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DSCI 2010 Course Introduction
Data Science Essentials* (5 credits) (DSCI 2010) Learn to use data science to solve real-world problems. Topics include: working with data, large datasets, and basic statistical concepts; introduction to R; data visualization; prediction, modeling, and forecasting; summarization and similarity search. Prerequisites: DSCI 1101 or equivalent. Visit the course website for more information. Instructors: Zhenyu Xu ; Yingying Wu ; Ling Wang Credits: 5 Class Hours
DSCI 2010 Course Description
Data Science Essentials is a data science course for students with a non-engineering background. Students will learn the fundamental concepts and data analysis techniques needed to identify, describe, and analyze datasets. Students will be introduced to commonly used Python data science libraries such as Pandas, NumPy, SciPy, and Matplotlib. The course is designed to be a hands-on introduction to the basic concepts of data science. It will focus on modern statistical methods such as regression, classification, clustering, visualization, time series
Universities Offering the DSCI 2010 Course
– L3 – Course Offerings
May 30, 2019 to Jun 13, 2019
Donna Cech-McCarty (View Courses)
– BLA – Science and Technology Studies
Zoe Boggess (View Courses)
– BLA – Science and Technology Studies
This course is an introduction to the world of data science. It explores key concepts in data science and the challenges they pose to
DSCI 2010 Course Outline
Introduction to the Data Science discipline, including: data collection and preparation; manipulating data using programming languages; drawing scientific conclusions with data. Through lecture, discussion and hands-on exercises, students will become familiar with the R programming language for statistical computing. The course will also cover tools for big-data analysis, visualization, clustering and classification as well as exploratory data analysis techniques. This course requires strong mathematical and quantitative skills and familiarity with the DSCI 2010 Program Handbook (linked below). Prerequisite(s):
DSCI 2010 Course Objectives
– Part 1: Course Overview This course is the first of three courses required to earn a Bachelors degree in Data Science. By the end of the course, you should be able to: Evaluate data acquisition methods and tools for their utility.
Apply machine learning techniques to solve real-world problems.
Analyze data as a form of information; discover patterns and relationships within the data.
Implement techniques for predictive modeling and feature engineering (including linear regression, decision trees, naive Bayes models, k-ne
DSCI 2010 Course Pre-requisites
Data Analysis & Programming 1 (DSCI 2000) Requirements for DSCI 2010 – Data Science Essentials* (5 credits) (DSCI 2010) Data Visualization 1 (DSCI 2000) Data Visualization 2 (DSCI 2000) Data Analytics & Machine Learning I (DSCI 3000) Required Courses: CompTIA A+ Certification Preparation (CompTIA A+) Introduction to Computer Systems and Networks (A+ Certification Prep) Networking
DSCI 2010 Course Duration & Credits
8-10 weeks Start date: May 26, 2010 End date: July 13, 2010 (No class on Memorial Day, July 4th) Meeting Days & Times: MWF 10:30-11:45AM Location: TBA Instructor(s): Chris Widener
Prerequisites: None DSCI 2011 Course Duration & Credits for DSCI 2011 – Data Science Essentials* (5 credits) (DSCI 2011)
DSCI 2010 Course Learning Outcomes
*See the course webpage for more details.
DSCI 2010 – Data Science Essentials Course Learning Outcomes for DSCI 2010 – Data Science Essentials* (5 credits) (DSCI 2010) Assessments of Students’ Success (50%) Students will be required to take and pass an in-class assessment test, online exam, and a final project. The average score on the in-class assessment is typically in the 70s; the average score on the online exam is typically
DSCI 2010 Course Assessment & Grading Criteria
1. The course grade will be determined based on the following criteria: A+ = 90-100% B+ = 80-89% C+ = 70-79% D+ = 60-69% F < 60% *The DSCI course assessment criteria is based on the syllabus and is subject to change for next semester.
