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Advanced Analytics and Data Science Expertise

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An overview of SoftServe's Data Science service line.

- Data Science Group
- Data Science Offerings for Business
- Machine Learning Overview
- AI & Deep Learning Case Studies
- Big Data & Analytics Case Studies

Visit our website to learn more: http://www.softserveinc.com/en-us/

Veröffentlicht in: Daten & Analysen
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Advanced Analytics and Data Science Expertise

  1. 1. Advanced Analytics and Data Science Expertise Iurii Milovanov Lead Data Scientist April, 2016
  2. 2. Today, SoftServe is a leading technology solutions company with 4,000 employees, specializing in software product and application development and services.
  3. 3. Global Locations 29 offices 8 countries
  4. 4. Data Science Group
  5. 5. Data Science Group Iurii Milovanov Lead Data Scientist Tetiana Gladkikh Data Scientist, Competency Manager Roman Grubnyk Data Scientist Ihor Kostiuk Data Scientist Taras Hnot Data Analyst Volodymyr Solskyy Data Analyst Pavlo Kramarenko Data Analyst, BI Consultant
  6. 6. Core Competency Artificial Intelligence State-of-the-art Machine Learning Deep human-level Insight Unstructured and High-dimensional data High Performance Computing Big Data Apache Hadoop Ecosystem Data Collection and Augmentation Big Analytics Real-time & Batch Data Processing Predictive Analytics Forecasting Risk Analysis Cluster Analysis Decision Support Systems Data Analysis Data Exploration Statistical Inference Visualization Business Intelligence
  7. 7. Domain-Specific Expertise • Computer Vision – deep image and video understanding • Natural Language Processing – human language processing and understanding • Speech Recognition – spoken language processing (speech-to-text and text-to-speech) • Social Media Analysis – web mining, behavioral analytics, and social network analysis • Recommender Engines – help users find content they might like by making automatic personalized recommendations
  8. 8. Data Science Toolkit
  9. 9. Methodology & Typical Roadmap Initial Stage Research Phase Prototyping Data Collection Data Exploration Data Modelling Result Communication Performance Tuning Model Integration Deployment Phase Evaluation Inputs: • Problem definition • Initial requirements Outputs: • Data processing model • Final requirements 1 2
  10. 10. Tiny Neural Network Framework TNNF – an open source GPU-friendly Deep Learning library developed by Data Science Group @ SoftServe
  11. 11. Data Science Offerings for Business
  12. 12. Data Science in Retail Business Area: • Customer 360 view • Product recommendation • Direct marketing • Opinion mining • Sales analytics • Logistics optimization Improves customer and business insights, provides a deep understanding of customer’s profile and behavior.
  13. 13. Data Science in Healthcare Business Area: • EMR processing • Patient monitoring • Biometric data analytics • Decision support systems • Computer-aided diagnosis • Precision medicine Helps physicians make better decisions across the board – from personalized treatments to preventive care.
  14. 14. Data Science in Telecom Business Area: • CDR analytics • Geospatial analysis • Anomaly and fraud detection • Network optimization Applies real-time and batch predictive analytics to analyze subscriber behavior and create individual network usage policies.
  15. 15. Data Science in HR Business Area: • Workforce analysis • Capacity management • Employee retention • Talent analytics • Resume screening Provides a deep insight on company's employee profile in order to help HR department in solving employee-focused challenges.
  16. 16. Data Science in Social Media Marketing Business Area: • Social profiling • Information flow analysis • Promotion optimization • Community detection • Behavioral analytics Discovers hidden trends, patterns and relationships in social media in order to enable micro-market campaign management, maximize engagement and optimize social promotion strategy.
  17. 17. Data Science in IT & Security Leverages ultra-large volumes of data from IT Infrastructure, improves overall service availability and reduces time required for root cause analysis. Business Area: • Operations analytics • Network log analysis • Anomaly and Intruder detection • Cloud optimization
  18. 18. Data Science in Finance Gives a significant competitive advantage by incorporating new types of unstructured and semi-unstructured data into financial decision-making, building predictive models and live market simulations. Business Area: • Financial forecasting • Price optimization • Risk management • Fraud detection • Bitcoin analytics
  19. 19. Machine Learning Overview
  20. 20. Premise of Machine Learning Complex problems (such as image, text or speech processing) usually are: • High-dimensional (1000+ dimensions) • Poorly defined, since we still don’t know how its done in our brain Therefore, hand-coding for such problems suffers a 'complexity collapse' and is not really feasible
