Basic Statistics By Bl Agarwal Pdf Free Download
Rivka Licklider
Note: This course has been approved as a regular course in the curriculum and given a regular course number, 542. For a more recent version of the course, please visit the CS 542 page.
Prerequisites
Linear algebra, probability & statistics, and basic calculus. Experience with machine learning (e.g., CS 446), and preferably reinforcement learning. It is also recommended that the students are familiar with stochastic processes and numerical analysis.
Coursework & Grading
Homework may be assigned on an ad hoc basis to help students digest particular material. The main assignment will be a course project that involves literature review, reproduction of theoretical analyses in existing work, and original research (see details below). No exams.
Something between 1 & 2 I would encourage most of you to start in this category. The idea is to reproduce the proofs of existing results and see if you can extend the analysis to a more challenging and/or interesting setting. This way, even if you do not get the new results before the end of semester, your project will just fall back to category 1.
See the link at the top of this page for potential topics. You are expected to submit a short project proposal in the middle of the semester. The proposal should consist of a short paragraph describing your project topic, the papers you plan to work on, and the original research question (if applicable).
If you do not have experience in statistics or coding, the foundational coursework gives you the framework needed to advance to more complex data modeling concepts. In addition, Merrimack offers free boot camps in R, Python, SQL, Tableau, and a review of statistics.
Data capture-related rights and responsibilities, data governance design and management, data security and privacy, information quality, and the ethical aspects of data access, usage, and sharing. operational and experiential aspects of data governance and differential privacy. Credits: 4
This course will provide students with a comprehensive understanding of the big data processing foundation and techniques. Students will understand basic concepts of parallel computing, big data, Hadoop, MapReduce, and Spark. Students will develop skills to solve big data processing problems. Credits: 4
In this capstone experience, students take a problem through the full data science lifecycle using data provided by the instructor or a data set from an employer or internship. Instructor provides data and requirements. Students must complete six courses from the MSDS program before taking this capstone course. Credits: 4
Randhir (Randy) Agarwal currently leads the data engineering and data science teams at Samsung Electronics. He has over 25 years in the wireless telecommunications industry, including deploying Green-field wireless networks worldwide and fine-tuning them for optimal performance. Mr. Agarwal holds an M.S. in Data Science from Northwestern with a specialization in analytics and modeling, and a Bachelor of Engineering in Telecommunications from the University of Mumbai.
Torrey is our success coach for the Data Science & Analytics program and is very important to the overall student experience. Throughout your time working towards your degree, Torrey will help address many of the questions you may have outside the classroom. In doing so, Torrey will help free more of your time to concentrate on mastering the content that is so important to your program progression.
Torrey earned her B.A. in Mathematics from the College of the Holy Cross and an M.Ed. in Higher Education from Merrimack College. You will get to engage with Torrey from the time you are admitted until you graduate, allowing you to have a consistent point of contact as you work to achieve your goals.
Michael Dupin, Ph.D., is the Head (and founder) of Data Science at C Space, a market research company where he leads the efforts on artificial intelligence. With more than twenty years of experience in data and statistical analytics, he is a self-confessed geek, data scientist, statistician, researcher, modeler, author, and sailor.
Prior to C Space, Mike held various roles within banking, where he led efforts such as macroeconomic stress testing, risk management, financial modeling, and statistical model validation. Before the corporate world, he was a research fellow at Harvard University modeling blood flow in tumors. He holds degrees in nuclear physics, instrumentation, and a Ph.D. in Computational Fluid Dynamics.
Katherine Geist holds a Ph.D. in Biology with an emphasis in computational evolutionary genomics. Her research ranges from gene expression in social insects to migraine symptom tracking in multiple sclerosis patients, with big data at the core of her research.
Chris received a Ph.D. in Industrial Engineering from Georgia Tech and a B.S. in Mathematics from William and Mary. Chris teaches Data Exploration for the Data Science & Analytics program. Chris teaches Visual Data Exploration for the Data Science program.
Jeremiah is a data scientist with nine years of experience in analytics. He is currently an adjunct faculty member in the Data Science & Analytics program. His interests include computer vision, natural language processing, time series analysis, and distributed computing. Jeremiah is fluent in R, Python, VBA, and SQL, and is interested in learning new programming languages.
In his leisure, he spends time with his wife Brooke and son Magnus. Jeremiah holds a B.S. in Financial Analysis from Ball State University, an M.S. in Analytics from Dakota State University, and is pursuing an M.S. in Information Systems and Ph.D. from Dakota State University. Jeremiah teaches R and Python programming in the data science program.
Dr. Peter Salemi has over 10 years of experience in quantitative research, machine learning, and statistics in both academic and industry settings. As a data scientist at The MITRE Corporation, he works with several federal agencies ranging from the United States Department of Veterans Affairs to the Centers for Medicare & Medicaid Services. Dr. Salemi received his Ph.D. in Operations Research from Northwestern University, an M.S. in Operations Research from the University of California, Berkeley, and a B.S. in Mathematics and B.B.A. in Finance from the University of Massachusetts, Amherst.
Kathryn Wifvat, originally from Minnesota, earned her B.S. in Mathematical Statistics and a B.A. in Applied Mathematics before completing a Ph.D. in Applied Mathematics at Arizona State University. With experience in data analytics and full-stack web and mobile app development, she is now the founder and CEO of an ed tech startup, in addition to being an adjunct faculty member.
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