Although I am not a data scientist by any means, I was recently asked what knowledge of math someone would need to be able to efficiently get into the field.

Well, I researched a little and came up with the following course track, starting with the basics.

🧵👇
Calculus 1 will cover a lot of the basics, relevant especially to optimization problems which are quite common in data science.

khanacademy.org/math/calculus-1
Calculus 2 will go even deeper and cover topics such as integration techniques and differential equations.

Especially integrals are pretty important for probability distributions and hypothesis testing!

khanacademy.org/math/calculus-2
More integrals!

You'll find many topics from Calculus 2 here, but use the chance to repeat integrals, as they are pretty important for data science.

khanacademy.org/math/integral-…
Linear Algebra covers vectors, matrices, and such.

It is important if you want to get into recommendation systems or even deep learning, later.

If you don't want to get into those topics, yet, view this one as optional!

khanacademy.org/math/linear-al…
Statistics and probability is basically your bread and butter.

This is a lot of what data science is about, so it should, of course, be included here!

khanacademy.org/math/statistic…
And that's it!

You can cover the basic math by simply using Khan Academy, and I really like their curriculum for how good it is.

There are, of course, other sources out there on the internet, but having only one platform to work with, might help. 😊

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More from @oliverjumpertz

21 Nov
DIP - The Dependency Inversion Principle

The Dependency Inversion Principle is a part of SOLID, a mnemonic acronym which bundles a total of 5 design principles.

It is often associated with clean code.

But what exactly is it, is it important to you, should you even care?

🧵👇
1⃣ What does it state?

It states:

Modules that encapsulate high-level policy should not depend upon modules that implement details. Rather, both kinds of modules should depend upon abstractions.

This may sound a little complicated, but you can break it up, as follows:
1. High-level modules should not depend on low-level modules. Both should depend on abstractions (e.g., interfaces).

2. Abstractions should not depend on details. Details (concrete implementations) should depend on abstractions.
Read 17 tweets
20 Nov
ISP - The Interface Segregation Principle

The Interface Segregation Principle is part of SOLID, a mnemonic acronym which bundles a total of 5 design principles.

It is often associated with clean code.

But what exactly is it, is it important to you, should you even care?

🧵👇
1⃣ What does it state?

The ISP sets a pretty simple statement:

"No client should be forced to depend on methods it does not use."

ISP applied to the extreme will result in single method interfaces, also called role interfaces.
You can take a look at the following example to get an idea of how the ISP can be violated in one way (this time it's in TypeScript): interface ReportService {  ...
Read 14 tweets
14 Nov
Clean code is important, clean code helps others understand your code, but clean code is also pretty subjective!

I want to give you my perspective on it, drawing from years of experience leading teams of developers as a tech lead and working in teams.

🧵
1️⃣ Clean code is important

Clean code helps people understand code.

The more your code is structured according to the opinion of the majority of developers, the more likely it is that other developers will understand your code!
A few principles of clean code are:

- Use meaningful and understandable identifiers
- Use functions and keep them as short as possible
- You don’t need comments most of the time when your code is human readable
- Don’t repeat yourself (DRY)
- Keep it Simple, Stupid! (KISS)
Read 18 tweets
27 Oct
Have you ever had to process events?

If you have, have you ever thought about if the order of events really matters?

There is a huge difference in complexity whether the order of events processed matters or not.
Imagine the following task/problem:

You want to analyze application logs, because you're curious how an API that starts a long running background process behaves performance-wise.

One log message signals the start, a few intermediate ones, and one message that signals the end.
1⃣
If you can somehow ensure that every log message you process comes in order, the problem is manageable.

When you get your end message, you know that you already processed the start message.

You could now, for example, just subtract the end time from the start time.
Read 8 tweets
14 Aug
Interviewing for a new position is never easy.

Algorithms & Data Structures are usually a pain for candidates, but so can System Design be.

Let me give you 7 steps that will help you to improve your experience in system design interviews.

🧵👇
1) Clarify The Requirements

Ask questions, and a lot of them!

Try to find out what exactly the interviewer expects from you.

Try to narrow it down as far as possible to the exact scope of the problem.

Even if the question is to (re)design an existing system, ask!
2) Define System Interfaces

Define all APIs that the system to design will need.

Explain what each API is for as detailed as possible, so the interviewer can jump in and tell you if you got a requirement wrong.

If you got something wrong, no problem, adjust accordingly.
Read 8 tweets
13 Aug
Improving developer experience is always a good investment.

It's even one of the investments with the highest return, in my opinion!

Some things that help to improve DX:

🧵👇
1) Providing All Relevant Information Up Front

Ensure that all information necessary is present in a README.md within the project's repository.

It should be sufficient enough to state what the project does, what problems it tries to solve, etc.
2) Making Sure Starting Out Is Easy

Does the project require some setup to be able to run/test/develop locally? -> Write an interactive script which sets everything up, maybe creating a config file / .env with meaningful defaults or settings from script input.
Read 8 tweets

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