On Friday, Feb. 26, 2016, Pittsburgh Data Jam advisory member and Oracle enterprise architect Brian Macdonald led a hands-on workshop for teachers and students participating int the 2016 Pittsburgh Data Jam to learn about basic data analysis. The workshop was conducted at Carnegie Mellon University. This page includes the presentations, slides, and materials from that workshop.
On Friday, Feb. 26, 2016, Pittsburgh Data Jam advisory member and Oracle enterprise architect Brian Macdonald led a hands-on workshop for teachers and students participating int the 2016 Pittsburgh Data Jam to learn about basic data analysis. The workshop was conducted at Carnegie Mellon University. This page includes the presentations, slides, and materials from that workshop.
GradTrack: Getting Started with Statistics September 20, 2018Nancy Garmer
Dr. Gary Burns, Professor, School of Psychology, Florida Institute of Technology Evans Library Introduction to Statistics: Don't be afraid
Video presentation with audio available on YouTube:http://bit.ly/GradTrackStatistics2018
YouTube Presentation: http://bit.ly/GradTrackStatistics2018
Dr. Gary Burns, Professor, School of Psychology, Florida Institute of Technology, Evans Library GradTrack Workshop
Statistical Techniques for Processing & Analysis of Data Part 9.pdfAdebisiAdetayo1
the present book has been written with two clear objectives, viz., (i) to
enable researchers, irrespective of their discipline, in developing the most appropriate methodology
for their research studies; and (ii) to make them familiar with the art of using different researchmethods
and techniques. It is hoped that the humble effort made in the form of this book will assist in
the accomplishment of exploratory as well as result-oriented research studies.
The Power of Topology - Colleen Farrelly - WiDS Miami 2018Catalina Arango
A lot of data science coverage in the media focuses on big data—storage systems, deep learning, and analyzing data with billions or trillions of observations. However, there’s an equally pressing problem in many industries and smaller companies today: small sample sizes or small subgroups within larger datasets. Machine learning algorithms fail to converge. Statistical methods break down completely. And valuable insight is lost.
However, recent advances in a branch of machine learning called topological data analysis (TDA), along with novel applications of topology to existing statistical methods, have provided a toolset suited to the challenges of small data. These methods have great potential as the field of data science moves from quantity to quality of data. This talk overviews several of TDA’s major tools, as well as their applications to three projects in which traditional methods fail.
Women in Data Science 2018 Slides--Small Samples, Subgroups, and TopologyColleen Farrelly
A lot of data science coverage in the media focuses on big data—storage systems, deep learning, and analyzing data with billions or trillions of observations. However, there’s an equally pressing problem in many industries and smaller companies today: small sample sizes or small subgroups within larger datasets. Machine learning algorithms fail to converge. Statistical methods break down completely. And valuable insight is lost.
However, recent advances in a branch of machine learning called topological data analysis (TDA), along with novel applications of topology to existing statistical methods, have provided a toolset suited to the challenges of small data. These methods have great potential as the field of data science moves from quantity to quality of data. This talk overviews several of TDA’s major tools, as well as their applications to three projects in which traditional methods fail.
I will link to the video when it is made available :)
6.04218.062J Mathematics for Computer Science September 9, 20.docxtroutmanboris
6.042/18.062J Mathematics for Computer Science September 9, 2010
Tom Leighton and Marten van Dijk
Problem Set 1
Problem 1. [24 points]
Translate the following sentences from English to predicate logic. The domain that you are
working over is X, the set of people. You may use the functions S(x), meaning that “x has
been a student of 6.042,” A(x), meaning that “x has gotten an ‘A’ in 6.042,” T (x), meaning
that “x is a TA of 6.042,” and E(x, y), meaning that “x and y are the same person.”
(a) [6 pts] There are people who have taken 6.042 and have gotten A’s in 6.042
(b) [6 pts] All people who are 6.042 TA’s and have taken 6.042 got A’s in 6.042
(c) [6 pts] There are no people who are 6.042 TA’s who did not get A’s in 6.042.
(d) [6 pts] There are at least three people who are TA’s in 6.042 and have not taken 6.042
Problem 2. [24 points]
Use a truth table to prove or disprove the following statements:
(a) [12 pts]
¬(P ∨ (Q ∧ R)) = (¬P ) ∧ (¬Q ∨ ¬R)
(b) [12 pts]
¬(P ∧ (Q ∨ R)) = ¬P ∨ (¬Q ∨ ¬R)
Problem 3. [24 points]
The binary logical connectives ∧ (and), ∨ (or), and (implies) appear often in not only ⇒
computer programs, but also everyday speech. In computer chip designs, however, it is
considerably easier to construct these out of another operation, nand, which is simpler to
represent in a circuit. Here is the truth table for nand:
P Q
true true false
true false true
false true true
false false true
P nand Q
2 Problem Set 1
(a) [12 pts] For each of the following expressions, find an equivalent expression using only
nand and (not), as well as grouping parentheses to specify the order in which the operations ¬
apply. You may use A, B, and the operators any number of times.