DSCI 2010 Course Fact Sheet
6 Course Description: This course covers concepts and principles of data science, including statistics and machine learning, with a focus on the fundamental problem of gathering data and applying it to meaningful discovery. The topics include descriptive statistics, predictive modeling, clustering, time series analysis, deep learning and reinforcement learning. Students will apply these methods in the context of a variety of real-world problems to complete a research project. More detail is available at: http://www.edx.org/course/data-science-essentials-d
DSCI 2010 Course Delivery Modes
Undergraduate course delivery mode(s): Traditional (Distance) delivery mode for DSCI 2010. Course Delivery Mode(s): Distance delivery mode for DSCI 2010.
* This course will be delivered using a combination of online and face-to-face components. Please see the departmental website for further details.
Understand the structure and key elements of Data Science
Understand R and how to use it to analyse data
Understand the fundamental concepts of machine learning, deep learning
DSCI 2010 Course Faculty Qualifications
– 15 Semester Hours
This course is designed to provide a foundation in the principles of data science and quantitative research methods. The course is intended for students with little or no experience in quantitative methods. It is also appropriate for graduate students who have completed DSCI 2010, but who need a refresher in the basics of data science. Instruction will consist of lecture, discussion, and exercises.
Prerequisites: MATH 1010; A grade of C or better in DSCI 201
DSCI 2010 Course Syllabus
DSCI 2010 course meets for 3 hours per week during the scheduled class times. Class schedule is Tuesday and Thursday, from 4:00-6:20 PM in Lenfest Hall Room 310.
Credits and Grades
Course Grade % Total Points % Effective date Hours/week Fall Spring Summer _____ _____ _____ _____ DSCI 2010 Syllabus http://ischool.wustl.edu/dsci/
Suggested DSCI 2010 Course Resources/Books
– Course Syllabus* (DSCI 2010) – Course Information Packet – Required reading for each unit and section* (DSCI 2010) –
Course Syllabus: DSCI 2010 Database Systems
In this course we will explore databases and data structures. We will learn about design patterns, normalization and other topics that are vital to understanding how databases work. This is an introduction to data structures and databases so expect a good amount of math as we delve into the world
DSCI 2010 Course Practicum Journal
Fall 2010 The purpose of this journal is to help students write a scientific report based on their first research project. 2. Reading Assignment: *DSCI 2010 Course Practicum Journal for DSCI 2010 – Data Science Essentials* (5 credits) (DSCI 2010) Fall 2010 The purpose of this journal is to help students write a scientific report based on their first research project. Writing Assignments: Students will create and submit a short report on the
Suggested DSCI 2010 Course Resources (Websites, Books, Journal Articles, etc.)
Data Science Essentials 1. Caffeine 2. Google News 3. Glucosamine 4. Lattice Programming 5. Elasticity Toolbox (a spreadsheet program) 6. Random Forests (a machine learning library) 7. Python: Beyond the Basics (a basic text for beginning programmers and non-programmers) http://perceptualcomputing.org/Tutorials/Python/EcologicalApplications.htm#random_forests This page has a list of books and articles
DSCI 2010 Course Project Proposal
– University of Waterloo This is a course project proposal for the course DSCI 2010 Data Science Essentials. The course will be taught using the Canvas LMS, in the Fall Term. Due Date: May 16, 2010 at 11:59 p.m.
This is a tentative proposal of the course content; all material remains subject to change. Note that this content is from an earlier version of the course; it may be significantly different after finalizing the content.
DSCI 2010 Course Practicum
(Introduction to Data Science – Introduction to Algorithms) Prerequisite: DSCI 1010. DSCI 2010 is a data science course with an emphasis on the computational aspects of data science. The course covers the basics of programming, statistics, visualization and data mining. Topics include: – Introduction to Data Science (What is Data Science?) – Probability Theory – Descriptive Statistics – Statistical Learning – Regression Techniques – Deep Learning *Practicum credit will be allocated for the practicum, but students
Related DSCI 2010 Courses
Basic Introduction to Data Science (5 credits) (DSCI 2010) Introduction to Data Science and Machine Learning (5 credits) (DSCI 2010) Computational Analysis of Biological Data with R *on request (7 credits) To be considered for enrollment in the DSCI 2010 course, a student must have completed one of the following courses: DSCI 1000 – Data Science and Big Data, DSCI 2001 – Introduction to Computational Life Sciences, or a D
This course is a precursor to DSCI 3010. We will be building on the foundations of data analysis and visualization techniques we learned in the first class. *No P/NP.
