I could remember writing my first “Hello World!” sometime in May, 2020☺. It was a very exciting moment of my life, as it was my very first time understanding how codes work. The study resource used then was “python tutorials for absolute beginners”. The course flow begun with variables in python, and then, gradual progress was made to data types, functions, the conditional statements (if, else & elif), loops (for & while), lists, index slicing, tuples, sets, dictionaries, datetime module, web scrapping, and zip functions. …

It’s been quite a very long time I gave an update to my study path😐 I’ve been doing a whole lot more study than penning down my experiences. It’s been a wholesome journey altogether.

Anyways, I’d be doing a brief review on Statistics (basically what you need to know as a Data Scientist) in this blog post.

Statistics generally entails the collection, organization, analysis, and interpretation of data. The data (in question) could either be population data or sample data. Sample data is just a subset of the population data. Whilst dealing with population data (denoted by N), you use…

Woww! It’s been quite an interesting learning path as a beginner in Data Science. In my previous blogpost, a brief note was made on probability, combinatorics and Bayes’ theorem. The focus here would be strictly on probability distribution.

Probability distribution shows the collection of all possible values a variable can take and how frequently they occur.

Two (2) vital characteristics of a probability distribution are:

- Mean- the average value of a collection of variables.
- Variance- the measure of dispersion of a certain dataset; how spread out a data is.

Based on the type of data we have, probability distribution is…

**Probability**

Our everyday life comes with uncertainties; an event could be expected or not. For instance, when someone sits for an examination, the person either succeeds (passes) or fails. The attempt in sitting for the examination, in this instance, is a **trial** (or experiment), while the result of such attempt is the **outcome. **Similarly, in a crate of eggs, there are chances that we could take out both rotten eggs or the good ones when doing a random selection.

The basis of probability is that expectations may likely occur or not, due to happenings (events) and their results.

Therefore, probability…

Hello, my Medium fellows!😊 It’s my first work here, and I’d be making some updates on my journey thus far into Data Science.

Getting into Tech from a non-Tech (or even a Mathematics or Statistical) background could be quite difficult, especially when there isn't any proper exposure.

The best way to dive in would have to be undoubtedly via an Introductory course. I would be taking a course on Udemy in conjunction with 365 DataScience (the complete data science bootcamp).

**General Overview of Data Science**

Data science is just basically all about storytelling (visualizing data) and making sense of numbers.

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