Data Science Methodology (IBM)

Compleated another certificate realted to data Science. This was was step by step explainaiton of the whole procedure of a Data Science project. It showed the pattern as a whole instead of goind too much deep into how each operation is being done. In the discussion coloum of the IBM portal some students found it way too eazy, whereas the idea of working with so much data and numbers on a excell sheet in future overwelmed me a bit.

So finally I overcame my fears and compleated this bit and here is the link to flaunt on another certificate from IBM

https://coursera.org/share/728d9ea3de111e743bf30d7be0dd2050

Yes I also love the badging system by IBM and I have three digital version collected till now.

Link: https://www.credly.com/badges/ac38f9af-f9ac-459c-a909-3052efd041f6/public_url

Or the whole collection at https://www.credly.com/users/tapish-dongre/badges

Hope I can add more in the future months.

Year 2021 is looking promising. See you in the next post or next life….

Cheers

Tapish

Tools for Data Science

In this article, I am just documenting my journey of learning Data Science. Currently, I have completed Course 2 of the IBM Data Science Professional Certificate titled “Tools for Data Science”

I have currently two digital badges from IBM to flaunt on this Digital Universe

The link to the Badges is:

  1. Data Science Orientation

https://www.credly.com/badges/82b8daa6-9d94-4ea5-a7ed-1dcd78accc91

2. Tools for Data Science

https://www.credly.com/badges/e8119c02-2b2f-4fdd-852e-bab85cc64bfa

Now Coming to the main part of this article. The final takeaway of this course was the final assignment learned in week 4 of the course where we executed the learning of different Markdowns (i.e Fonts / Caligraphy in Coding). This has complied in a Jyupter Notebook made inside Waston Studio provided by IBM. I am sharing a link to my work as this would be the first-ever task I managed to pulled of in my Data Science journey. (I know I know in the future I will laugh that I shared a barely minimum sill level task but it is just the start of a big Marathon thus I thought the smaller achievements are worth a share)

The link to my work is :

https://eu-gb.dataplatform.cloud.ibm.com/analytics/notebooks/v2/86e5b12c-d95a-4038-9873-35835cafb4d8/view?access_token=486577407cc406a91d70ef53ec197f2ba50cb70e79295bbd588de37513a605c0

Hey I can also share my Completion certificate now, the link to my Certificate is :

https://coursera.org/share/27abd5146bfe79343b0fe0e678b289b8

Hopefully, more Certificates, as well as projects, would come in the future to flaunt my skills thus do sign up / follow /Subscribe or bookmark this website to get further updates posted as and when I learn new cool stuff.

Thanks

Tapish

Jupyter Notebooks on the Internet

Copy of Original Article by “Romeo Kienzler” in IBM Data Science Course

There are thousands of interesting jupyter notebooks available on the internet for you to learn from. One of the best sources is: https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks

It is important to notice that you can download such notebooks to your local computer or import them to a cloud based notebook tool so that you can rerun, modify and follow along what’s explained in the notebook.

Very often jupyter notebook are already shared in a rendered view. This means, that you can look at them as if they were running locally on you machine. But sometimes, folks only share a link to the jupyter file (which you can make out by the *.ipynb extention). In this case you can just grab the URL to that file and past it to the NB-Viewer => https://nbviewer.jupyter.org/

The list above gives you a very nice start with a huge collection of materials to explore. Therefore it’s maybe more useful to give you some pointers to interesting notebooks. As we have covered some toy examples with toy data in the labs, let me just point to some work which uses these data and goes further down the road of data science. In addition, as we’ve covered the different tasks in data science we’ll also provide an exemplar notebook for each of those.

