This document summarizes a student's progress in an online course on Python for data science. It shows the student received high scores on quizzes and labs for most course sections, with scores ranging from 60-100%. The student performed particularly well in sections on Python basics, lists, functions, NumPy and Matplotlib.
A Research Study on the Use of Wimba Classroomahornton
This presentation was presented at the 2009 Wimba Connect Conference in Scottsdale, AZ. The presentation includes the data collected after the pilot use of Wimba Classroom at The University of Southern Mississippi.
An alternative learning experience in transition level mathematicsDann Mallet
QUT Mathematical Sciences Seminar series, November 1 2013
Traditionally at QUT, mathematics and statistics are taught using a face-to-face lecture/tutorial model involving large lecture classes for around 1/2 to 3/4 of the time and smaller group tutorials for the remainder of the time. This is also one of the main models for teaching at other campus-based institutions. Recently, in response to (learning) technology advances and changes in the ways learners seek education, QUT has made a significant commitment to a “Digital Transformation” project across the university. In this seminar I will present a technical overview, with some demonstrations, of a pilot project that seeks to investigate how digital transformation might work in a QUT mathematics or statistics subject. In particular, I will discuss the use of tablet PC technology and specialist software to produce video learning packages. This approach has been trialled in a transition level mathematics unit this semester. I will also cover integration of these learning packages with QUTs Learning Management System “Blackboard”. This seminar is a technical preview to another talk I will give early in the new year that will look at the impact of the altered learning experience on student outcomes, feedback and the unit itself.
A Research Study on the Use of Wimba Classroomahornton
This presentation was presented at the 2009 Wimba Connect Conference in Scottsdale, AZ. The presentation includes the data collected after the pilot use of Wimba Classroom at The University of Southern Mississippi.
An alternative learning experience in transition level mathematicsDann Mallet
QUT Mathematical Sciences Seminar series, November 1 2013
Traditionally at QUT, mathematics and statistics are taught using a face-to-face lecture/tutorial model involving large lecture classes for around 1/2 to 3/4 of the time and smaller group tutorials for the remainder of the time. This is also one of the main models for teaching at other campus-based institutions. Recently, in response to (learning) technology advances and changes in the ways learners seek education, QUT has made a significant commitment to a “Digital Transformation” project across the university. In this seminar I will present a technical overview, with some demonstrations, of a pilot project that seeks to investigate how digital transformation might work in a QUT mathematics or statistics subject. In particular, I will discuss the use of tablet PC technology and specialist software to produce video learning packages. This approach has been trialled in a transition level mathematics unit this semester. I will also cover integration of these learning packages with QUTs Learning Management System “Blackboard”. This seminar is a technical preview to another talk I will give early in the new year that will look at the impact of the altered learning experience on student outcomes, feedback and the unit itself.
Text Analytics (unstructured - Twitter, Facebook posts):
Information Extraction is the problem of distilling structured information from unstructured text, for example, finding entities such as persons and organizations, and the relationships between them. Using SystemT - a state-of-the-art Information Extraction System.
Making Machine Learning Work in Practice - StampedeCon 2014StampedeCon
At StampedeCon 2014, Kilian Q. Weinberger (Washington University) presented "Making Machine Learning work in Practice."
Here, Kilian will go over common pitfalls and tricks on how to make machine learning work.
This project was initiated by a brain storming process to find out the reasons for the bunking of lectures by the students. The causes which were responsible for 80% of the class bunking were identified.
Can You Show Me That Again? Recording Lectures in BrightspaceD2L Barry
Can You Show Me That Again? Recording Lectures in Brightspace; David Leskiw, SAIT Polytechnic.
Presented on May 8, 2015 at the Brightspace Ignite forum in Calgary, Alberta.
Text Analytics (unstructured - Twitter, Facebook posts):
Information Extraction is the problem of distilling structured information from unstructured text, for example, finding entities such as persons and organizations, and the relationships between them. Using SystemT - a state-of-the-art Information Extraction System.
Making Machine Learning Work in Practice - StampedeCon 2014StampedeCon
At StampedeCon 2014, Kilian Q. Weinberger (Washington University) presented "Making Machine Learning work in Practice."
Here, Kilian will go over common pitfalls and tricks on how to make machine learning work.
This project was initiated by a brain storming process to find out the reasons for the bunking of lectures by the students. The causes which were responsible for 80% of the class bunking were identified.
Can You Show Me That Again? Recording Lectures in BrightspaceD2L Barry
Can You Show Me That Again? Recording Lectures in Brightspace; David Leskiw, SAIT Polytechnic.
Presented on May 8, 2015 at the Brightspace Ignite forum in Calgary, Alberta.
Action Research: Using Quizlet for Mobile Vocabulary Learning and Retention
DAT208x Progress _ edX
1. 6/15/2016 DAT208x Progress | edX
https://courses.edx.org/courses/coursev1:Microsoft+DAT208x+4T2016/progress 1/6
Course Progress for Student 'vijaybhojrajcm' (vijaybhojraj.cm@hotmail.com)
Start Here Welcome to the Course!