  21. 21. Basic idea of Machine Learning Training Data Learning Algorithm Model Prediction Engine New Data Predictions Instead of writing a program by hand, we use a set of observations to uncover an underlying process which can be generalized to a new data CAVEAT: Although Machine Learning has been already proved to be theoretically feasible, we need efficient algorithms to uncover complex patterns and relationships in data Testing Data
  22. 22. AI & Deep Learning Application Domains: • Image Classification • Object Recognition • Motion Detection • Speech-to-Text • Emotion Recognition • Robotics Deep Learning – family of Machine Learning techniques inspired by cognitive and neuroscience, decent state-of-the-art in Artificial Intelligence
  23. 23. Successful applications of Deep Learning • Apple, Google and Baidu use Deep Learning for speech recognition • Content recommendation engines at Amazon, Netflix and Google highly rely on Deep Learning • Facebook applies Deep Learning to facial detection and recognition • Twitter analyze their twit-database using DL techniques • Deep Learning plays an important role in fraud detection at PayPal
  24. 24. Biggest challenges in Machine Learning • Training data • Noisy and missing values • Model generalization • Non-convex optimization • Hyperparameters tuning • Result interpretation • Computational resources
  25. 25. GPU-accelerated Computing  Perfectly fits to iterative Machine Learning algorithms  Gives an approximately up to 40x speedup on training time  Inherently more energy efficient than other ways of computation  CUDA – general purpose processing framework developed by NVIDIA Where GPUs are deployed:
  26. 26. AI & Deep Learning Case Studies
  27. 27. Case Study: X-Ray Image Recognition Technologies:  Matlab/Octave  Python  Deep Learning  Probabilistic modeling Business Area: Healthcare. Computer-aided diagnosis system (CADe) that can recognize human body part on X-Ray image and detect broken or fractured bones Analytical Engine This is a hand. Broken bone detected X-Ray Image
  28. 28. Case Study: Image Object Recognition Business Area: Retail. Software solution to analyze and recommend optimal products placement on store shelves Key Steps:  Preprocessing – scaling, normalization etc.  Segmentation – define areas of interest  Recognition – where is the product located  Classification – what kind of product we can see
  29. 29. Case Study: Smart Agents, DRLearner.org Business Area: DRLearner is SoftServe’s open source implementation of the deep reinforcement learning algorithm for game playing, invented by Google DeepMind. This is a successful approach to mimic aspects of human brain to solve complex problems such as autonomous car control Techniques:  Convolutional Neural Networks  Reinforcement Learning  Python  TNNF/Theano
  30. 30. Big Data & Analytics Case Studies
  31. 31. Case Study: Social Trends Analysis Business Area: Distributed solution to monitor and analyze customers' opinion on Ukrainian banking industry Key Steps:  Web Crawling  Data Transformation  Sentiment Analysis  Social Network Analysis (SNA)  Time-series Analysis  Data Visualization
  32. 32. Case Study: Social Trends Analysis Learning-based Sentiment analysis: • Collect a training set of positive and negative examples • Perform data cleaning and normalization on unstructured textual data • Build a model that generalizes to different domains Social Network Analysis: • Discover hidden social communities • Perform bot-detection • Discover social information flow Time-series analysis: • Calculate basic time-series statistics • Discover hidden trends and fluctuations in time-series • Compare time-series sequences
  33. 33. Case Study: Recommender Systems & SmartTraveler Business Area: Helps users find content they might like by making automatic personalized recommendations Application Domains:  E-commerce  News  Entertainment  Social Networks  Tourism and visitor guides
  34. 34. Case Study: Recommender Systems & SmartTraveler
  35. 35. Case Study: Log Analytics and Anomaly Detection Business case: • Discover hidden patterns and relationships in Netflow logs in order to identify unusual activity in corporate network infrastructure Problem Statement: Identify the items, events or observations which do not conform to an expected pattern or behavior
  36. 36. Case Study: Log Analytics and Anomaly Detection Timestamp Number of packets Volume of packets (in bytes) Source IP Destination IP Source port Destination port Protocol Netflow Data:
  37. 37. Case Study: Log Analytics and Anomaly Detection Time-Series SegmentationDynamic Thresholds
  38. 38. Check out our Data Science and Big Analytics web pages For more details on our Advanced Analytics service line
  39. 39. USA HQ Toll Free: 866-687-3588 Tel: +1-512-516-8880 Ukraine HQ Tel: +380-32-240-9090 Bulgaria Tel: +359-2-902-3760 Germany Tel: +49-69-2602-5857 Netherlands Tel: +31-20-262-33-23 Poland Tel: +48-71-382-2800 UK Tel: +44-207-544-8414 EMAIL info@softserveinc.com WEBSITE: www.softserveinc.com Thank You!

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