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Ronny De Winter

Lifelong learning animal, MOOC aficionado, was there from the very beginning (Coursera student id 2564). Completed 50+ courses and also was Community Teaching Assistant for 2 of them. Favourite topics: data science, software engineering, innovation, business & finance, personal development

Ronny De Winter
Belgium
Software Engineering
Masters Degree

Completed ( 21 )

Partially Completed ( 1 )

Learning How to Learn: Powerful mental tools to help you master tough subjects

Written 2 years ago
Excellent course for the largest possible audience. Life-long-learning is a necessity for today's knowledge workers. We all (should) spend a lot of time in our life on learning. This course helps tremendously in using this learning time efficiently, whatever your learning topics are.
My rating
Ronny De Winter completed this course and found the course difficulty to be easy.

The Analytics Edge

Written 2 years ago
One of the best MOOCs I ever followed (up to now completed more than 30).

Good combination of conceptional introduction and on hands experiments.

Lots of fascinating cases worked out with R.

Takes quite some effort to do all the lectures but it is very well worth it.

A must follow for anyone who wants to become a data scientist.
My rating
Ronny De Winter completed this course, spending 10 hours a week on it and found the course difficulty to be medium.

Statistical Inference

Written 2 years ago
My rating
Ronny De Winter completed this course, spending 8 hours a week on it and found the course difficulty to be medium.

Client Needs and Software Requirements

Written 2 years ago
Excellent set of techniques to cope with software requirements, good fit with modern agile software development.

Good use of the different learning materials: videos, inline quizzes, graded quizzes (you will learn a lot by redoing them and reason about your mistakes!), assignments with peer reviews, course notes, complementary reading materials, ....

The theoretical material looks quite easy but it is not always obvious to bring it into practice. The course provides a good balance between theoretical concepts the applying them in exercises.

Recommended for everybody joining a software development organisation as product owner, scrum master, analyst, project manager, ...

This could be a good course for companies looking for training their software product managers.

My rating
Ronny De Winter completed this course, spending 3 hours a week on it and found the course difficulty to be medium.

Machine Learning Foundations: A Case Study Approach

Written 2 years ago
My rating
Ronny De Winter completed this course.

Big Data: Statistical Inference and Machine Learning

Written 2 years ago
Nice exploratory course. All levels of experience are welcome but I guess you get the most of it if you already did some introductory course before.

The coure covers some interesting tools like H2o and weka. It only touches the surface of these tools but the examples gives you a good idea of their power.

There is very good student community, with good interactions, additional references, ...

The examples are coded in R, however not necessary it is helpful if you have some background in R.

The mentioned 2 hours per week are probably not enough, I would recommend about 6 hours.
My rating
Ronny De Winter completed this course, spending 6 hours a week on it and found the course difficulty to be medium.

Developing Data Products

Written 2 years ago
Good overview of tools to use in rstudio to produce data products. Especially liked shiny: efficient tool to produce useful data products on the web.
My rating
Ronny De Winter completed this course, spending 4 hours a week on it and found the course difficulty to be medium.

Big Data: Data Visualisation

Written a year ago
This short course gives you good opportunities to explore many data visualisations techniques and tools.

Good as introduction to get a feeling on visualisations in case you don't know yet where to start. Once you decided on a technology / framework you will need more learning and practice to become proficient.

The more time you spend on the course the more you will learn, the 2 hrs / week mentioned on the course info page is the absolute minimum, please do take more time, eg to interact with other students, 4 hrs / week looks like a better figure to me.
My rating
Ronny De Winter completed this course, spending 4 hours a week on it and found the course difficulty to be easy.

Learning From Data (Introductory Machine Learning)

Written 12 months ago
Professor Yaser Abu-Mostafa created an exceptional course and provides it for free to everyone who wants to take the time and effort to dive into this excellent material. His domain and instructional skills are top of of the bill and the world should be thankful he makes this available to millions, skills which belong to the most wanted in industry today.

I am a bit of a MOOC addict having finished more than 50 MOOCs so far. This machine learning course belongs to the top3, if not the top1 course I followed. I very much appreciate it that the MOOC mirrored the in-class semester course and not watered it down to something simpler to attract more people, a regretful technique applied too much on todays MOOC platforms. The Prof's contributions on the discussion forum together with the TAs are exceptional, they help you to push through the difficult moments.

I knew upfront that this would be a tough one, 10+ hours per week combining with a heavy loaded fulltime job and family life is not obvious, but I am so glad I stayed disciplined and did it.

So be prepared, you'll need to be reasonable confortable with calculus, linear algebra, basic stats and a data science friendly programming language (python, R, matlab, ...) to achieve a good result on this course. Free up 10-20 hours weekly to focus on the course material and the exercises. If you think this is too demanding for a first encounter with machine learning, take a look at other MOOCs first, for example Andrew Ng's Machine Learning course on Coursera or The Analytics Edge course on edX, both are somewhat less demanding but evenly engaging. But be sure to come back to this course afterwards!

If a nobel prize for education would exist, Yaser Abu-Mostafa would be my number one candidate!
My rating
Ronny De Winter completed this course, spending 12 hours a week on it and found the course difficulty to be hard.

Big Data Analysis with Apache Spark

Written 12 months ago
My rating
Ronny De Winter completed this course.

Organizational Analysis

Written 11 months ago
My rating
Ronny De Winter completed this course.

