In this two-hour session, renowned Data Scientist, Dr. Murthy Kolluru, will demystify Data Science and provide business leaders with a deeper understanding of the subject. The emphasis will be on the intuition, not the math, behind cutting edge algorithms and concepts like Machine Learning. We will also discuss the ROI of data driven decision making and highlight a few business problems that Data Science enabled us to solve.
Dr. Murthy holds a Ph.D. in Materials Science from Carnegie Mellon and started his career as a Rocket Scientist. He has architected and solved extremely complex problems in both business and engineering and is considered one of the top Data Science academicians in the world.
Gaurav is a dynamic entrepreneur and co-founder of Soothsayer. He holds Masters Degrees in Electronics and Technology and has spent over two decades in technology. Prior to Soothsayer, he founded two successful companies and oversaw the delivery of enterprise solutions to Fortune500 companies in the US, UK, Germany, and Asia-Pacific. Gaurav oversees human resources, operations and project delivery.
Chris is a co-founder of Soothsayer. He studied Political Science at The Ohio State University, but earned his stripes in business. He brought the Soothsayer team together and shepherds it’s strategic vision. Chris oversees marketing, partnerships, sales, and strategy.
Dr. Gnana combines analytics and synthetics to diagnose organizational problems and design appropriate analytic approaches. Gnana has holds a PhD from the University of Pennsylvania, where he still does research. He has 15+ years of commercial and research based analytics consulting experience. His primary work centers on complex system problems in business and society. This involves systems (design) thinking, analytics (modeling, simulation, and analysis), emphasizing human behavior, representing rich interconnections between parts, and employing both quantitative and qualitative knowledge elicitation techniques. He publishes regularly in reputed journals.
Akshay was the first Data Scientist at Soothsayer, and he is actively involved in problem assessment, solution design, and project execution. He holds a Bachelor’s in Control Systems and a Masters in Computer Science from The Ohio State University. Prior to joining Soothsayer, he was a Senior Consultant at IBM, where he successfully delivered on multiple advanced analytics projects. He is an expert in Machine Learning, especially in relation to Speech Recognition and the Rapid Development of Pronunciation Models for low resource languages. He enjoys tackling complex business problems and building world-class solutions for clients.
Gordon has over 15 years of experience in that No Man’s Land between IT and Business. He has been extracting intelligence out of swaths of data before Big Data was even coined. He has extensive experience in government, finance, healthcare, and marketing. This cross-industry experience helps him guide clients along the path of successfully harnessing Advanced Analytics.
Jarred holds a Bachelor’s degree from the University of Arizona in Applied Mathematics and Physics, as well as a Master’s in Physics from The Ohio State University. Prior to joining Soothsayer, he worked in multiple fields ranging from condensed matter physics to quantitative psychology in physics education. This included developing models for electronic transport across molecular junctions to assessing the conceptual knowledge state of students in various levels of physics. He has a strong background in analytical thinking and coding, as well as the ability to apply these techniques across a wide spectrum of problems.
Connal Brown has 20+ years of experience providing telecom service and support to Government and Commercial customers. He has a deep background in business process analysis as well as a successful track record of managing large and complex government transition projects. Connal will assume responsibility for new business development and strategic alliances in the DC/MD/VA area.
Andrea is actively involved in relationship building, strategy, and corporate training. She studied Game Theory, Intelligence Analysis, and Political Science at The Ohio State University. She leverages this background to develop a deep understanding of client problems and offer actionable solutions.
Prashanth is involved in Data Science consulting and business development for Soothsayer’s education initiative. He holds a Bachelor’s in Metallurgical Engineering and a Master’s in Business Analytics from Drexel University. Prior to joining Soothsayer, he worked as a Data Scientist on an analytics product development team. He also worked for the International School of Engineering. He has a strong background in Data Mining, Optimization, and Machine Learning.
Dr. Murthy is a former Rocket Scientist and the President of the International School of Engineering, which was recently named the 3rd best Data Science program in the world (www.CIO.com).
Dr. Boorn has spent the bulk of his career on Urban Planning, Sustainability, and issues that arise from an urbanized environment. We work together to solve problems related to the vast and disconnected dimensions of urban places.
