bigdataanalytics.mit.eduData Science and Big Data Analytics: Making Data-Driven Decisions | MIT xPRO

Get updates & access a FREE case study from this course Get updates & access a FREE case study from this course Take a sneak peek at the case study used in this course and learn to build your own recommendation engine. Complete the form below: By submitting your information, you are agreeing to receive periodic information about online programs from MIT related to the content of this course. Sorry, your browser doesn't support embedded videos. Data Science and Big Data Analytics: Making Data-Driven Decisions Turn big data into even bigger results with a seven-week online course from MIT. Get updates & access a FREE case study from this course By submitting your information, you are agreeing to receive periodic information about online programs from MIT related to the content of this course. Make better data-driven business decisions Could you be using data more effectively? 90% of the world’s data has been created in just the past few years. Faced with overwhelming amounts of data, organizations are struggling to extract the powerful insights they need to make smarter business decisions. To help uncover the true value of your data, MIT Institute for Data, Systems, and Society (IDSS) created the online course Data Science and Big Data Analytics: Making Data-Driven Decisions for data scientist professionals looking to harness data in new and innovative ways. Over the course of seven weeks, you will take your data analytics skills to the next level as you learn the theory and practice behind recommendation engines, regressions, network and graphical modeling, anomaly detection, hypothesis testing, machine learning, and big data analytics. At the end of this course you will receive a digital Professional Certificate and 1.8 Continuing Education Units (CEUs) from MIT. MIT Faculty explain the impact of big data on business decision making. After this course, you will be able to: Apply data science techniques to your organization's data management challenges and business decision making Determine the difference between graphical models and network models. Convert datasets to models through predictive analytics. Deploy machine learning algorithms to improve business decision making. Master best practices for experiment design and hypothesis testing. Identify and avoid common pitfalls in big data analytics. Don't just discover new strategies, tools, and insights - put them to the test! With a selection of 20 case studies and hands-on projects, this course helps learners apply their newfound knowledge to realistic business challenges. Although Python is the most frequently used language, R can be used to complete many of the case studies. Both of required case studies in this course can be completed with either Python or R. View the week-by-week course schedule. THE MIT XPRO LEARNING EXPERIENCE We bring together an innovative pedagogy paired with world-class faculty. Learn by doing Practice processes and methods through simulations, assessments, case studies, and tools. Learn from others Connect with an international community of professionals while working on projects based on real-world examples. Learn on Demand Access all of the content online and watch videos on the go. Reflect and Apply Bring your new skills to your organization, through examples from technical work environments and ample prompts for reflection. Demonstrate your Success Earn a Professional Certificate and 1.8 Continuing Education Units (CEUs) from MIT. Learn from the best Gain insights from leading MIT faculty and industry experts. START DATE Feburary 3, 2020 Duration 7 weeks PRICE $899 Enroll Now What learners and companies are saying More than 7,000 professionals have completed this course. Murali Thyagarajan - DBA & Application Support, Nasdaq "The course was easy to understand and had depth. All the concepts were clearly laid out and explained. This is the best course I have come across on this topic." Dr. Jasmine Latham - Lead Data Scientist at ONS Data Science Campus "I am very pleased with the course content, it is exactly the level I am looking for. Each professor/course presenter has packed a lot of information and has explained complex algorithms in good detail. Some with [a] good sense of humor." Adnan Raza - Business Consulting, Mackenzie Investments "As a novice student in the field of AI and Machine learning, the module based approach really helped me structure the step-wise approach. In addition the real world examples helped associate concepts with applications. I am now more aware and equipped compared to Day 1." Sunil Upadhyay - Tech Lead, IBM "I had very basic information about data sciences and machine learning when I started this course. This course has definitely helped in getting an overview of different aspects and algorithms for data analysis and processing and to gather insights out of it." MIT FACULTY TEACHING THIS COURSE Devavrat Shah Professor, Department of Electrical Engineering & Computer Science; Director, Statistics and Data Sience Center Philippe Rigollet Associate Professor, Mathematics Department at MIT Victor Chernozhukov Professor, Department of Economics and the Statistics and Data Science Center at MIT Stefanie Jegelka Associate Professor, Department of Electrical Engineering and Computer Science and member of Computer Science and AI Lab and IDSS Ankur Moitra Associate Professor, Department of Mathematics and member of the Computer Science and AI Lab at MIT Tamara Broderick Associate Professor, Department of Electrical Engineering and Computer Science and a member of the Computer Science and AI Lab at MIT David Gamarnik Nanyan Technological University Professor, Sloan School of Management Jonathan Kelner Associate Professor, Department of Mathematics and a member of the Computer Science and AI Lab at MIT Kalyan Veeramachaneni Principal Research Scientist at the Laboratory for Information and Decision Systems at MIT Caroline Uhler Associate Professor, Department of Electrical Engineering and Computer Science at MIT and IDSS Guy Bresler Associate Professor, Department of Electrical Engineering & Computer Science, Laboratory for Information and Decision Systems and IDSS Who Should Enroll Professionals at any career stage, looking to turn large volumes of data into actionable insights. Past learners' job roles have included: business intelligence analysts, management consultants, technical managers, business managers, data science mangers. Data science enthusiasts and IT professionals. Background knowledge of statistical techniques and data calculations or quantitative methods of data research is strongly recommended. Familiarity with either R or Python is recommended but not required. JUSTIFY YOUR PROFESSIONAL DEVELOPMENT Many companies offer professional development benefits to their employees but sometimes starting the conversation is the hardest part of the process. Use these talking points, stats, and email template to advocate for your professional development through MIT xPRO's online course, Data Science and Big Data Analytics. VIEW YOUR GUIDE Ready for a sneak peek? Sample the case study. Ever wonder how companies like Netflix, Spotify and Pandora filter products based on their unique user’s preference? In this case study, you will learn how Netflix utilizes Recommendation Engines to provide the best possible shows and movies for unique users. To access this case study, submit your information in the form above. Propel Your Career On Your Terms Technology is accelerating at an unprecedented pace causing disruption across all levels of business. Tomorrow’s leaders must demonstrate technical expertise as well as leadership acumen in order to maintain a technical edge over the competition while driving innovation in an ever-changing environment. MIT uniquely understands this challenge and how to solve it with decades of experience developing technical professionals. MIT xPRO’s online learning programs leverage vetted con...