First you start with exploratory data analysis, so this notebook is highly recommended to have a look at: https://nbviewer.jupyter.org/github/Tanu-N-Prabhu/Python/blob/master/Exploratory_data_Analysis.ipynb

For data integration / cleansing at a smaller scale, the python library pandas is often used. Please have a look at this notebook: https://towardsdatascience.com/data-cleaning-with-python-using-pandas-library-c6f4a68ea8eb

If you want to already experience what clustering is, have a look at this: https://nbviewer.jupyter.org/github/temporaer/tutorial_ml_gkbionics/blob/master/2%20-%20KMeans.ipynb

And finally, if you want to go for a more in-depth notebook on the iris dataset have a look here: https://www.kaggle.com/lalitharajesh/iris-dataset-exploratory-data-analysis

“I have just kept a copy of this page on my website as a personal note that this infomation is useful and I should read the individual links in my free time.” – Tapish Dongre

Creative Markdown Cheatsheet

While practicing work on Jupyter Notebooks there are many fancy ways to write a code markdown. Markdown are like Headings, Titles and Subtitles in a code which are not executed in while we run the programme. These Markdowns are only there to guide the reader of what the code is about or explain the code step by step or add hint or comments.

There is a cheat sheat to guide us through the variety of Markdown styles. I will provide the link to original post so that the credit goes to the content creator rather than just copy pasting the styles in this web page.

This link to the Markdown Sheet is: https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet

Hope to share more info about Data Science as and when I learn about it.

Regards

Tapish

Skills Vs Challenge

World used the television series How I Met Your Mother as an Comedy entertainment option to relax after a stressful day. But back in India, I used this as something a simulating model of how the behaviourial pattern would be if/when I would get a chance to visit or live in some other country. Back then it was a platform to learn about other cultures. Fast forward 10 years down the line, I actually got a chance to study and live in Australia. Though the series dose not exactly mimic actual life living in land outside India, but it was like a starter/refreshment course for us to actually understand a little bit of western culture, their problems, way of thinking and what can or cannot be done in a day to day life.

In the same series a character named “Barney Stinson” played by Neil Patrick Harris used a graph called “Hot vs Crazy” to define or explalin in general that a person can tollerate as much as crazyness of girl/woman as much Hot she is. Definatly I am not trying to justify any objectification of any gender as the graph was used only for a comic purpose which in their fictional world made some sense. Comming back to reality, I found another graph which relate to Challenge vs Skills origally observed in “Finding Flow by Mihaly Csikszentmihalyi”

Finding Flow by Mihaly Csikszentmihalyi

This Graph I can actually realte to while I am studing Data Science or Working on any Project.

For a simple task like washing utensils, the skil level is low, and the challege is low, so I sometimes get into wonderland in my own mind.

Where as Studing Diffrential Calculus where I have less skill and being a highly challenging task for me gives me Anxiey and I try to quit or find excuses to postponed the task.

Current target is not directly aim for flow, but to at least be in the Relaxation or Control zone while gaining more knowledge.

Please do wrte in comment section below what state of mind you are while currenlty doing any task in your current life stage. Hopefully we can share strategies to overcome our hurdles with collective information and experience.

Best regards

Tapish

Gallery of Interesting Jupyter Notebooks

While studing IBM Data Science, which I voluntary signed up for I found this intresting link of all the self learning stuff we can do at Juniper Notebooks tutorials.

Thus thought to paste the link here in case I forget to add it to my github account.

(Self note: Learn how to delete a repository on github)

So the link is

https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks#introductory-tutorials

Hope to find more resources in future and share with the world.

Thanks

Regards

Tapish

I know I F**ked UP : Journey to Data Science or Any Other Distance Learning Pathway

I know I fucked up, so bad, but there is always a possibility to recover from it.

Recently seen a lot of drop in the followers of this blog, reason being I am trying my best to avoid my presence on the web. Avoiding the un-nessary chaos that I created with putting my ideaologies on the web and clashing with someone having the opposite of the thinking curve. These conversations results in uselss time waste. Thus I really stop posting. So now with limited followers, I can use this website/blog as a personal Journal instead of the mass communication targetted. (Personal Journal open to access by all….thats sounds cool idea at this moment of time, *may bite me on my ass in future, but presently it sounds so COOL)

But what I really did with the so much saved up time?

Binge Watch NETFLIX

Scatterd Watching Youtueb Video after Youtube Video.

Seriously I keep thinking What I am doing with my life at the peak age of 31 years.

I joined up edX platform for a Audit Course on Data Scinece in the month of Nov 2020.

