How to generate random numbers in Python using NumPy’s updated method

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If you do an online search about how to generate random numbers in Python NumPy, most probably you will see this kind of code:

GitHub gist by Author

Sometime ago NumPy had updated its method of generating random numbers but almost all of the search results are littered with…


How I failed to learn math for data science and then what I did to understand Linear Algebra, Probability, Bayes’ Theorem, Probability Density Function, and basic Statistics

Accident ferroviaire de la gare Montparnasse, Source : Wikimedia

Background

That is exactly how my data science journey looked like after a year.

And yeah, Happy New Year :-)

When it comes to learning math for data science, everything starts and ends with failure. I am sure I am not alone, and it is the same story of many of those who started in data science. If you want one word to describe my efforts to learn math for data science, it is:


Key points from Pie & AI’s webinar on Building career in Data Science

A few hours ago I attended a YouTube live webinar organized by deeplearning.ai featuring Ayodele Odubela as the speaker.

This post contains the key points I learned from the webinar. In just under an hour Ayodele (eye-ya-deli) explained the entire anatomy of building a data science career. This was one of the best webinars on data science I have attended so far. It is a must-watch if you are trying to get into data science. Since there is no consensus on who a data scientist is, in this blog post whenever I mention data science, I mean data analysis, machine…


How to re-define Learning, Failures, and Motivation to find more fulfillment in life

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These definitions are what life has taught me. I have not read these in a book or watched in a video. These are my personal experiences. And I used to think the opposite earlier. For learning, I used to learn because I just wanted to learn. For doing something hard, I used to use push motivation. And for failure, I was always big a fan of getting myself a corner to cry, sob and scream on myself.

I have stopped doing that. In the last eight and a half years, I have completely changed the meanings of these and it…


My key takeaways from a deeplearning.ai webinar

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Recently I attended Pie & AI’s webinar. For those who don’t know, Pie & AI is deeplearning.ai’s meetup series that typically includes conversations with leaders in the AI industry. It is spearheaded by the most well known AI face, Andrew Ng. They use “AI” as a keyword to refer to machine learning, deep learning, and data science.

This week I attended their webinar on The Full-Stack Data Scientist: Myth, Unicorn, or New Norm? All of the speakers had different backgrounds and experiences. Hence each gave a different idea about the same field.

Disclaimer: This is my interpretation of what I…


What obstacles I faced and currently facing while learning data science and how did I overcome them

Eric Weber (yes, that nice-looking guy with a lovely dog) wrote a post on LinkedIn recently about 10 things he wished he had done less when he started his data science career. This post is my journey through those 10 points. First, you should go ahead and read his post. Here is a screenshot:

Original Post on LinkedIn by Eric Weber

First things first, this is not going to be a “content” post.

There are so many articles and blog-posts on that already, so check them out. …


Opinion

Why you should not neglect software design and development principles in data science and machine learning

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UPDATE, Feb 26, 2021: I will keep on adding links, at the bottom of the post, to prove my point that this blog-post might look like an opinion piece but it is not. That software design is a fact when it comes to data science in production.

Background

Everyone talks about the need of R, Python, Statistics, data analysis, and machine learning as the primary skills to seek employment as a data scientist. I have taken many introductory and advanced data science and machine learning courses. …


My experience with the course and the book from “fastai”

Image by Ryan McGuire from Pixabay

The Background

I started Practical Deep Learning for Coders 10 days ago. I am compelled to say their pragmatic approach is exactly what I needed.

I started data science by learning Python, Pandas, NumPy, and whatever I needed in a short few months. I did whatever courses I need to do (e.g. Kaggle micro-courses) and whatever books I needed to read (e.g. Python for Data Analysis). All of this I did as a part of a 90-day MOOC’athlon learning challenge started back in April this year. It was one of the greatest learning periods of my life. After this, I completed both…


An honest review of his book ‘Predictive Analytics’

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I finished reading Eric Siegel’s Predictive Analytics. And I have to say it was an awesome read. How do I define an awesome or great book? A book that changes your attitude permanently. You must not be the same person that you were before you picked up the book. It impacts one or more aspects of your life: personal, financial, social, romantic, family, or professional. Also, I read a book only if I can use what I learn from it. I don’t read it just for the sake of learning something. It needs to be practically usable in one of…


Photo by Jon Tyson on Unsplash

The Background

Two months ago I wrote about my plan on how I am going to utilize this Coronavirus pandemic lock-down. The gist is I wanted to do a 90-day MOOC’athlon learning challenge inspired by Rassul-Ishame Kalfane and Scott H. Young. Here is the list of things I wanted to accomplish (You can read the entire plan in detail here.):

  1. Read and understand Python Language Reference, Python Library Reference, Python tutorial, Functional Programming Style, all from the official docs.
  2. SQL Intro and familiarity with basic command at https://sqlbolt.com/
  3. Learn NumPy and its internals. Learn SciPy Ecosystem
  4. Work through Python for Data Analysis

Arnuld On Data

Industrial Software Developer turned Data Scientist. From C to Python. Linkedin: https://www.linkedin.com/in/arnuld-on-data/

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