Start Date: 08/16/2020
Course Type: Common Course |
Course Link: https://www.coursera.org/learn/datasciencemathskills
Data science courses contain math—no avoiding that! This course is designed to teach learners the basic math you will need in order to be successful in almost any data science math course and was created for learners who have basic math skills but may not have taken algebra or pre-calculus. Data Science Math Skills introduces the core math that data science is built upon, with no extra complexity, introducing unfamiliar ideas and math symbols one-at-a-time. Learners who complete this course will master the vocabulary, notation, concepts, and algebra rules that all data scientists must know before moving on to more advanced material. Topics include: ~Set theory, including Venn diagrams ~Properties of the real number line ~Interval notation and algebra with inequalities ~Uses for summation and Sigma notation ~Math on the Cartesian (x,y) plane, slope and distance formulas ~Graphing and describing functions and their inverses on the x-y plane, ~The concept of instantaneous rate of change and tangent lines to a curve ~Exponents, logarithms, and the natural log function. ~Probability theory, including Bayes’ theorem. While this course is intended as a general introduction to the math skills needed for data science, it can be considered a prerequisite for learners interested in the course, "Mastering Data Analysis in Excel," which is part of the Excel to MySQL Data Science Specialization. Learners who master Data Science Math Skills will be fully prepared for success with the more advanced math concepts introduced in "Mastering Data Analysis in Excel." Good luck and we hope you enjoy the course!
This module contains three lessons that are build to basic math vocabulary. The first lesson, "Sets and What They’re Good For," walks you through the basic notions of set theory, including unions, intersections, and cardinality. It also gives a real-world application to medical testing. The second lesson, "The Infinite World of Real Numbers," explains notation we use to discuss intervals on the real number line. The module concludes with the third lesson, "That Jagged S Symbol," where you will learn how to compactly express a long series of additions and use this skill to define statistical quantities like mean and variance.
Data Science Math Skills Data Science Math Skills is the first course in the Data Science Specialization. This course will introduce data scientists and data analysts to basic math concepts and gives you the tools to apply them in practice. Data scientists will learn how to access, manipulate, and manipulate data using basic math formulas. These tools will prepare them to work with more advanced math problems. Data Science Math Skills will build on the research presented in the first Data Science Math Capstone Course, which is the second course in the Specialization. This course will focus on building more advanced applications in Excel by adding new elements, new formulas, and new datasets. These topics will require more advanced math knowledge and will be introduced through peer review. Data Science Math Skills introduces the knowledge of accessing, manipulating, and manipulating data using basic math formulas. These tools will prepare them to work with more advanced math problems. Data Science Math Skills introduces the concepts of arithmetic and algebra. These topics will require more advanced math knowledge. The course will have four parts. First, we will introduce the topics of data analysis and data visualization. We will then introduce the basic concepts of data visualization and explore graphics. We will then introduce the basics of data manipulation and how to introduce new data into Excel. We will then apply the basics of Excel to introduce new data. And finally, we will introduce a number series and its exponential form. Each part of the course is designed to give you a good understanding of the
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Pre-math skills | "Pre-math skills" (referred to in British English as pre-maths skills) is a term used in some countries to refer to math skills learned by preschoolers and kindergarten students, including learning to count numbers (usually from 1 to 10 but occasionally including 0), learning the proper sequencing of numbers, learning to determine which shapes are bigger or smaller, and learning to count objects on a screen or book. Pre-math skills are also tied into literacy skills to learn the correct pronunciations of numbers. |
Data science | he initiated the modern, non-computer science, usage of the term "data science" and advocated that statistics be renamed data science and statisticians data scientists. |
Data science | In 2013, the IEEE Task Force on Data Science and Advanced Analytics was launched, and the first international conference: IEEE International Conference on Data Science and Advanced Analytics was launched in 2014. In 2014, the American Statistical Association section on Statistical Learning and Data Mining renamed its journal to "Statistical Analysis and Data Mining: The ASA Data Science Journal" and in 2016 changed its section name to "Statistical Learning and Data Science". In 2015, the International Journal on Data Science and Analytics was launched by Springer to publish original work on data science and big data analytics. 2013 the first "European Conference on Data Analysis (ECDA)" was organised in Luxembourg establishing the European Association for Data Science (EuADS) in August 2015. In September 2015 the Gesellschaft für Klassifikation (GfKl) added to the name of the Society "Data Science Society" at the third ECDA conference at the University of Essex, Colchester, UK. |
Data science | Turing award winner Jim Gray imagined data science as a "fourth paradigm" of science (empirical, theoretical, computational and now data-driven) and asserted that "everything about science is changing because of the impact of information technology" and the data deluge. |
Data science | Data science, also known as data-driven science, is an interdisciplinary field about scientific methods, processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to Knowledge Discovery in Databases (KDD). |