Four months down the line, the distance learning without a supervison is way difficult that actually going and attending a class in University or College.

I know this post my thoughts are disorganised and all over the place. But I feel….currelty no one is reading it so I am free to type my mind out.

So the difficulties I face in Distance Learing are:

  1. Lack of Motivation: You dont really feel like to study. The concept of Delayed Gratificaiton is not present in everyone by deafult, so you tend to reap up the present fruit instead of sticking to your study routine.
  2. Having So Much on Your Plate: You think you will Binge Study all the subjects like giving 8 hours per day time to study instead of the 4 hour per week time as suggested by the cource coordinator. But in reality neither you spend the recommed 4 hours nor the ideal 8 hour time toward your study
  3. Distractions Distractions Distractions: If you house, palce of study or the workplace is not designed to make you focus on your study, you get distracted on doing other day to day household duties as they seem importat right at that moment. (When they are really not)
  4. Procastination: The energy required to build the momentum to study at home is never self born. Untill you force yourself to study, you never even start.
  5. Self Evaluation: Your self worth at the time of your study seems so low that you loose hope to ever be on the market using the education you are studing. This is pure negative thinking, I am aware, but lacking the patience to put effor in study, make the false demons seems real.
  6. Ideal Study Day : There is none. You think of the day that you will feel like studing, but it never arrived. Every day is Ideal day if we make it like that. Without dedicating at least a small amount of time toward your study, you will never do it.
  7. Ergonomics: Sounds fantcy word, but really it dose affect. If you dont have a ergonomics sitting place to work or study, you end up leaving that work/study place more often due to the restles postion you are in.
  8. Social Support: It sounds absurd, but when you discuss what you are doing with your peers and if they dissaprove of your activities, even that hampers your study. I know the devil is in your own thinking and thus the key to fight the devil must also be present inside your mind. But the social acceptence is a key feature while performing any task. (Recommeded Reading : IKIGAI)
  9. Resources for Learning. I know this factor is applied to some and not applied to others. For exaple there are still countries where electricity and internet are availiable only in limited quantities/time to making it difficult to opt distance learning as their choise of study pathway. Other thing from personal experince is the funding to put on distance learning. My free/scholarship pathway to study Data Science on EdX expires on 24th March 2021 and currently and very unfortunatley I dont have the $1000+ required to fund that professional certificate. I have to make my way to reach out to that funding complete that course. Other way round, if you have too much resources in the online age, even that hampers in a negative way of self doubt are you on the right track or not.
  10. _______________________________________ I will leave this 10th point blank as you might fill this youself. My list can keep continuing as I am a bit pessimistic thinker. But all the above point seems genuine to me at the time of writing this article.

Since now you have the main points why we dont feel like to study. There must be solutions to every problem. As Dr. Anand Raj says, “Stick some glue on your seat and start studing”. More on that on next post.

Please Like/Share/Subscribe to give me Social Dopamine Boost ha ha

Cheers

Tapish Dongre

R resources (Recommended by Dr Rafael A Irizarry on EdX)

Online R resources:

Maths for Machine Learning

I came across lot of learing material since last month I am exploring the scope and structure to learn Data Science. One of the structure presented was on top my head due to the humour of the presenter used on his presentation. (Wow so many p words in one sentence).

So the video link to this is presentation is

The particular slide I love is at 7min 26 sec. Image Attached Below:

The Course Link mentioned in this video is:

MIT LINEAR ALGEBRA

https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/

MIT CALCULUS

https://ocw.mit.edu/courses/mathematics/18-01sc-single-variable-calculus-fall-2010/

https://ocw.mit.edu/courses/mathematics/18-02sc-multivariable-calculus-fall-2010/

MIT STATS & PROBABILITY

https://ocw.mit.edu/courses/mathematics/18-05-introduction-to-probability-and-statistics-spring-2014/

These are the top Google Search results of year 2021, there might be more uploaded in the future years that can be searched on the MIT website. Other way round is signing up for an edx course.

See you in next post or next life, whichever comes earlier.

Wish Best wishes for the Year 2021

Tapish Dongre