(i) A ∧ B
(ii) A ∨ B
(iii) A B⇒
(b) [4 pts] It is actually possible to express each of the above using only nand, without
needing to use ¬. Find an equivalent expression for ( A) using only nand and grouping ¬
parentheses.
(c) [8 pts] The constants true and false themselves may be expressed using only nand.
Construct an expression using an arbitrary statement A and nand that evaluates to true re
gardless of whether A is true or false. Construct a second expression that always evaluates
to false. Do not use the constants true and false themselves in your statements.
Problem 4. [10 points] You have 12 coins and a balance scale, one of which is fake. All
the real coins weigh the same, but the fake coin weighs less than the rest. All the coins
visually appear the same, and the difference in weight is imperceptible to your senses. In at
most 3 weighings, give a strategy that detects the fake coin. (Note: the scale in this problem
is a scale with two dishes, which tips toward the side that is heavier. For clarification, do an
image search for “balance scale”).
Problem 5. [6 points] Prove the following statement by proving its contrapositive: if r is
irrational, then r1/5 is irrational. (Be sure to state the cont.
Big Data - To Explain or To Predict? Talk at U Toronto's Rotman School of Ma...Galit Shmueli
Slide from Prof. Galit Shmueli's talk at University of Toronto's Rotman School of Management, March 4, 2016. This talk is part of Rotman's Big Data Expert Speaker Series.
https://www.rotman.utoronto.ca/ProfessionalDevelopment/Events/UpcomingEvents/20160304GalitShmueli.aspx
GradTrack: Getting Started with Statistics September 20, 2018Nancy Garmer
Dr. Gary Burns, Professor, School of Psychology, Florida Institute of Technology Evans Library Introduction to Statistics: Don't be afraid
Video presentation with audio available on YouTube:http://bit.ly/GradTrackStatistics2018
YouTube Presentation: http://bit.ly/GradTrackStatistics2018
Dr. Gary Burns, Professor, School of Psychology, Florida Institute of Technology, Evans Library GradTrack Workshop
Statistical Techniques for Processing & Analysis of Data Part 9.pdfAdebisiAdetayo1
the present book has been written with two clear objectives, viz., (i) to
enable researchers, irrespective of their discipline, in developing the most appropriate methodology
for their research studies; and (ii) to make them familiar with the art of using different researchmethods
and techniques. It is hoped that the humble effort made in the form of this book will assist in
the accomplishment of exploratory as well as result-oriented research studies.
The Power of Topology - Colleen Farrelly - WiDS Miami 2018Catalina Arango
A lot of data science coverage in the media focuses on big data—storage systems, deep learning, and analyzing data with billions or trillions of observations. However, there’s an equally pressing problem in many industries and smaller companies today: small sample sizes or small subgroups within larger datasets. Machine learning algorithms fail to converge. Statistical methods break down completely. And valuable insight is lost.
However, recent advances in a branch of machine learning called topological data analysis (TDA), along with novel applications of topology to existing statistical methods, have provided a toolset suited to the challenges of small data. These methods have great potential as the field of data science moves from quantity to quality of data. This talk overviews several of TDA’s major tools, as well as their applications to three projects in which traditional methods fail.
Women in Data Science 2018 Slides--Small Samples, Subgroups, and TopologyColleen Farrelly
A lot of data science coverage in the media focuses on big data—storage systems, deep learning, and analyzing data with billions or trillions of observations. However, there’s an equally pressing problem in many industries and smaller companies today: small sample sizes or small subgroups within larger datasets. Machine learning algorithms fail to converge. Statistical methods break down completely. And valuable insight is lost.
However, recent advances in a branch of machine learning called topological data analysis (TDA), along with novel applications of topology to existing statistical methods, have provided a toolset suited to the challenges of small data. These methods have great potential as the field of data science moves from quantity to quality of data. This talk overviews several of TDA’s major tools, as well as their applications to three projects in which traditional methods fail.
I will link to the video when it is made available :)
6.04218.062J Mathematics for Computer Science September 9, 20.docxtroutmanboris
6.042/18.062J Mathematics for Computer Science September 9, 2010
Tom Leighton and Marten van Dijk
Problem Set 1
Problem 1. [24 points]
Translate the following sentences from English to predicate logic. The domain that you are
working over is X, the set of people. You may use the functions S(x), meaning that “x has
been a student of 6.042,” A(x), meaning that “x has gotten an ‘A’ in 6.042,” T (x), meaning
that “x is a TA of 6.042,” and E(x, y), meaning that “x and y are the same person.”