This course covers basic statistics and linear regression theory. Topics include conditional probability, marginal models, sampling distributions, normality, chi-square, one-way ANOVA, paired t-tests, correlation, linear regression with categorical dependent variables.
This is a continuation of DSCI 2010.
Interpreting output from
Top 100 AI-Generated Questions
– Course Description
The primary goal of this course is to give you an introduction to machine learning and artificial intelligence (AI), and the basic tools required to solve problems that require some level of AI. We will cover the fundamental concepts of machine learning, data mining, and artificial intelligence, emphasizing their applications in business and industry. Topics include classification methods, decision trees, neural networks, Bayesian models, reinforcement learning algorithms, and other techniques for solving problems using AI.
The course also provides a general introduction to
What Should Students Expect to Be Tested from DSCI 2010 Midterm Exam
*Students will be required to show their mastery of the following concepts in class during the midterm: 1. Introduction to Programming – basic data types, data structures, string manipulation, looping, iterative and recursive programming (list comprehension, recursion, iteration) 2. Statistics – descriptive statistics; interpretation of means, median, mode; distributions of continuous variables; hypothesis tests (t-test for normally distributed samples, t-test for non-normal distributed samples) 3. Exploratory Data Analysis – variable importance
How to Prepare for DSCI 2010 Midterm Exam
2010-06-08 13:23 dsci2010 Midterm Exam Review Session I #1 *View Slides (draft) This is the review session for DSCI 2010. There are only two midterm exams for this class. The first exam will cover the material in Chapter 5, Introduction to Data Science and Chapter 6, Introduction to Data Science II. The second exam covers Chapters 1 through 4, Introduction to Data Science Essentials and Chapter 7,
Midterm Exam Questions Generated from Top 100 Pages on Bing
– Fall 2010 * The questions for this exam are taken from the 100 most popular pages on Bing for DSCI 2010. If you click the question number, you will be redirected to that page. NOTE: The top ten pages with the most hits (at time of writing) are shown in bold text. You can click any of the questions to see the page where it was posted. All questions are multiple choice, with each question having three answer choices (you have only one
Midterm Exam Questions Generated from Top 100 Pages on Google
DSCI 2010 The following exam questions are randomly generated from the material covered in the lecture and textbook. The questions are numbered and come with some commentaries about why these questions may be asked in this way or that one. How should a machine learn from its mistakes? Can it learn by trial and error, or can it learn by looking at the correct answer? If so, how do we know when to go on the “right” path? (4) Questions: A) “Modern
will be administered on the following dates and times:
Dates: Thursday, March 10th, 2020, 5:00pm – 7:30pm
Location: Virtual Classroom (Online)
Exam will cover material from Chapters 1-4 of Introduction to Data Science (DSCI 2010)
Results will be available by March 13th
*Note that if you do not complete this course with a minimum grade of C+, you will need to retake the exam for
Top 100 AI-Generated Questions
Syllabus with Lecture Notes, Assignments and Readings (access via Canvas) Updated: 6/4/18.
Lecture Notes for Database Concepts and SQL *for Introduction to Data Science course (10 credits) – Updated: 5/14/18. DSCI 1011.01 Database Concepts and SQL*Introduction to Data Science Course Syllabus with Lecture Notes, Assignments and Readings.
(Credit Hours: 3.0) Course Learning Objectives:
What Should Students Expect to Be Tested from DSCI 2010 Final Exam
It is recommended that students take DSCI 2010 in the fall semester. It is also recommended that students take DSCI 2900 as a prerequisite. It is also required that DSCI 2900 be taken before taking DSCI 2010. After taking both of these classes, it is possible to have taken the course at another university (with the permission of the instructor) and receive credit for it.