No problem scores in this section
Pre-course survey (1/1) 100%
Survey
Problem Scores: 1/1
1. Python Basics Lecture: Hello Python! (3/3) 100%
Quiz
Problem Scores: 1/1 1/1 1/1
Lab: Hello Python! (2.5/3.5) 71%
Lab
Problem Scores: 2.5/3.5
Q01
Q02
Q03
Q04
Q05
Q06
Q07
Q08
Q09
Q10
Q11
Q12
Q13
Q14
Q15
Q16
Q
Avg
L01
L02
L03
L04
L05
L06
L07
L08
L09
L10
L11
L12
L13
L14
L15
L16
LAvg
S01
S02
SAvg
Total
100%
0%
Pass 70%
87%
Microsoft: DAT208x Introduction to Python for Data Science
2. 6/15/2016 DAT208x Progress | edX
https://courses.edx.org/courses/coursev1:Microsoft+DAT208x+4T2016/progress 2/6
Lecture: Variables and Types (3/3) 100%
Quiz
Problem Scores: 1/1 1/1 1/1
Lab: Variables and Types (5.5/6) 92%
Lab
Problem Scores: 5.5/6
Further Readings
No problem scores in this section
2. List - A Data
Structure
Lecture: Python Lists (4/4) 100%
Quiz
Problem Scores: 1/1 1/1 1/1 1/1
Lab: Python Lists (2.5/3.5) 71%
Lab
Problem Scores: 2.5/3.5
Lecture: Subsetting Lists (4/4) 100%
Quiz
Problem Scores: 1/1 1/1 1/1 1/1
Lab: Subsetting Lists (4/4.5) 89%
Lab
Problem Scores: 4/4.5
Lecture: Manipulating Lists (3/3) 100%
Quiz
Problem Scores: 1/1 1/1 1/1
Lab: Manipulating Lists (2.5/3.5) 71%
Lab
Problem Scores: 2.5/3.5
Further Readings
No problem scores in this section
3. 6/15/2016 DAT208x Progress | edX
https://courses.edx.org/courses/coursev1:Microsoft+DAT208x+4T2016/progress 3/6
3. Functions and
Packages
Lecture: Functions (4/4) 100%
Quiz
Problem Scores: 1/1 1/1 1/1 1/1
Lab: Functions (1.5/2.5) 60%
Lab
Problem Scores: 1.5/2.5
Lecture: Methods (3/3) 100%
Quiz
Problem Scores: 1/1 1/1 1/1
Lab: Methods (2.7/3) 90%
Lab
Problem Scores: 2.7/3
Lecture: Packages (4/4) 100%
Quiz
Problem Scores: 1/1 1/1 1/1 1/1
Lab: Packages (2/2.5) 80%
Lab
Problem Scores: 2/2.5
Further Readings
No problem scores in this section
4. Numpy Lecture: Numpy (4/4) 100%
Quiz
Problem Scores: 1/1 1/1 1/1 1/1
Lab: Numpy (4.2/5.5) 76%
Lab
Problem Scores: 4.2/5.5
4. 6/15/2016 DAT208x Progress | edX
https://courses.edx.org/courses/coursev1:Microsoft+DAT208x+4T2016/progress 4/6
Lecture: 2D Numpy Arrays (3/3) 100%
Quiz
Problem Scores: 1/1 1/1 1/1
Lab: 2D Numpy Arrays (3/4) 75%
Lab
Problem Scores: 3/4
Lecture: Basic Statistics with Numpy (3/3) 100%
Quiz
Problem Scores: 1/1 1/1 1/1
Lab: Basic Statistics with Numpy (2/3) 67%
Lab
Problem Scores: 2/3
Further Readings
No problem scores in this section
5. Plotting with
Matplotlib
Lecture: Basic Plot with matplotlib (4/4) 100%
Quiz
Problem Scores: 1/1 1/1 1/1 1/1
Lab: Basic Plots with matplotlib (3.5/4.5) 78%
Lab
Problem Scores: 3.5/4.5
Lecture: Histograms (3/3) 100%
Quiz
Problem Scores: 1/1 1/1 1/1
Lab: Histograms (4/4) 100%
Lab
Problem Scores: 4/4
Lecture: Customization (3/3) 100%
Quiz
Problem Scores: 1/1 1/1 1/1
5. 6/15/2016 DAT208x Progress | edX
https://courses.edx.org/courses/coursev1:Microsoft+DAT208x+4T2016/progress 5/6
Problem Scores: 1/1 1/1 1/1
Lab: Customization (5.5/5.5) 100%
Lab
Problem Scores: 5.5/5.5
Further Readings
No problem scores in this section
6. Control Flow and
Pandas
Lecture: Boolean Logic and Control Flow (6/6) 100%
Quiz
Problem Scores: 1/1 1/1 1/1 1/1 1/1 1/1
Lab: Boolean Logic and Control Flow (6/7) 86%
Lab
Problem Scores: 6/7
Lecture: Pandas (4/4) 100%
Quiz
Problem Scores: 1/1 1/1 1/1 1/1
Lab: Pandas (4/5) 80%
Lab
Problem Scores: 4/5
Further Readings
No problem scores in this section
Course Wrap-up Course Wrap-up
No problem scores in this section
Post-course Survey (1/1) 100%
Survey
Problem Scores: 1/1