Dr. Li is trained in science and educated in management. He has multiple degrees (including an MBA) and certifications. He is the former Head of Predictive Analytics at Alliance Data, and he advises us on analytics projects related to retail.
David was most recently the VP of Analytics at Battelle, after years with Bell Labs applying research in data management and analysis. He advises us on projects related to Telecommunications.
Ryan is an industry thought leader and frequent speaker at major conferences. He is currently an EVP at one of the largest healthcare marketing agencies in the world, and he advises us on projects related to Marketing Analytics.
Paul is a Professor at the University of Cincinnati, a Statistician, a veteran Sports Journalist, and the founder of PredictionMachine. We are working together to develop a best in class analytical solution for the NFL.
Zain was an analytical leader at JP Morgan Chase and Wells Fargo. He is now the Founding Executive Director of Bellarmine University’s MS in Data Science. He advices us on analytical innovation in Banking.
Matthew is an experienced engineer and human resources executive. He advises us on opportunities for leveraging data analysis to drive practical and profitable human capital decisions.
Data Science Demystified for Decision makers - Nov 2014. Series of Seminars were conducted in Columbus, OH & Detroit, MI.
We are not limited by canned tools purporting to do “Analytics” but really just querying your data. We are experienced working with data of all shapes, sizes, and complexity. Every solution is architected by a PhD, and we leverage our team of Mentors on problems that require specific domain knowledge.
For companies looking to develop a new product or to improve upon an existing one, we can help. We are currently exploring multiple product development partnerships and plan on developing state-of-the-art Data Science solutions and Intellectual Property for a variety of domains.
Our corporate training programs are led by world-class PhD’s from schools like Carnegie Mellon, Johns Hopkins, Stanford, and Wharton. Each program is custom and has the option of producing a deliverable at the end. We can deliver on a project while training your team to solve problems in the future. We offer both executive and hands-on training programs.
Traditional algorithms fail to reveal well concealed patterns in complex, high dimensional, and unstructured data. For this kind of data, methods like decision trees tend to extract patterns that are obvious, meaning patterns with 70% or more support. This results in very few eye openers.
We utilize advanced Machine Learning techniques to search through data and extract Hidden Insights and Micro-patterns that most off-the-shelf methods miss. Instead of just drawing lines down the middle and computing averages, we identify all data points and their surrounding area, and extract actionable and intuition crushing insights.
When needed, we can extract actionable If-Then patterns from otherwise seemingly black-box results. This means you can achieve the accuracy of the complex models, without sacrificing the explicability of traditional methods.
It is becoming increasingly important for businesses to tap into the potential of unstructured data. We are experienced with working on complex problems involving images, speech, and text.
We customize solutions that use both syntactic and semantic analysis, and our delivery team has successfully executed several projects involving Natural Language Processing, Sentiment Mining, and Text Classification.
We actively work with the latest and most sophisticated Machine Learning techniques to deliver these solutions. Our aim is to solve the unstructured data problems of the present and to continue exploring solutions for the unstructured data of the future.
6 Months Our program schedule encompasses knowledge-filled classroom and hands-on sessions, which include 8 hours of lectures and 8 hours of guided lab sessions (per week). The program is intensive, so students will need to dedicate 30-35 hours per week to work on assignments. In addition, students will take 32 tests during the program to satisfy the credits required for successful graduation.