When you register for DSCI 2900, please
How to Prepare for DSCI 2010 Final Exam
– 1,300 words. 1. Show your understanding of the world by telling stories using data from a variety of sources: 2
Computer Science 1 – Computer Science Department The Department of Computer Science at the University of Minnesota was founded in 1955 to provide students with an interdisciplinary program that would prepare them for careers in industry, government, and academia. During its
Cybersecurity Careers: Salary Information | U.S. News & World Report Our program prepares graduates for careers in
Final Exam Questions Generated from Top 100 Pages on Bing
This document shows the questions, answers, and hints that were generated for this exam in 2017. This is based on 40% of students who took the exam in 2017. The questions are organized by category. Questions were generated using Pure Calculus, Microsoft Excel, and SQL.
This document is intended for students who have taken a prior DSCI course. It will not be updated with new questions or answers each year because this course does not require the use of these tools.
Final Exam Questions Generated from Top 100 Pages on Google
– Spring 2018.
Week by Week Course Overview
DSCI 2010 Week 1 Description
This course introduces the core ideas and techniques of data science, without requiring prior background in statistics or mathematics. The concepts are presented through the use of algorithms and a variety of examples from contemporary applied and academic research. This course is intended for students with little or no experience in statistics, but will also be accessible to those with significant experience in statistics who have a specific interest in learning about computational methods for data analysis. Required texts: * Scott E. Murray, “Introduction to Data Science”, Cambridge University Press
DSCI 2010 Week 1 Outline
*Last modified October 2016, Contact the DSCI Dept. This course presents a unified vision of data science, describing how data science is used to discover patterns and trends from large volumes of data in contexts ranging from bioinformatics to marketing. The class focuses on the importance of exploring both structured and unstructured data, and discusses some of the challenges facing researchers in these areas. The topics covered include: Why extract patterns and trends from data? How can we use both structured and unstructured data?
DSCI 2010 Week 1 Objectives
Introduction to data science and its applications. Data mining, machine learning, artificial intelligence, and deep learning. *DSCI 2010 Week 1 Objectives for DSCI 2010 – Data Science Essentials* (5 credits) (DSCI 2010) Introduction to data science and its applications. Data mining, machine learning, artificial intelligence, and deep learning. Introduction to MATLAB: GUI programming Introduction to MATLAB: Numerical computations Instructors: Jonathan Amaya
DSCI 2010 Week 1 Pre-requisites
Lecture: Wednesdays, 4:00-5:20pm, EESC 225
Instructor: Angela K. Bennett
Office Hours: Thursdays, 2-4 pm, EESC 325
Course Description: Introduction to statistical methods for data analysis and predictive modeling. The course provides an overview of the fundamentals of data science and software for data scientists as well as a survey of applications of statistics to data science problems.
The course will provide
DSCI 2010 Week 1 Duration
Information for Students *Please note that you will only be enrolled in DSCI 2010. DSCI 2010 is a prerequisite for some courses (e.g., DSCI 3021; DSCI 4025). If you want to earn your Data Science Certificate, then you must complete DSCI 2010 and any of the following: •DSCI 3121* or DSCI 3125* – Introduction to Computer Science and Programming (3 credits) •DSCI
DSCI 2010 Week 1 Learning Outcomes
Data Science Foundation for Data Science DSCI 2010 Week 1 Assignment: Preparing Digital Collections for Mining (DSCI 2010) DSCI 2010 Week 1 Assignment: Setting up a Digital Library (DSCI 2010) DSCI 2010 Week 1 Project Presentation (DSCI 2010) DSCI 2010 Week 2 Data Warehouse Design and Modeling (DSCI 2007/2008) DATAW-4010 Data Warehousing Fund
DSCI 2010 Week 1 Assessment & Grading
Week 1 Assessment & Grading for DSCI 2010 – Data Science Essentials* (5 credits) (DSCI 2010) Week 2 Assignment #1: Data Set Input, Analysis and Visualization * (5 credits) (DSCI 2010) Week 2 Assignment #1: Data Set Input, Analysis and Visualization * (5 credits) (DSCI 2010) Week 3 Assignment #2: Classification Models with Random Forests * (10 credits) (
DSCI 2010 Week 1 Suggested Resources/Books
– Course Resources – DSCI 2010 Week 1 Suggested Resources/Books for DSCI 2010. We will start with a short pre-course introduction on data science fundamentals. You need to be able to recognize data and work with it, generate and clean it, interpret it in context, create visualizations and do analysis using R or Python. The class will include discussions on data modeling including the core concepts of database design.
