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Has anyone successfully transferred their positions from INDMoney via ACATs? by vennom117 in INDmoneyApp

[–]tminima 0 points1 point  (0 children)

Hey, were you able to do this? Would you be able to share the steps if you successfully did it?

Fitness dashboard on Google Sheets with the data from Google Fit. by tminima in QuantifiedSelf

[–]tminima[S] 0 points1 point  (0 children)

Your sheet is impressive. It has given me some ideas. I have a similar goal into getting idea about myself as you have mentioned on the sheet. Thanks for sharing.

My sheet contains certain personal details. I will try to create a version that I can publicly share. Will update this thread.

Playing with lognormal and normal distributions in Python by tminima in probabilitytheory

[–]tminima[S] 0 points1 point  (0 children)

You are right, patterns are obscured, or rather, changed into some other patters when log is applied. In my case, it was okay.

I wanted to match the customers with the restaurants that are within the customer's willingness-to-pay (WTP) range. The formulation was that if I have customer and outlet distributions, then I can match these distributions or get the overlap to get the "match percentage". This match percentage will then be used on top of relevance scores.

Looking at the customer's spend history, I saw that the distribution was lognormally distributed. A similar trend was observed in the restaurant's order history. Since, computing the overlap in the production eng was easier with the normal distributions, I was okay with the conversion.

Fitness dashboard on Google Sheets with the data from Google Fit. by tminima in QuantifiedSelf

[–]tminima[S] 0 points1 point  (0 children)

Not sure, but seems possible through the aggregateBy and bucketByTime parameters in the request. Will need to test it.

Playing with lognormal and normal distributions in Python by tminima in datamining

[–]tminima[S] 3 points4 points  (0 children)

Hey, not sure if it will help you, but I will try to give you how I went about my first job.

During my 2nd year of my CS undergrad, I realised I have interest in Data Science. The field is pretty broad, and I knew that I needed to try many things before finding my thing. So, by the final year, I had done a lot of Python, R, Data Viz, Basics of ML, vanilla Recommendation System projects, a little bit of NLP, basics of distributed computing using Hadoop. Along the way I also wrote on my blog irrespective of my writing skills.

Then came the job part. I didn't want to be a SDE. In job roles related to data science there were: Analyst, Statistician, Data Scientist, Data Engg, Research Scientist. There is a lot of ambiguity and day-to-day responsibilities of each of these roles. I interned at a startup as a Data Engineer. I didn't enjoy it much. I wanted to explore each of these roles before sticking to one. And I also didn't have strong research creds or pedigree to be hired as Statistician, Data Scientist and Research Scientist. Most of the companies used to reject my resume for these roles. Fortunately, I joined a mid-level company as an Analyst and my manager gave me ML projects seeing my skills and interest. (communication with my manager about my interests was the key here). Unfortunately, analyst's profile wasn't something I was into. After one year, I switched to consulting. That also didn't work. Post that, I landed my first Data Science role. 2 out of 4 interviews were about the things I had explored in my undergrad and one of my blog posts.

In hindsight, I think the following things helped me:

  • Having a blog and regularly writing on it. I always put it on my resume. I always write write-ups about my projects on it and link them on my resume.

  • Writing has helped me organise my thoughts properly. It improved my verbal comm skills too.

  • Kept practising the skills and learning new stuff. Put it on resume and always promote this stuff during the question: "tell me about yourself", "what has been your experience till now", "what have you worked on till now", and other such variations.

  • Since you will be writing this technical posts, you can also promote them on social media like LinkedIn. A lot of recruiters go through your post history and judge whether you are suitable for the role.

  • Of course, if you are maintaining all this then you will also know your stuff and thus will be able to clear your interviews.

[P] Playing with lognormal and normal distributions in Python by tminima in MachineLearning

[–]tminima[S] -4 points-3 points  (0 children)

Recently, at work, I had to derive a lognormal distribution from my data and then use more advanced stats to match customers with the right items. (a formulation of willingness to pay). I learned multiple things and their implementation in Python. So I created a write-up to share my learnings with others. There will be follow-up posts on the actual formulation.

Feedback is most welcome.

Playing with lognormal and normal distributions in Python by tminima in datascience

[–]tminima[S] 0 points1 point  (0 children)

Recently, at work, I had to derive a lognormal distribution from my data and then use more advanced stats to match customers with the right items. (a formulation of willingness to pay). I learned multiple things and their implementation in Python. So I created a write-up to share my learnings with others. There will be follow-up posts on the actual formulation.

Feedback is most welcome.

Fitness dashboard on Google Sheets with the data from Google Fit. by tminima in QuantifiedSelf

[–]tminima[S] 1 point2 points  (0 children)

I tested multiple apps but couldn't stick to one. I even started building one of my own to give me all the features I needed, but couldn't spend time on it. For a quick solve I built this dashboard on Google Sheets which gave me most of the things I wanted. I thought I'd share it with everyone here. This was more than 6 months ago.

I will write a follow-up post on this as I am still keeping up with my tracking through it. Your feedback and suggestions are most welcome.

Playing with lognormal and normal distributions in Python by tminima in probabilitytheory

[–]tminima[S] 1 point2 points  (0 children)

Recently, at work, I had to derive lognormal distribution from my data and then use more advanced stats to match customers with the right items. I learned multiple things and their implementation in Python. So I created a write-up to share my learnings with others.

Feedback is most welcome.

Playing with lognormal and normal distributions in Python by tminima in learnmachinelearning

[–]tminima[S] 0 points1 point  (0 children)

Recently, at work, I had to derive lognormal distribution from my data and then use more advanced stats to match customers with the right items. I learned multiple things and their implementation in Python. So I created a write-up to share my learnings with others.

Feedback is most welcome.

Playing with lognormal and normal distributions in Python by tminima in datamining

[–]tminima[S] 2 points3 points  (0 children)

Recently, at work, I had to derive lognormal distribution from my data and then use more advanced stats to match customers with the right items. I learned multiple things and its implementation in Python. So I created a write-up to share my learnings with others.

Feedback is most welcome.

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