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**govarthenan****Member**- Registered: 2022-01-26
- Posts: 1

Hi I'm a tech enthusiast in his early 20s who didn't take Biology in high school due to certain unavoidable reasons. Now, luckily, I've been selected for a BSs in Information Systems course from a major university. It will start in a few months. And oh, I want to enter the field of data science, with all things considered.

I already know some data science-related coding, and have been an intern at an entry-level startup and did some AI-related stuff and data cleaning. However, I can already feel my lack of math knowledge affecting my self-studies, as I read tutorials and come across math notations regarding equations that describe a machine-learning algorithm, etc. For example, I struggled with the following equation for calculating the sum of squared residuals in linear regression. SSR = Σᵢ(?ᵢ - ?(?ᵢ))².

So now that you get an idea of how disconnected I've been from math in the past 3 - 4 years, please point me to the topics in MathIsFun I should familiarize myself with. And also, please recommend good books considering the level of knowledge I have now.

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**Jai Ganesh****Administrator**- Registered: 2005-06-28
- Posts: 48,340

It appears to me that if one wants to make progress in mathematics, one should study the masters and not the pupils. - Niels Henrik Abel.

Nothing is better than reading and gaining more and more knowledge - Stephen William Hawking.

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**Bob****Administrator**- Registered: 2010-06-20
- Posts: 10,610

hi govarthenan

Welcome to the forum.

Tell me more about "sum of squared residuals in linear regression".

I used to teach this, back in the stone age, so maybe I can help.

Bob

Children are not defined by school ...........The Fonz

You cannot teach a man anything; you can only help him find it within himself..........Galileo Galilei

Sometimes I deliberately make mistakes, just to test you! …………….Bob

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**pamshaw****Member**- Registered: 2021-12-07
- Posts: 21

If you are looking forward to taking admission in data science, you must have to be good in math. And if you are not good at math this will be a bumpy ride. Because every single algorithm is created with math functions. Usually, in Data science most of the math is based on statistics but not completely. In data science three topics that come up consistently are:

Statistics

Calculus

Linear algebra

Statistics is also divided into two branches which are inferential and descriptive. Statistics is used in a large number to develop new algorithms and applications. This also helps to create a summary image of an industry’s process flow.

Calculus is the math branch that studies the changes and optimizes the result at the end. If you don’t have knowledge of calculus it will be difficult to find better outcomes and fix the issue.

Last but not least is linear algebra. To deal with a problem it provides fast speed. Also helps to understand the different algorithms. It can be accessed in Python using the NumPy library. With the combination with Calculus, it helps us decision-making in vectors and matrices.

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