4th International Conference on Data Science and Engineering
DSCI 2010 Week 1 Assignment (20 Questions)
at University of Toronto. Similar Assignment. We found the best assignment help in Australia at MyAssignmenthelp.com with a team of 700+ Ph.D. certified expert writers who offer homework help to students worldwide. StudentsAssignmentHelp.com provides 100% original and high quality homework solutions for all subjects to their customers including engineering, medical, management, arts and science.
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DSCI 2010 Week 1 Assignment Question (20 Questions)
Week 1 Assignment Questions (20 questions) for DSCI 2010 – Data Science Essentials* (5 credits) (DSCI 2010). All. I can’t download the assignments and follow the tutorial, please help. Click here to visit our frequently asked questions about HTML5 video.
Date: May 12th Time: 6PM – 9PM Location: Room #304, School of Information Studies, Ohio State University URL: https://www.facebook.com/events/631614
DSCI 2010 Week 1 Discussion 1 (20 Questions)
at University of California, San Diego – Spring 2010.
*This is an open course. You can find the syllabus here.
Please provide me with your comments if you have any questions or concerns.
Have fun with the course and please keep in mind that this is an introductory course about data science for those interested in learning more about it. We will cover a range of topics related to machine learning and statistical inference and data analytics. The lectures will be interactive and your participation is key! If
DSCI 2010 Week 1 DQ 1 (20 Questions)
DSCI 2010 Week 1 DQ 2 (20 Questions) for DSCI 2010 – Data Science Essentials* (5 credits) (DSCI 2010) DSCI 2010 Week 1 DQ 3 (20 Questions) for DSCI 2010 – Data Science Essentials* (5 credits) (DSCI 2010)
What are the possible business impacts of this data warehouse design decision?
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DSCI 2010 Week 1 Discussion 2 (20 Questions)
at University of Washington – Tacoma on StudyBlue. ECE 1302: Fundamentals of Electronics and Communication Systems I and ECE 1303: Fundamentals of. A free inside look at company reviews and salaries posted anonymously by employees. Give me a number between 1-100 with two decimal places using powers of ten. The total length of the rectangle (assuming a width of 4) is the length x width = 4x. This is only an estimate based on the information
DSCI 2010 Week 1 DQ 2 (20 Questions)
N/A DSCI 2010 Week 1 DQ 2 (20 Questions) for DSCI 2010 – Data Science Essentials* (5 credits) (DSCI 2010) N/A DSCI 2010 Week 1 DQ 2 (20 Questions) for DSCI 2010 – Data Science Essentials* (5 credits) (DSCI 2010) N/A
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DSCI 2010 Week 1 Quiz (20 Questions)
at University of Missouri – Kansas City. Learn vocabulary, terms, and more with flashcards, games, and other study tools. Our “DSCI 2010 Week 1 Quiz” quiz is comprised of 5 sets of questions arranged in a form of a quiz. You can use your existing account or create a new one to take this quiz.
dsci 2010 week 1 quiz
I have taken DSCI 2010 Week 1 Quiz and I am very impressed with the
DSCI 2010 Week 1 MCQ’s (20 Multiple Choice Questions)
at University of Waterloo
1. What is a time series? A) The average value of a variable over a period of time B) A collection of values which are related to each other C) A data set that is repeated over a period of time D) None of these
2. What is the difference between discrete and continuous variables? A) Discrete variables can only be represented as numbers. B) Continuous variables can have many different values, whereas discrete variables only have 2 possible values
DSCI 2010 Week 2 Description
This course is a survey of the core concepts and methods used in the study of data science. Students will learn how to organize, analyze, visualize and present data using techniques such as data mining, machine learning, and artificial intelligence. The course will include an overview of tools like R for statistical computing and visualization, as well as advanced topics such as big data analysis. No previous programming experience is required.
