Page 1 of 10 11 August 2020 Annex A COURSE CONTENT Academic Year AY2019/20 Semester 1 Author(s) Associate Prof Wang Zhiwei ([email protected]) Course Code CV0003 Course Title Introduction to Data Science and Artificial Intelligence Pre-requisites CV1014 Introduction to Computation Thinking Pre-requisite for Nil No of AUs 3 Contact Hours LECTURES 0 LAMS/TEL (Online Videos and … Examples of Big Data and Data Science in Practice (Healthcare, Logistics & Transportation, Manufacturing, and Real Estate & Property Management industries). This course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Course Aims. Data science includes analyzing data collected from the web, smartphones, customers, sensors, and other sources. Putting the ‘G’ in ‘AI’: An Overview of Terms used in (Narrow/Applied) AI- and what they mean to each other”, Suraj Jena, June 10, 2018 “Artificial Intelligence and Machine Learning in the Media Sector”, Fraunhoker Fokus Google’s Search Engine One of the most popular AI Applications is the google search engine. Our active work toward reconciliation takes place across our campuses through research, learning, teaching, and community building, and is centralized within our Indigenous Initiatives Office. While many consider contemporary Data Science as Artificial Intelligence, it is simply not so. Admittance will be decided by the Graduate Director on a case-by case basis. Here, one of the booming technologies of computer science is Artificial Intelligence which is ready to create a new revolution in the world by making intelligent machines.The Artificial Intelligence is now all around us. Data Analytics: Introduction and Guide to Data Science, Analysis, Artificial Intelligence and Machine Learning (Audible Audio Edition): Markus Schellander, Chris Johnson, Nicolas Ezequiel Sosa: Amazon.ca: Audible Audiobooks Variables and Data Types 4. Courses not on this list are subject to the approval of the Graduate Director. Many problems in AI can be solved theoretically by intelligently searching through many possible solutions: Reasoning can be reduced to performing a search. While Data Science may contribute to some aspects of AI, it does not reflect all of it. If you open up your chrome browser and start typing something, Google immediately provides … Introduction . Data Science, Artificial Intelligence, Machine Learning, Deep Learning are the most prominent words which are still sounding weird for many.As we know that the entire world is chasing this technology to grab into its hand. Note direct entry into the Master of Data Science and Artificial Intelligence (MDSAI) program is only available through the part-time option. Errors and Exceptions 2 EE0005 Introduction to Data Science and Artificial Intelligence 1 - INTRODUCTION Welcome to Math for Machine Learning: Open Doors to Data Science and Artificial Intelligence! Nine terms (36 months) for part-time students. In the introduction, the terms “data science” and its taxonomy are defined. Artificial intelligence — A computer system able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. Students must complete a 3-day workshop on “Ethics in Data Science and Artificial Intelligence” that will be offered once a year. 30 November 2018. Wael is also a Co-Authour of the book "The Future of IoT". Qualified Management Accountant (CIMA). But we introduce these techniques from data science alongside the cognitive science that makes them possible. Data Science and Artificial Intelligence. This document presents a review of existing literature on the future of work. This review has been commissioned by The Alan Turing Institute to inform the Turing research strategy aiming to further data science and artificial intelligence (AI) research to address real-world problems. There is also sufficient information on Data Science, required skill sets for implement the data science and also brief of machine learning algorithms. Data science use statistical learning whereas artificial intelligence is of machine learning’s. Faculty of Engineering minimum requirements, general information and regulations section of the Graduate Studies Academic Calendar, Graduate Academic Integrity Module (Graduate AIM), Combinatorics and Optimization (CO) courses, Graduate Studies and Postdoctoral Affairs (GSPA). Setting Up Python Development Environment 3. This first course introduces the core techniques of natural language processing (NLP). Students are responsible for reviewing the general information and regulations section of the Graduate Studies Academic Calendar. Note that part-time students starting in Winter or Spring will need to consider course sequencing options since some courses are not offered every term. This is a first textbook in math for machine learning. Please note that this is NOT TECHNICAL TRAINING and it does NOT teach Coding/Development or Statistics. Students must take enough additional elective courses to fulfill the 9-course requirement. Students are required to take the following core courses: CS 651 Data-Intensive Distributed Computing (designed for CS major background students), or, CS 631 Data-Intensive Distributed Analytics (designed for non-CS major background students), STAT 841 Statistical Learning - Classification, STAT 844 Statistical Learning - Function Estimation, CS 638 Principles of Data Management and Use, CS 685 Machine Learning: Statistical and Computational Foundations, CO 602 / CS 795 Fundamentals of Optimization, CO 673 / CS 794 Optimization for Data Science. 