Data science | The term "data science" (originally used interchangeably with "datalogy") has existed for over thirty years and was used initially as a substitute for computer science by Peter Naur in 1960. In 1974, Naur published "Concise Survey of Computer Methods", which freely used the term data science in its survey of the contemporary data processing methods that are used in a wide range of applications. |
Data science | Data science is a "concept to unify statistics, data analysis and their related methods" in order to "understand and analyze actual phenomena" with data. |
Data science | In April 2002, the International Council for Science: Committee on Data for Science and Technology (CODATA) started the "Data Science Journal", a publication focused on issues such as the description of data systems, their publication on the internet, applications and legal issues. Shortly thereafter, in January 2003, Columbia University began publishing "The Journal of Data Science", which provided a platform for all data workers to present their views and exchange ideas. The journal was largely devoted to the application of statistical methods and quantitative research. In 2005, The National Science Board published "Long-lived Digital Data Collections: Enabling Research and Education in the 21st Century" defining data scientists as "the information and computer scientists, database and software and programmers, disciplinary experts, curators and expert annotators, librarians, archivists, and others, who are crucial to the successful management of a digital data collection" whose primary activity is to "conduct creative inquiry and analysis." |
Data science | Although use of the term "data science" has exploded in business environments, many academics and journalists see no distinction between data science and statistics. Writing in Forbes, Gil Press argues that data science is a buzzword without a clear definition and has simply replaced “business analytics” in contexts such as graduate degree programs. In the question-and-answer section of his keynote address at the Joint Statistical Meetings of American Statistical Association, noted applied statistician Nate Silver said, “I think data-scientist is a sexed up term for a statistician...Statistics is a branch of science. Data scientist is slightly redundant in some way and people shouldn’t berate the term statistician.” |
Chicago Math and Science Academy | Chicago Math and Science Academy offers a variety of extracurricular clubs: |
Chicago Math and Science Academy | Advanced Placement (AP) courses are available in English, history, science, and math. |
Math Science Teaching Corps | The Math Science Teaching Corps Act of 2006 (or MSTC, pronounced "mystic") is legislation based on nonprofit Math for America's (MƒA's) Programs, the MƒA Fellowship and the MƒA Master Teacher Program. The bill was introduced in the 109th Congress by Charles Schumer in the Senate as S. 2248 and by Jim Saxton in the House as H.R. 4705. The MSTC legislation creates a National Science Foundation Fellowship Program to recruit, train, and retain outstanding math and science teachers. MƒA is engaged in an advocacy campaign to move forward federal legislation based on MSTC principles. |
Data science | In 2001, William S. Cleveland introduced data science as an independent discipline, extending the field of statistics to incorporate "advances in computing with data" in his article "Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics," which was published in Volume 69, No. 1, of the April 2001 edition of the International Statistical Review / Revue Internationale de Statistique. In his report, Cleveland establishes six technical areas which he believed to encompass the field of data science: multidisciplinary investigations, models and methods for data, computing with data, pedagogy, tool evaluation, and theory. |
Data science | "Data Scientist" has become a popular occupation with Harvard Business Review dubbing it "The Sexiest Job of the 21st Century" and McKinsey & Company projecting a global excess demand of 1.5 million new data scientists. Universities are offering masters courses in data science. Shorter private bootcamps are also offering data science certificates including student-paid programs like General Assembly to employer-paid programs like The Data Incubator. |
Singapore math | Compared to a traditional U.S. math curriculum, Singapore math focuses on fewer topics but covers them in greater detail. Each semester-level Singapore math textbook builds upon prior knowledge and skills, with students mastering them before moving on to the next grade. Students, therefore, need not re-learn these skills at the next grade level. By the end of sixth grade, Singapore math students have mastered multiplication and division of fractions and can solve difficult multi-step word problems. |
Minnesota Math and Science Academy | The Math and Science Academy celebrates a certain nerd/geek culture and annually holds several notable events. |
National Math and Science Initiative | The National Math and Science Initiative (NMSI) is a non-profit organization based in Dallas, Texas, that launched in 2007. Its mission is to improve student performance in the subjects of science, technology, engineering, and math (STEM) in the United States. It attempts to do this by scaling up local academic programs to a national level. |
Sylvan Heights Science Charter School | Statewide 61.9% of fifth (5th) graders were on grade level in reading, while 42.8% demonstrated on grade level math skills. Pennsylvania 4th graders were 58.6% on grade level in reading and 44.4% demonstrated on grade level math skills. In science, 77.3% of fourth graders showed on grade level understanding. Among Pennsylvania third (3rd) graders, 62% were reading on grade level, while 48.5% demonstrated on grade level math skills. |
Orion Academy (California) | Orion Academy has teachers in language arts, math, science, special education, social science, and social skills. |
Data science | In 1996, members of the International Federation of Classification Societies (IFCS) met in Kobe for their biennial conference. Here, for the first time, the term data science is included in the title of the conference ("Data Science, classification, and related methods"), after the term was introduced in a roundtable discussion by Chikio Hayashi. |