Maths is the backbone for data science . Actually maths is a broader term . Therefore If we filter out maths word , there are few things where you need to brush up as data science learner . Probability , statistics , linear algebra etc are most required but there are so many other things apart from them which are also relevant to data science . In this article I will be more specific to the topics inside them . The only thing which I will recommend you if you are really interested to learn Maths Essential for Data Science is to bookmark this article and finish them . in short this article is a road map for Maths Essential for Data Science .
Maths Essential for Data Science : Topics Overview
1.Linear Algebra –
As You already know most of the data science operations are performed in Matrixes . Under Liner algebra , You should at least know –
- Basics Matrix and vectors operations
- singular value decomposition (SVD) , Eigen Value and Eigen vector etc
- triangular matrix , identity matrix, square matrix and other special type of matrix .
Where to read Linear Algebra for Data Science-
2 . Calculus-
Calculus has wide application area in data science . Specially in deep learning and neural network it is must to have skills .Lets brief the the topic under the calculus umbrella which you should learn first –
- Concept of Maxima and minima.
- Concept behind – Functions of single variable, limit, continuity .
- Concepts of ordinary and partial differential equations.
As we all know , Few of us really like calculus but most do not . In order to realize its importance lets understand with Gradient descent .Gradient Descent is one of the elementary concept of Machine Learning . Do you know its completely on the top of gradient, derivatives etc.In order to understand this completely you must know the calculus basics .
Where to read Calculus for Data Science-
I do not think , I need any more explanation on data science and statistics relation and importance . As statistics is one the most important area , So I will suggest you to go throw the below article for topic reference and reading material –
Where to read statistics for Data Science-
As you know , Probability is also equally important as statistics .We have also covered Probability in a separate article . Get the complete overview for probability topics overview and relevant tutorials reference here –
Where to read probability for Data Science-
Most of the data science projects usually start with Proof of Concepts Right ? Discrete Mathematics is full of such theorems and methods which we use to proof some thing . Amazing thing which we ignore usually that most of the data structure concepts are built on discrete mathematics . Whether it is graphs , stack , queue or some others etc. So Discrete Mathematics is important from developer and data scientist both point of views right . Here are some important topics which are really important in context of data science-
- Concepts of Basic Proof Techniques like – induction, proof by contradiction etc .
- Concepts of Basic data structures- stacks, queues, graphs, arrays, hash tables, trees etc .
- Concept of Growth of functions
Where to read Discrete Mathematics for Data Science-
Optimization and operation research –
Under this umbrella you should know the below topics –
- Concepts of Randomized optimization techniques — hill climbing, Genetic algorithms etc .
- Constraint programming ,Linear programming
- problem formulation for optimization .
Where to read Optimization and operation research for Data Science-
To sum up ,I have tried to simplify this topic in easy words for you. Still its an two directional work if you think we can add some thing related to Maths Essential for Data Science : Topics Overview or simplify some thing which is explained already , comment below or send us an email . Most of us are already aware to these maths concept from the school days . Actually the difference between the level of studies is very clear . In school days we mainly focus on solving the maths problem .Moreover in data science , Now we have to frame real problem into data science problem followed by their solution using maths concepts .
Data Science Learner Team
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