Course Goal: To expose students to fundamental concepts in Data Science.
DSCI 2010 Week 2 Outline
Course Schedule 2010 (DSCI 2010) Week 1 Introduction (DSCI 2010) – Data Science Essentials (5 credits) * DSCI 2010 Week 2 Course Outline – Spring 2011 (DSCI 2010) • Introduction to Data Science Concepts and Terminology • Lecture: Describing Data (slides, video) • Homework: R Vocabulary list from introduction to data science, for study of R • Lecture: Boxplots and the Basics of Probability
DSCI 2010 Week 2 Objectives
Objectives for Week 2 *Students will: (1) understand the fundamental definitions and concepts of data science; (2) understand the role that high-dimensional systems play in our world today; and (3) describe how they can be modeled, manipulated, and analyzed using high-dimensional data. Assignment 1 Due Date: November 5th Assignment 2 Due Date: November 12th * Students will be expected to complete both assignments in a timely manner. The due date is not extended if
DSCI 2010 Week 2 Pre-requisites
Fundamentals of Programming (5 credits) (FSCI 2010) Principles of Machine Learning (5 credits) (PLSCI 2010) Data Management for Machine Learning (5 credits) (DML 2010) Data Analysis for Machine Learning (5 credits) (DML 2010)
* Includes an introduction to data science and is intended to provide a foundational background for DSCI students.
DSCI 2010 Week 3 – Convolutional Neural Networks, GANs
DSCI 2010 Week 2 Duration
2 weeks • Course Description This course will be an introduction to programming, data analysis and machine learning. Students will learn the basics of python, explore the pandas library and use it to manipulate data structures, perform numerical analysis on data using NumPy and SciPy, and create plots using matplotlib. This is an introduction to data science concepts and techniques using Python. The course will build on previous CS courses in data representation, numerical computing and statistical modeling.
18 DSCI 2010 Week 3 Duration
DSCI 2010 Week 2 Learning Outcomes
Week 2, The Arrivals of 2010: Data and Classification My Lab Assignment for DSCI 2010 Week 2 (DSCI 2010) Week 2, The Arrivals of 2010: Data and Classification Your Assignment for DSCI 2010 Week 2 (DSCI 2010) Week 3, Introduction to Computational Thinking: Defining the Problem How do you define a problem? (DSCI 2010) Week 3, Introduction to
DSCI 2010 Week 2 Assessment & Grading
Course Description: This course is designed to provide the foundation necessary for the study of science. The focus of this course is on introducing students to data science and its role in solving real-world problems. We will use a variety of open-source software tools to solve challenging data problems and present our results in a presentation format. We will also discuss best practices and techniques for handling large data sets. In this course, we will cover topics such as exploratory data analysis, machine learning, text analytics, anomaly detection
DSCI 2010 Week 2 Suggested Resources/Books
*Note: I also recommend the following resources for this class, but they are not required:*** Data Science Basics by Jeffrey Ullman.*** A Course in Statistical Learning by Nick Ayache, John Platt, and Yan Zeng.*** Introductory Probability and Statistics for Data Science by Robert C. Martin. ***Data Mining Concepts and Techniques* (3 credits) (DSCI 2010) ***Machine Learning* (3 credits) (DSCI 2010) ***Ensemble Methods
DSCI 2010 Week 2 Assignment (20 Questions)
Week 2 Discussion Questions (50 points) 1. Why does it make sense to choose a Bayesian model and Bayes factor when fitting data from univariate distributions versus multivariate distributions? 2. For this week’s activity, discuss what the following are not binned variables: age, gender, country of birth, income level (Q11), height (Q14), weight (Q17). 3. How is a variable’s distribution related to the distribution of its classes? 4
DSCI 2010 Week 2 Assignment Question (20 Questions)
at University of North Carolina, Chapel Hill
You are required to submit a week 2 assignment in which you will work through the following questions:
1. Explain what a sample is and why it matters.
2. Name a type of data set that would be useful for decision making.
3. Define the following:
a) A dataset
d) Input data set.