1. The program information below is valid for the winter 2021 term (January 1, 2021 - April 30, 2021). NoSQL, Hadoop), Big Data Technology Architecture (including examples of popular technologies), Introduction to data analysis, Artificial Intelligence and Machine Learning, Big Data Analytics and Artificial Intelligence Definitions, Machine Learning Workflow and Training a Model, Simplified Overview of Machine Learning Algorithms, Classical Machine Learning: Supervised and Unsupervised Learning, Classification: Support Vector Machines (SVM), Classical Machine Learning: Unsupervised Learning, UNSUPERVISED LEARNING: Dimensionality Reduction, CLASSICAL MACHINE LEARNING - Section Wrap Up, Introduction to Deep Learning and Neural Networks, NEURAL NETWORKS: Convolutional Neural Network, NEURAL NETWORKS: Recurrent Neural Network, NEURAL NETWORKS: Generative Adversarial Network (GAN), AWS Certified Solutions Architect - Associate, Anyone who is interested in big data, machine learning and artificial intelligence, People with technical background who want to gain insights in real life applications of data science skills, Anyone who works with coders, data engineers and data scientists and wants to learn basics about big data technology and tools, People without maths or computer science background, but who want to understand how Machine Learning algorithms work. At the end, to me it is very good course. An enthusiastic management consultant, project, programme and change manager, media producer/director with 20 years of experience in financial services industry (operations and consulting) and 5 years of experience in media/video production industry  (educational content and corporate communications). File Input and Output 6. We have got fantastic guest speakers who are the experts in their areas: - WAEL ELRIFAI - Global VP of Solution Engineering - Big Data, IoT & AI at Hitachi Vantara with over 15 years of experience in the field of machine learning and IoT. This chapter covers the relationship between artificial intelligence, machine learning, and data science, provides the motivation for data science, an introduction to key algorithms, and presents a roadmap for rest of the book. Note (*): CO 769, CS 798, CS courses at the 800 level, and STAT courses at the 900 level should be on a topic in Data Science or Artificial Intelligence; they are subject to the approval of the Graduate Officer. I have experience of working in both large corporate and start-up environments. 2. Ideal student: If you're a working professional needing a refresher on machine learning or a complete beginner who Students whose average falls below the program’s minimum requirements may be required to withdraw from the program. - YULIA PAK - Real Estate and Portfolio Strategy Consultant with over 12 years of experience in Commercial Real Estate advisory, currently working with clients who deploy IoT technologies to improve management of their real estate portfolio. I design my own training courses, and offer production services to other instructors/lecturers or organisations. Data Science observe a pattern in data for decision making whereas AIs look into an intelligent report for decision. There are no special requirements or prerequisites. Alternatively, students can complete the course CS 798 Advanced Research Topics on “Artificial Intelligence: Law, Ethics, and Policy’’. Collect/extract relevant data, visualize and perform exploratory analysis on data. However, real Artificial Intelligence is far from reachable. Using a … You will learn how machine learning is used to predict engine failures, how artificial intelligence is used in anti-ageing, cancer treatment and clinical diagnosis, you will find out what technology is used in managing smart buildings and smart cities including Hudson Yards in New York. My name is Richard Han. Visit our COVID-19 information website to learn how Warriors protect Warriors. 3. Proposal Date. 78 graduates from 29 countries selected from more than 2,000 applicants The student body, including 14 Emiratis, were given a detailed orientation about the university prior to … Students are expected to take at most 1 of the following 2 foundational courses depending on their undergraduate major: CS 600 Fundamentals of Computer Science for Data Science (designed for non-CS major background students), STAT 845 Statistical Concepts for Data Science (designed for non-STAT major background students). In the Above Section, we have studied about Introduction to AI, So now we are going ahead with the components or frameworks that majorly contribute towards the implementation of various intelligent systems are as follows: Our main campus is situated on the Haldimand Tract, the land promised to the Six Nations that includes six miles on each side of the Grand River. DS 701/702 Data Science Project 1 & 2. English language proficiency (ELP) (if applicable). It is currently working with a variety of subfields, ranging from general to specific, such as self-driving cars, playing chess, proving theorems, playing music, Painting, e… Data science has become a necessary leading technology for combining multiple fields including statistics, scientific methods, and data analysis to extract value from data. According to the father of Artificial Intelligence, John McCarthy, it is “The science and engineering of making intelligent machines, especially intelligent computer programs”.Artificial Intelligence is a Data Science & Artificial Intelligence. These courses must normally be taken from the following list of selected graduate courses. The University of Waterloo acknowledges that much of our work takes place on the traditional territory of the Neutral, Anishinaabeg and Haudenosaunee peoples. Big