![]() While enrolled in the Certificate in Data Analytics, Big Data, and Predictive Analytics (or Certificate in Practical Data Science and Machine Learning), you will receive a variety of supports, including: Upon completing the certificate, you will be prepared to take the INFORMS Certified Analytics Professional (CAP®) exam to become a certified professional in this new and growing field. It will guide you in using data analytics, big data, and predictive analytics to optimize performance in fields like data warehousing, data management, IT, and more. With hands-on learning, this certificate will provide you with a strong foundation in analytics, tools, and statistics. Complete five certificate courses in three months with this fast track followed by the additional capstone course in the term of your choosing to complete the certificate. ![]() However, the instructor holds no responsibility in case you do not satisfy the prerequisite and need to drop the course.Note: CKME 995 - Data Analytics (Fast Track Option) is available for Spring/Summer 2023. If there is no waiting list, it is fine to provide the certificate or show your previous project before the course begins (January 13, 2022). If you use options b) or c): if there is a waiting list for the course, the certificate or the project must be shown before the beginning of the term to hold a place among the regular attendees. Projects from someone else (web, friend, previous students) are not considered. Please bring the syllabus of the course together with the certificate.Ĭ) Show and discuss a project you developed in Python. ![]() I recommend the course on Code Academy, however other courses are also fine. Since we need to pick one programming language for the course, we require students to prove proficiency with Python before the course starts, in one of the following ways:Ī) Have passed the course DNDS 6288 Scientific Python.ī) Take a MOOC course on programming with Python and show the certificate. As such, we use a programming language, Python, to solve real world learning problems and extract knowledge from real datasets. This course has a focus on data mining and big data analytics. ![]() Basic programming skills and basic skills in statistics and linear algebra are required. You need to be proficient with Python to take this course – read the “Prerequisites” section below. However, during the class all examples and sample code will be provided in Python and Jupyter notebooks, thus the use of Python is strongly encouraged. Students are free to work in any computer language/network software they feel most comfortable. ![]() The course will have a hands-on approach, with homeworks, practical classes and with the development of a project. We will also demonstrate the applications of these tools on real datasets, to show how they can help us to analyse the digital traces of human activities at societal scale, to understand and forecast many complex socio-economic phenomena. We will cover the key data mining methods of clustering, classification and pattern mining are illustrated, together with practical tools for their execution. In this course we will introduce methods of data aqusition and concepts of data mining, machine learning and big data analytics. Their common purpose is to uncover hidden patterns, unknown correlations and other useful information useful to make better decisions. Data mining and big data analytics is a core subject in data science with the aim to develop methods to examine sizable and multivariate datasets. ![]()
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