(a) [6 pts] There are people who have taken 6.042 and have gotten A’s in 6.042
(b) [6 pts] All people who are 6.042 TA’s and have taken 6.042 got A’s in 6.042
(c) [6 pts] There are no people who are 6.042 TA’s who did not get A’s in 6.042.
(d) [6 pts] There are at least three people who are TA’s in 6.042 and have not taken 6.042
Problem 2. [24 points]
Use a truth table to prove or disprove the following statements:
(a) [12 pts]
¬(P ∨ (Q ∧ R)) = (¬P ) ∧ (¬Q ∨ ¬R)
(b) [12 pts]
¬(P ∧ (Q ∨ R)) = ¬P ∨ (¬Q ∨ ¬R)
Problem 3. [24 points]
The binary logical connectives ∧ (and), ∨ (or), and (implies) appear often in not only ⇒
computer programs, but also everyday speech. In computer chip designs, however, it is
considerably easier to construct these out of another operation, nand, which is simpler to
represent in a circuit. Here is the truth table for nand:
P Q
true true false
true false true
false true true
false false true
P nand Q
2 Problem Set 1
(a) [12 pts] For each of the following expressions, find an equivalent expression using only
nand and (not), as well as grouping parentheses to specify the order in which the operations ¬
apply. You may use A, B, and the operators any number of times.
(i) A ∧ B
(ii) A ∨ B
(iii) A B⇒
(b) [4 pts] It is actually possible to express each of the above using only nand, without
needing to use ¬. Find an equivalent expression for ( A) using only nand and grouping ¬
parentheses.
(c) [8 pts] The constants true and false themselves may be expressed using only nand.
Construct an expression using an arbitrary statement A and nand that evaluates to true re
gardless of whether A is true or false. Construct a second expression that always evaluates
to false. Do not use the constants true and false themselves in your statements.
Problem 4. [10 points] You have 12 coins and a balance scale, one of which is fake. All
the real coins weigh the same, but the fake coin weighs less than the rest. All the coins
visually appear the same, and the difference in weight is imperceptible to your senses. In at
most 3 weighings, give a strategy that detects the fake coin. (Note: the scale in this problem
is a scale with two dishes, which tips toward the side that is heavier. For clarification, do an
image search for “balance scale”).
Problem 5. [6 points] Prove the following statement by proving its contrapositive: if r is
irrational, then r1/5 is irrational. (Be sure to state the cont.
Big Data - To Explain or To Predict? Talk at U Toronto's Rotman School of Ma...Galit Shmueli
Slide from Prof. Galit Shmueli's talk at University of Toronto's Rotman School of Management, March 4, 2016. This talk is part of Rotman's Big Data Expert Speaker Series.
https://www.rotman.utoronto.ca/ProfessionalDevelopment/Events/UpcomingEvents/20160304GalitShmueli.aspx
The Indian Digital Future
Mobile marketing is a specific way of wireless marketing
Impact of mobile marketing on customer relationship management in the social distanced COVID era.
Review of Literature
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
BÀI TẬP BỔ TRỢ TIẾNG ANH GLOBAL SUCCESS LỚP 3 - CẢ NĂM (CÓ FILE NGHE VÀ ĐÁP Á...
Six days workshop on business analytics and exposure to various statistical platforms (m s excel, spss, amos, python and r)
1. ays workshop on Business Analytics and exposure to Various Statistical Platforms (M S Excel, SPSS, AMOS, Python a
Date Resource person Topics Timings
From To
3rd
August
2020
Dr. J. K.
Sachdeva (MS
Excel)
4:00 PM 7:45 PM
1. Introduction to Data analysis basics (Deductive reasoning)
2. Introduction to Central Tendency (Mean, Median, Mode, Variance, Standard Deviation, Correlation)
3. Normal Distribution, Normality and Reliability Test
4. Formal Experimental Designs
5. ANOVA (One Way & Two Way)
4th
August
2020
Dr. J. K.
Sachdeva (MS
Excel)
4:00 PM 7:45 PM
1. Testing Of Hypothesis ( Mean, Difference of means, Propositions)
2. Difference between T-test and Z-test
3. Relationship among T-stat, Z, χ2, and F
4. Test of Association
5th
August
2020
Dr. Saket
Jeswani (SPSS)
4:00 PM 7:45 PM
1. Exploratory Factor Analysis
2. Multiple Linear Regression
3. Ordinary Least Squares regression (OLS)
4. Discriminant analysis
5. Logistic Regression
6th
August
2020
Dr. Saket
Jeswani
4:00 PM 7:45 PM
1. Introduction to AMOS
2. Confirmatory Factor Analysis
3. Path Analysis
4. Structural Equation Modelling
5. Mediation & Moderation
7th
August
2020
Dr. M. M. Tripathi 4:00 PM 7:45 PM
Python Basics and Data Structuring
Data Analysis Using Regression and Decision Tree
8th
August
Dr. Mukund
M dh T i hi
4:00 PM 7:45 PM
Introduction to R