Data Definition and Data Sources. The course will also introduce you to the fundamentals of Artificial Intelligence – state space representation, uninformed search, and reinforcement learning. Perform machine learning models to extract meaningful insights from data. Note (*): CO 769, CS 798, CS courses at the 800 level, and STAT courses at the 900 level should be on a topic in Data Science or Artificial Intelligence; they are subject to the approval of the Graduate Officer. Masters in Banking and Finance. It incorporates techniques of statistics and mathematics, such data mining, multivariate data analysis and visualization, along with computer science and even machine learning to draw knowledge from data and provide both insights and decision paths. Master’s in computer science (in French) A number of options including artificial intelligence, computational biology and operations research. In order to remain in good academic standing, students must maintain an average of 75% and a minimum grade of 70% in all their courses. The ability to extract value from data is becoming increasingly important in the job market of today. I have learnt about Big Data and its 3-4 Vs variable, how to manage the big data as well as the application in various services and industries, such as logistic, property and health care. Data Science and artificial intelligence (AI) are the emergent fields of activity with the most important needs within the digital economy in the coming years. Artificial Intelligence and Data Science are some of the common buzz words that you hear nowadays. Students in the Master of Data Science and Artificial Intelligence - Co-operative Program can apply to transfer into the Master of Data Science and Artificial Intelligence Program after completing at least one academic term. Diploma of Higher Specialized Studies (DESS) in Machine Learning (in French) A program with fewer credits than the master’s program and a shorter internship. Learn about neural networks, or how AI learns, and delve into issues like algorithmic bias and the ethics of AI decision-making. Experience at the senior level in at least one of computer science or statistics. Progress reports are not required; however, the Director will review students’ overall average every term. Ph.D. in computer science If you want to become a Data Scientist, this is the place to begin! You will learn about big data, Internet of Things (IoT), data science, big data technologies, artificial intelligence (AI), machine learning (ML) algorithms, neural networks, and why this could be relevant to you even if you don't have technology or data science background. Hope you will enjoy the course and let me know  in the comments of each section how I can improve the course! Students must complete at least 9 courses: normally 1 foundation course, 5 core courses, and 3 elective courses. Certified in Filmmaking (London Film Academy). Introduction to Data Science and Skillset required for working with Big Data, Technological Breakthroughs which Enable Big Data Solutions (Connectivity, Cloud, Open Source, Hadoop and NoSQL), Big Data Technology Architecture and most popular technology tools used for each Architecture Layer, Beginner's Introduction to Data Analysis, Artificial Intelligence and Machine Learning, Simplified Overview of Machine Learning Algorithms and Neural Networks, Course overview and Introduction to big data, Big Data in Practice - LOGISTICS & TRANSPORTATION, Logistics & Transportation: Social Impact of Artificial Intelligence & IoT, Logistics & Transportation: Predictive & Prescriptive Maintenance, Logistics & Transportation: Prepositioning of Goods and Just in Time inventory, Logistics & Transportation: Route Optimisation, Logistics & Transportation: Warehouse Optimisation and order picking, Logistics & Transportation: The Future of the industry, Big Data in Practice - PREDICTIVE MAINTENANCE IN MANUFACTURING, Predictive Maintenance in Manufacturing - Case Study SIBUR, Big Data in Practice: REAL ESTATE & PROPERTY MANAGEMENT, Real Estate: Introduction to big data in real estate, Real Estate: Business Drivers for Using Big Data, Real Estate & Property Management: Technological Enablers, Real Estate: Building Asset Management and Building Information Modelling, Real Estate: Big Data and IoT in Building Maintenance and Management - examples, Additional Resources to Lecture on Smart Buildings, Real Estate: Smart Cities (examples - Los Angeles and Hudson Yards in New York), Real Estate: Smart Technologies Cost and Government Subsidies (example - Norway), Operational Efficiencies and Sustainability, Healthcare: Data Challenges in Healthcare Industry, Healthcare: Transforming Role of AI and Data Measurement Technologies, Healthcare: Artificial Intelligence in Disease Prevention, Healthcare: Artificial Intelligence in Anti-Ageing, Healthcare: AI in Clinical Decision Making and Cancer Treatment, Healthcare: Clash of AI and Traditional Healthcare Science, Healthcare: Final Remarks - Value of Artificial Intellegence to Consumers, Data Science Definition and Required Skillset, Guest Speakers importance of working in teams & understanding business objective, Data Management Technological Breakthroughs (e.g. In today's world, technology is growing very fast, and we are getting in touch with different new technologies day by day. Identify interesting data-driven questions in your respective field of study. The course includes the interviews with industry experts that cover  big data developments in Real Estate, Logistics & Transportation and Healthcare industries. With increasing unstructured data volume, Data Science is becoming one of the fastest growing technology. AU: 3 | Prerequisite: EE1005 | LAMS/TEL(Online Videos & Resources) (16); Example Classes (26 hrs) Course Aims. Data Science is the most popular field in the world today. Control Flow 5. Data Science and Artificial Intelligence are the most commonly used interchangeably. In short, it is basically a curated list of the latest breakthroughs in AI and Data Science by release date with a clear video explanation, link to a more in-depth article, and code (if applicable). Many startups and MNC's across worldwide are trying to show their supremacy by adopting to this well significant technologies. 