Information Visualization

Written 11 months ago
Good introduction of the concepts of information visualisation and one of the best course projects I've encountered in a mooc. Only the course project alone is already worth it to take this course.
My rating
Ronny De Winter completed this course, spending 7 hours a week on it and found the course difficulty to be medium.

Mindshift: Break Through Obstacles to Learning and Discover Your Hidden Potential

Written 6 months ago
If you are here on class central in search for good online courses "Mindshift" should be on the top of your list! Highly recommended for everybody who wants to broaden their passion and enhance their career, personal life, and learning experience.
My rating
Ronny De Winter completed this course, spending 2 hours a week on it and found the course difficulty to be easy.

Learning from Data (Introductory Machine Learning course)

Written 6 months ago
Excellent caltech course which runs in parallel with the on-site university class.

Good theoretical coverage and applied programming exercises. Highly dedicated teacher and teaching assistants, closely following up the discussion forum. recommended for every serious data scientist. One of the best MOOCs I've completed. More elaborated than Andrew Ng's intro to Machine Learning.
My rating
Ronny De Winter completed this course, spending 10 hours a week on it and found the course difficulty to be hard.

Applied Plotting, Charting & Data Representation in Python

Written 5 months ago
I found in general this course too short and too superficial to become fluent with matplotlib. Module 1 provides philosophical background based on the work of Eduard Tufte and Alberto Cairo, an execellent introduction in the general practices and principles to data visualisation, independent on what tools you use (not python/matplotlib related). Modules 2 and 3 are about the matplotlib architecture, basic plotting (line chart, scatter, barchart, histogram, boxplot) and dynamic plotting (animation and interaction), areas that definitely need to dive a little bit deeper to make the concepts stick. Also module 4 on pandas and seaborn could be more elaborate.

The lectures are based on videos in combination with jupyter notebooks. I am big fan of jupyter notebooks but they combine not very well with video instruction. Too often you see the face of the instructor at times you really want to thinker about the code, sometimes the notebook cells scroll out of the video window so you miss the relation with the initial parts. These factors, together with a fast speaking instructor, needed me to often stop and rewind the video to get full understanding of the material. Not very handy.

For the exercises you will probably need more time than indicated if your main experience where the video lectures and jupyter notebooks from the course. A lot of time (80%) is needed to upfront wrangling the data. This is inline with data analysis practices, however this is a course about visualisation only, so one would expect that the focus of the exercise is more on the visualisation part. Expect to spend a lot of time on stackoverflow and the matplotlib documentation. This time could be reduced if the the course material went a bit deeper in some important areas.

As usual with Coursera courses today you cannot submit your work and get it peer reviewed, neither do peer review of other student's work, if you don't pay for a certificate.

For this topic (plotting with python/matplotlib) I would prefer some good online tutorials, for example:

- Matplotlib tutorial (Nicolas P. Rougier) https://www.labri.fr/perso/nrougier/teaching/matplotlib/

- Scientific Python lectures (J.R.Johansson) lecture 4: matplotlib http://nbviewer.jupyter.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-4-Matplotlib.ipynb

I believe these tutorials will bring you faster up to speed with matplotlib than this Coursera course.

My rating
Ronny De Winter completed this course, spending 7 hours a week on it and found the course difficulty to be medium.

Entrepreneurship for Engineers

Written 5 months ago
This entrepreneurship course provides a nice framework based on and extending the lean business model canvas. It is very well structured with good phasing for building up the business plan, the organization and the product with a focus on customer development.

Topics like identifying opportunities, customer validation, Minimal Viable Product, business modeling, finance, strategy, the ecosystem and partners for innovation are well covered, both from a conceptual point of view with short lecture videos and interview videos with entrepreneurs illustrating real-life examples.

The course transcends traditional classroom/tutorial kind of training by including good additional reading material, and exercises and assignments which culminate in creating a 2-page business plan for your startup, with optional video pitch and opportunity to enter an incubation program at one of the organizing universities.

The assessments' peer reviews can give you additional ideas, in my case, however, there were only a few comments beyond the usual grading.

I recommend this course to everybody involved in a startup, there are critical benefits to gain that will significantly reduce the risks inherently for startups. If you want to gradually answer the question "is my good idea good enough for launching a company?" then this course is for you. Do not hesitate to enroll!
My rating
Ronny De Winter completed this course, spending 4 hours a week on it and found the course difficulty to be medium.

Applied Social Network Analysis in Python

Written a month ago
Well structured course covering social network concepts, explaining the main features of networks and its nodes and edges. The algorithms are well explained, nicely illustrated and demoed with jupyter notebooks. Weekly quizzes check your understanding of the concepts and the assignments let you apply the material on practical examples, from basic network properties to link prediction using machine learning.

After finishing this course you are familiar with the python networkx library and ready to explore and analyze social networks on your own.

This is the final course of a specialization, ensure you have the necessary prerequisite skills or follow the earlier courses in the specialization first.
My rating
Ronny De Winter completed this course, spending 6 hours a week on it and found the course difficulty to be medium.

Be Visual! Sketching Basics for IT Business

Written a month ago
Good course for IT Business analysts. IT people are rational people preferring formulas over artistic drawings. However, drawings are an excellent way to improve communications between business and IT and allow for better UX design.

The course gives a lot of tips on materials and techniques, both on flipchart/whiteboard as on paper. The exercises are challenging if you are not used to drawing, but it is quite fun to come out of your comfort zone and use the other part of your brain with sketching.
My rating
Ronny De Winter completed this course, spending 3 hours a week on it and found the course difficulty to be easy.

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