Algorithms Technology & Infrastructure Data Visualization Insight to Action Try and Iterate Model Developmentand Analysis Model EvaluationData Preparation 1 Select algorithm and parameters and run model Analyze and interpret results Back-test Model Test Scalability Evaluate Model Fit Define computed variables Develop Data Frame with Input features Divide Data into Test and Train Sets Regression Algorithm Bayesian Algorithm Random Forest Algorithm Knowledge Discovery Configuration Management Package Management Accumulo ElasticSearchHadoop / YARN Workflow Unstructured Data Rapid Source Configuration Structured Data Dashboards and Visualizations PredictiveAnalyticsNatural Language Processing Machine Learning Using common open source analytics and visualization tools, we can help our clients harness the full powerful of cost-effective tools 2 3 Insight to Action Integrationwith Business and Mission Business Rules Automation Data Visualization Dash boarding and BI Technology and ToolsAnalytics Modeling Techniques Procurement and Deployment 1 2 3 4 1 2 3 4 Cognitro Analytics Big Data Analytics Services
- 33. Amjad Zaim, CEO How Deep Is Your Learning Engage Empower EnergizeEnable Educate Enablement through Data Science Models and Algorithms Technology & Infrastructure Data Visualization Insight to Action Cognitive Modeler We have developed a “Cognitive Modeler” tool to automate the most complex task of the data science journey 1 2 3 4 Golden Insights Text Messages Geospatial Data Media Online History Health Data Weather Data Crimes Data Learn PrioritizeIngest Modeling-Support System Regression Algorithm Bayesian Algorithm Random Forest Algorithm ModelAccuracy Golden Insights Financial Data Social Network Data Customer Data Call Data Records Model Development and Analysis Model EvaluationData Preparation 1 Select algorithm and paramete rs and run model Analyze and interpret results Back-test Model Test Scalabilit y Evaluate Model Fit Define compute d variables Divide Data into Test and Train Sets 2 3 Cognitro Analytics Big Data Analytics Services
- 34. Amjad Zaim, CEO How Deep Is Your Learning To ensure sustainability, we work with our clients to transfer and incubate analytics skills through our capacity building program Capability Assessment Executive workshop Training & Scaling Capstone Project Competency Model Engage Empower EnergizeEnable Educate Igniting Data Science Talent 1 Stage 1 Analytically Impaired Stage 2 Localized Analytics Stage 3 Analytical Aspirations Stage 4 Analytical Adopters Stage 5 Analytical Frontrunners Analytics & Data Governance • What data exists and how is it secured? • How does it move between systems, and what transformations were applied in the process?. What rules can be applied systematically in the capture, monitoring, and measurement of data assets? How can data be kept secure. • Who is accountable for maintaining it? How should information be used by employees, customers, and partners? • How to develop an ownership matrix for creating and maintaining the various types of information? Analytics Culture and Leadership • What’s the DNA of analytics leadership? • How to setup an analytics program to derive quick-wins • How to inspire change within the organization • What’s the best approach to embed analytics into the fabric of your organizational (Centralized vs. decentralized)? • How to setup a center of excellence to spur analytics from within? Analytics Technology • How to collect, integrate and consolidate data from multiple data sources? • How to organize data in the data warehouse and big data technology • How to program with different analytical tools such as R and Python? • How to push the discovered insights into the business environment for decision making? • How to streamline the data from the staging area to the analytics platform? Analytics Craftsmanship and People How to explore and assess the data? How to clean and reshape the data so its’ ready for analysis? How to apply basic trend analysis and produce descriptive insights? How to build predictive and prescriptive models to advance the value of insights? How to apply Natural Language Processing to analyze unstructured text data? Executives Managers Practitioners Cognitro Analytics Capacity Building Approach Cognitro Analytics Big Data Analytics Services
- 35. Amjad Zaim, CEO How Deep Is Your Learning We also work with clients to put their analytics knowledge and skills to test to help shape their capacity to produce intelligence Capability Assessment Executive workshop Training & Scaling Capstone Project Competency Model Engage Empower EnergizeEnable Educate Igniting Data Science Talent Cognitro Analytics Capacity Building Approach 1. Development & delivery of course pack including training goals & objectives 2. Conduct survey-based prequalification 3. Highlight Data Science career outlook Pre-Training (5 days) Executive Workshop (1 days) 1. Demystify analytics to senior leadership 2. Identify existing analytics gaps within organization 3. Recommend courses & pre- select target groups Practitioner Training (15 days) On-the-Job Coaching (10 days) 1. Build the theoretical background of analytics 2. Demonstrate the analytics value through 3 use cases 3. Conduct hands-on sessions to facilitate “learning-by-doing” 1. Develop a capstone project as an analytics exercise 2. Provide on-site supervision on applying gained skills 3. Assess the learning outcome & recommend enhancement A B C D 2 43 2 3 4 Cognitro Analytics Big Data Analytics Services
- 36. Through partnership with leading analytics academic and research institutions, we can offer our clients world-class training programsEngage Empower EnergizeEnable Educate Igniting Data Science Talent Cognitro Analytics Standard Data Science Training Program Capability Assessment Executive workshop Training & Scaling Capstone Project Competency Model 5 Beginners Level Track Practitioner Training Intermediate Level Track Advanced Level Track • Navigatethe data dimensionalspace and understand distribution • Develop basic analyticsmodeling techniquesto uncover root causes 1. Fundamentals of Data Science 2. Data QualityManagement 3. EssentialData Mining Tasks and Algorithms 4. Big data and Hadoop Technology 5. Data & AnalyticsGovernance 6. AnalyticsProject Management 7. Data Monetization LearningGoals Non-TechTech Sample Courses Dat a Structured data • Develop predictive modeling • Apply machine learning algorithms • Develop social network analytics • Learn Big Data basics & principles 1. Statistical-BasedAlgorithms 2. Learning-BasedAlgorithms 3. Social Graph Theory 4. Business Intelligence& Data Visualization 5. Standing Up the AnalyticsEnvironment 6. AnalyticsLeadership and CDO 7. AnalyticsStorytellingand Presentation • Learn best practicesfor tacklinga business analyticsproblem • Manage end-end analyticsproject • Build analyticscompetencies Structured and Big Data • Master main optimizationschemes • Understand text mining basics • Conduct social sensing and sentiment analytics 1. Natural Language Processing 2. Simulation and Linear Programming 3. Fuzzy Logic and Rule Sets Optimization 4. Real-timeAnalyticsfor IOT data 5. AnalyticsTalent Management 6. BuildingAnalyticsCenter of Excellence 7. AnalyticsFueling Business Innovations • Build collaborativeand mature analytics programs Unstructured text and Big Data Executive Track Online Courses Classroom-Based Courses Technical Track Team-Level Project-Level Organization-Level Cognitro Analytics Big Data Analytics Services