|Foundations of Probability and Statistics for Data Science||Descriptive statistics: central measures, measures of spread, exploring data using simple charts, identifying outliers | Probability distributions, Bayes theorem, central limit theorem | Sampling distributions: student-t, F, z, Chi-square | Hypothesis testing|
|Essential Engineering Skills in Big Data Analytics Using R and Python||Introduction to R and Python | Data pre-processing: type conversions, data transformations | Advanced utilities in R and Python|
|Statistics and Probability in Decision Modeling||Covariance – Correlation | Linear Regression: simple, multiple, StepAIC, multicollinearity check, diagnostics and validations| Logistic Regression | Time Series Analysis | Naïve Bayes classifier| Feature selection: regularization methods | Feature reduction: PCA|
|The Art and Science of Storytelling with Data Visualizations||Communicating with data | Aesthetics of chart | Create a Story for every chart | Tools: R Deducer and Tableau|
|Methods and Algorithms in Machine Learning||Association Rules: Prism rules, Apriori and FP tree algorithms | Decision Trees: C50, Rpart, Boosting | SVM: background maths, various kernels, kernel tricks | kNN & Collaborative Filtering |Ensemble Methods: Random forest, Gradient Boosting Machines, Adaboost | Clustering methods: distance metrics, hierarchical, K-means|
|Foundations of Text Mining and Search||Vector Space Models | Text data pre-processing and TF-IDF | Matrix factorization: SVD | Search engines and Page Ranking algorithms|
|AI and Decision Sciences||Artificial Neural Networks | Deep learning: auto encoders, deep architectures | Convolution Neural Networks and Recurrent Neural Networks: LSTM | Sentiment analysis, Text classification | Linear Programming | Evolutionary search methods: Genetic algorithm, Monte Carlo Simulation | Planning, Thinking and Architect a Data Science Solution|
|Engineering Big Data with Hadoop and Spark Ecosystem||Data center as a computer | Hadoop VM, GFS, HDFS and Capacity planning | Data ingestion: Chukwa, Flume, Avro | NoSQL: Big Table, HBase, Document stores, Graph stores, Key-Value stores | Processing frameworks on clusters: Map reduce, Yarn, MR2, R-Hadoop, Hadoop Streaming with Python, BSP, Spark | Spark processing | MR versus BSP | SQL on Hadoop | PIG programming, Oozie, Zookeeper and Mahout|
|Building End-to-End Data Science Applications||Provide business case scenarios | Architecting the solution approach and articulating | choosing the right tool set, situational based analysis, identify and feasibility testing of relevant programming techniques | solve the business case end-to-end applying all the Machine learning tools and techniques|
|Communication, Ethical and IP Challenges for Analytics Professionals (Video access)||Importance of communication | Seeing the big picture and communicating effectively | Framework for effective presentations | legal and ethical issues and challenges |The Ethics Check questions|
Undergraduate degree (or above) in a quantitative field of study. Ideal applicants should be strong in math/stats and have had exposure to some form of programming.
6-Month Advanced Certificate Program in Big Data Analytics & Optimization
Application Fee: $50
|$3500 – $8500||Minimum scholarship of $3,500 is guaranteed for all students accepted into the program + up to $5,000 in added scholarships will be provided based on course performance.|
|$3000||Top students will be offered career opportunities through Soothsayer or an industry partner/client. If this is accepted by the student, an additional $3,000 bonus will be awarded following 3 months on the job.|
|$250/ Referral||Refer a candidate for this program, and if they enroll, you will be paid $250. Referral fees will be paid after each referred candidate registers for the program and pays their 1st installment. No cap on referrals.|
Fee structure: $11,490 ($14,990 – $3500)
*Lunch and dinner will consist of Vegetarian and Non-Vegetarian Indian food.
Soothsayer Analytics, a U.S. corporation with offices in Ohio and Michigan, in collaboration with INSOFE, is offering full-fledged career support for qualified students in the U.S. This partnership will provide students with placement guidance upon graduation from our joint 6-month Data Science program.
Soothsayer engages with a wide-array of Fortune1000 companies and innovative start-ups and actively work in the areas of Pattern Recognition, Predictive & Prescriptive Analytics, Optimization, Natural Language Processing, and Computer Vision.
In addition to building data-driven business solutions and IP, Soothsayer also offers full-fledged career support designed exclusively for graduates of INSOFE’s program. This includes placement guidance when you return to America as well as the potential to be hired for various internal or external Data Science projects and R&D initiatives. As the program commences, Soothsayer will simultaneously seek to identify pertinent opportunities throughout the U.S.
Our program will also provide career services through customized training programs designed specifically for cultivating in-demand skillsets. At each phase of the program, students will undergo technical and professional hands-on training to match them to real-world industry needs.
Four months into the program, American students will have the opportunity to intern with Soothsayer and work on real-world problems for some of the world’s finest organizations (for a period of 2 months). Based on performance and fit, top graduates will also be given the opportunity to interview with Soothsayer, or their partners/clients for internal or external opportunities.