4. Define the following:
b) Cluster analysis
DSCI 2010 Week 2 Discussion 1 (20 Questions)
at University of California, San Diego. View Homework Help – DSIG 2010 Week 2 Discussion 1 (20 Questions) for DSCI 2010 – Data Science Essentials* (5 credits) (DSCI 2010) from DSIG 2010 at University of California, San Diego.
Week 3 Discussion Questions Week 3 Discussion Questions Name: Institution: Week 3 Discussion Question #1. What are the main problems that we currently face in regard to environmental sustainability?
DSCI 2010 Week 2 DQ 1 (20 Questions)
*All answers must be in APA format, and references are not required. 1. What is the difference between a data scientist and a data engineer? (10 points) 2. Distinguish between statistical modeling and machine learning using an example. (15 points) 3. What is the purpose of using a tool such as R, SAS, or SPSS to perform hypothesis testing? (15 points) 4. What are some of the common pitfalls that occur during data analysis? Give
DSCI 2010 Week 2 Discussion 2 (20 Questions)
at University of Maryland, College Park from Oct 20, 2010. Question 1 The Big Data Community describes a heterogeneous computing environment where multiple types of data are being collected and processed in real time. Compute resources may be shared across the community. At this time, compute resources that are shared across the community include: (a) separate clusters for each application or service; (b) machines for common use; and (c) an organization-wide cluster that provides a unified service to all users
DSCI 2010 Week 2 DQ 2 (20 Questions)
(formerly DSCI 3010). The following questions are from the Discussion Board. You can access them via the link above, or by going to the “Discussion” tab in the upper left of this course page. (I am not sure why you need to login in order to access the Discussion Board; however, I imagine this is an issue for those who do not have their own login credentials.) Question 1: What types of data sets would benefit from a data mining technique? Why?
DSCI 2010 Week 2 Quiz (20 Questions)
at University of Waterloo, Canada.
Syllabus and Course Packets are also available on your dashboard. You may access them from “Course Resources” in the DSCI 2010 course site.
For this assignment, you will use the data visualization software QlikView to create a visualization and a
report. Use at least two different charts (each with its own set of axes) for each chart below.
1. Plot the percentage change in the quarterly GDP growth rate for over the last five
DSCI 2010 Week 2 MCQ’s (20 Multiple Choice Questions)
for DSCI 2010 – Data Science Essentials* (5 credits) at University of Guelph, Canada. For more course tutorials visit
Week 2: Collaborative and Community Programming
1. What do you understand by the term “Community” in the context of collaborative programming?
2. What are some examples of projects or collaborative efforts that involve community members outside of your own organization?
3. Why do
DSCI 2010 Week 3 Description
This course provides an introduction to using computational science to analyze large data sets and provides a basic understanding of the fundamentals of programming. Topics include the use of data mining techniques, data visualization, probabilistic methods, and machine learning. Students will apply these principles in developing analytical models that can be used in solving business problems. The course focuses on the core concepts of Data Science with an emphasis on R, Python, and SQL. Keywords: Data Science; Data Mining; Statistics; Business Analytics; R; Python
DSCI 2010 Week 3 Outline
Week 3 Outline for DSCI 2010 – Data Science Essentials* (5 credits) (DSCI 2010) Week 3 Outline for DSCI 2010 – Data Science Essentials* (5 credits) (DSCI 2010)
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DSCI 2010 Week 3 Objectives
DSCI 2010 Week 3 Objectives for DSCI 2010 – Data Science Essentials* (5 credits) (DSCI 2010) – Course Outline Computer-based project management and analysis of data sets using the R statistical software package. The statistics course focuses on statistical estimation, hypothesis testing, multivariate analysis, and visualization of data in graphical form. Topics include regression models