5. Passionate about change, strategic development, operational transformation and learning new things. Data scientists and statistical officers combine statistics, mathematics, programming, problem-solving along with the activities of cleansing, preparing, and aligning the data so that it would be useful for everyone including government, scientists, health … In today's era of Information, ‘Data’ is the new driving force, provided we know how to extract relevant ‘Intelligence’. Want to know more about them? Course Code CV0003 Course Title Introduction to Data Science and Artificial Intelligence Pre-requisites CV1014 Introduction to Computation Thinking Pre-requisite for Nil No of AUs 3 Contact Hours LECTURES 0 LAMS/TEL (Online Videos and Resources) 13 EXAMPLE CLASSES (Hands-on Sessions and Seminars) 26 Proposal Date 21 February 2019 Page 1 of 9 MA0218 – Introduction to Data Science and Artificial Intelligence Academic Year AY1920 Semester 2 Course Convener Prof Sameer Alam (MAE) Course Code MA0218 Course Title Introduction to Data Science and Artificial Intelligence Pre-requisites MA1008 Introduction to Computational Thinking OR FE1008 Computing OR CY1402 Computing Pre-requisite for Nil “Expert Talk: Data Science vs. Data Analytics vs. Machine Learning”, Sarihari Sasikumar, Oct 18, 2018 “I. Why we need to be data and technology savvy. This course will start with the core principles of Data Science, and will equip you with the basic tool and techniques of data handling , exploratory data analysis, data … Introduction to Text Analytics with Python is part one of the Text Analytics with Python professional certificate. Here, we look at the 9 best data science courses that are available for free online. Google’s Search Engine – Artificial Intelligence Interview Questions – Edureka. Industry expert insights on IoT, AI and Machine Learning for all. The Graduate Studies Academic Calendar is updated 3 times per year, at the start of each academic term (January 1, May 1, September 1). - ED GODBER - Healthcare Strategist with over 20 years of experience in Healthcare, Pharmaceuticals and start-ups specialising in Artificial Intelligence. Data science is a broad field of study pertaining to data systems and processes, aimed at maintaining data sets and deriving meaning out of them. This need is mainly due to the increasing capacities in data acquisition and processing. You will learn about big data, Internet of Things (IoT), data science, big data technologies, artificial intelligence (AI), machine learning (ML) algorithms, neural networks, and why this could be relevant to you even if you don't have technology or data science background. Machine learning — Arthur Samuel said “Machine Learning is the ability to learn without being explicitly programmed.” 4. The course will motivate you to work closely with data and make data-driven decisions in your field of study. If you are like me - finding it difficult to read thick manuals with formulae, but still very much interested in modern technologies and their applications, then this course is for you. Introduction to Python Programming Language 2. Big Data Technology & Tools for Non-Technical Leaders. This course will start with the core principles of Data Science, and will equip you with the basic tool and techniques of data handling, exploratory data analysis, data visualization, data-based inference, and data-focussed communication. Data science look part of a loop from AIs loop of perception and … Formulate meaningful study problems that you want to explore. Honours Bachelor’s degree or equivalent in data science, computer science, statistics, mathematics or a related field, with a minimum overall average of 78%. Implement above techniques with R. 6. Data Science is a collection of skills such as Statistical technique whereas Artificial Intelligence algorithm technique. Artificial intelligence (AI) has transformed industries around the world, and has the potential to radically alter the field of healthcare. CO 769 Topics in Continuous Optimization(*), CS 686 Introduction to Artificial Intelligence, CS 742 Parallel and Distributed Database Systems, CS 743 Principles of Database Management and Use, CS 786 Probabilistic Inference and Machine Learning, CS 856 Advanced Topics in Distributed Computing(*), CS 885 Advanced Topics in Computational Statistics(*), CS 886 Advanced Topics in Artificial Intelligence, STAT 946 Topics in Probability and Statistics(*). Three terms (12 months) for full-time students. Anyone can learn from this course. In today's era of Information, ‘Data’ is the new driving force, provided we know how to extract relevant ‘Intelligence’. Graduate Studies Academic Calendars from previous terms can be found in the archives. For example, logical proof can be viewed as searching for a path that leads from premises to conclusions, where each step is the application of an inference rule. 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