1 Certificate Program in Applied Big Data Analytics in Dubai A Collaborative Program offered by INSOFE and Synergy-BI
2 Program Overview Today s manager needs to be extremely data savvy. They need to work with data scientists and business users who do not understand each other s language and be an effective interface between them. The demand for managers who can speak data science terms, lead data science teams and architect data driven solutions is at unprecedented high and continues to remain so in the near future. This 5 day program is concise, power-packed, practical exposure of all the skills that a manager would need to become a super-efficient data manager and consultant. The participants would gain the following 1. Complete comfort with analytics jargon and solution approaches to a variety of data problems from a variety of verticals 2. Hands-on exposure to various data science tools 3. Conceptual understanding of machine learning and big data engineering Who is it for? Managers from companies that want to encourage data driven decision making, entrepreneurs, and individuals with an aspiration to shift from their current work to data science at a leadership role.
3 Module 1: Analytics 360 o This module introduces the jargon of big data analytics, the teams, business models and the eco system. Lecture Topics: - 3 Hours Typical Problems solved by a business user and the challenges faced What are the prevailing big data analytics business models and the team constitution? A big picture view of analytics ecosystem Use case 1: How can a manufacturing company improve its operational efficiency and quality by studying the correlations between inputs and outputs? Understanding of jargon Ability to confidently identify and express big data problems to technical teams
4 Module 2: Visualizing Data Techniques of data exploration will be introduced to the participants so that they would be able to bring out various relationships among the attributes, and explore drill-down charts to view and gain deeper data insights. Lecture Topics: - 3 Hours Art and science of Data Visualization Introduction to current tools of visualization Learn to build powerful visualizations systematically Quiz 1: A real world scenario will be given along with the data and the participant need to analyze the problem thoroughly and create a visualization concept (even better the visualization itself). The problem will be taken from credit risk in a financial services industry. How can demographic, transactional views give a clearer understanding of customers?
5 Module 3: Architecting Data Science Solutions This module introduces the participants to a systematic approach to data science problem solving. What questions to ask, how to set up the problem, how to work with the teams and manage the progress Lecture Topics: - 6 Hours Typical phases of a data science problem: Discovery, Exploration, Feature engineering and data estimation, pre-processing, modeling and visualizations For each phase, we will touch upon the objectives to be set, teams to be constituted, questions to be asked/answered and forms of deliverables Use case from credit risk analysis will be covered in this problem The manager will be able to develop a blue print, plan of execution and monitor it accurately throughout the project life cycle Quiz: The participants will develop a blue print for a problem from pharma retail problems which has wide ranging implications in insurance and healthcare industries? How to predict customers who are not treating themselves and are falling sick and costing more to the insurance firm?
6 Module 4: Statistics and Machine learning on steroids This module teaches how to think like a data scientist. Various important statistical and machine learning methods will be introduced at a big picture level. Simple graphs will be used to explain complex mathematics. Lecture topics: - 6 Hours Models mostly being used: Statistical and probability analysis, regression, decision trees and K-NN Black box models: Random Forests; Neural Networks, Support Vector Machines Ensembles, deep learning and spectral methods: Ways to boost base models Important business problems: Recommendation engine; Fraud detection; Simulators, Forecasting and Classification. The participant will have developed a deep intuitive understanding of a variety of models and their limitations. They will be able to advise the teams on the solution approaches to be taken. Long quiz: The participants will develop a technical solution architecture to a healthcare fraud in a pharma industry or customer life time value for an online retailer. This involves applying everything they learned thus far in the course. The participants will work as a team and solve one problem.
7 Module 5: Big, unstructured data and Internet of things Unstructured data, big data possess a different sort of problems not discussed thus far. The participants will analyze the importance of analyzing text, graphs, click streams, logs, images, videos and audio. problems. They understand the technologies that are developed to address these Lecture topics: - 6 Hours Approaches to solving text data; what is common and what is different in images, audio and video Graphs, Logs, click streams: What are they and how you can do cool things with them Big data architectures for real fast analysis Use case: How does data science help one build a smarter city (how to design public transport, how to plan waste disposal, what should the Government see, how to detect crime in advance) The participant will be able to understand what additional data can be added to a problem to make it more solvable. They understand broad approaches they need to take to tackle unstructured data. Quiz: The participant will be provided a problem that has both structured and unstructured components to it. They will need to design and develop a solution blueprint for the same
8 Module 6: Practicing Analytics Problems will be identified from various industry verticals and corporate divisions with an emphasis to analyze how there is an underlying thread and how to connect problems from different domains to the same solution. The user will also learn what expectations should be set to the client and how to arrive at costing of a project. Lecture Hours: - 6 Hours Typical data science problems in the industry across verticals Industry verticals: Ecommerce, Health, Pharma, Manufacturing, Retail, Supply chain, Banking and Financial Services, Telecom and Insurance Corporate divisions: HR, Operation, Finance and Marketing Identifying the common thread and learning from other disciplines: Utilize design thinking strategies and methods within a holistic, multidisciplinary and collaborative perspective Arriving at meaningful, high ROI solutions Grand Quiz: The participants need to take one vertical and corporate division and present a compelling business case to solve a problem and put together the solution methodology along with estimated costs.
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