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By Harshita  |  Data science and Analytics  |  On 1/29/2019 8:12:47 PM

Learning Mathematics For Data Science

Maths is known to be the basis of data science. Everytechnique of modern data science required deep mathematical understanding. Butonly knowing mathematics is not enough, other features like business acumen,programming ability, and unique inquisitive and analytical mindset are alsorequired for a data scientist in order to know how the data would function.Data Science and mathematics go hand in hand. Without an understanding of mathsit is very difficult for a data scientist to function.

 

The knowledge of maths is crucial particularly fornewcomers in the field of data science as compared to people in otherprofessions like chemical process industry, business management, hardwareengineer, healthcare and medicine, retail, etc. Mths required in data science canbe different in spite of the fact that such fields require experience withnumerical calculations, spreadsheets, and projections. 

 

By its nature, data science is not particularly tiedto a specific subject area which is why it may deal with phenomena which may beas diverse as social behavior analysis and cancer diagnosis. This nature ofdata science enables the possibility of dizzying array statisticaldistribution, n-dimensional mathematical objects, optimization objectivefunction, etc.

 

Though the concepts of mathematics are very importantto know in the field of data science, mere know-how won't do, proper trainingand knowledge are needed. To master the art of understanding these skills it isimportant to get training under institutes like Tech data Solutions thatprovides data science training in Mumbai and datascience training in Pune. The institute helps the newcomers in learning newconcepts which helps them in empowering in order to hear the hidden music inthe machine learning and daily data analysis projects, which is actually agreat step towards becoming a top data scientist.

 

The topics to be studied in maths for the field ofdata science are:

 

Statistics

 

It is very crucial to have a solid grasp of theessential concepts of probability and statistics. In this field of datascience, many practitioners consider classical machine learning i.e. non-neuralwork to be statistical learning.

 

The subject is vast so there are some concepts thatare required to be covered:

•    BasicProbability: expectation, conditional probability, the basic idea, Bayes’Theorem, probability calculus.

•    ProbabilityDistribution Function: Binomial, uniform, chi-square, normal, central limittheorem, student’s distribution.

•    Centraltendency, descriptive statistics and data summaries, covariance, correlation,variance,

•   Regularization and linear regression

•   Measurement, sampling, random number generation, error.

•    T-test,ANOVA

•    Confidenceintervals, A/B testing, Hypothesis testing, p-values

It can be used in interviews, where one can show howvery well they have mastered these concepts and impress the panel immediately.Also, this is something which is required to be used every day by a datascientist.

 

Calculus

 

Whether one loves it or hates it, calculus issomething that is required in various ways in machine learning and datascience. It basically hides behind the simplest looking analytical solution ofthe most ordinary square problems in the linear regression. It is the mostvaluable skills which need to be added to the collection

 

Topics to learn in Calculus are:

•    Beta andgamma functions

•    Minima andmaxima

•    Product andchain rule

•    Limit,continuity, the function of multiple and single variables, partial derivatives

•    Mean valuetheorems, L’Hospital’s rules, indeterminate forms

•    Basics ofPartial and ordinary differential equations

•    Infinitiesseries summation, Taylor’s series

Concepts of calculus like derivatives, limits,gradient and chain rule can be used to understand how logistic algorithm isimplemented. One of the most common optimization techniques where it is used isgradient descent.

 

LinearAlgebra

 

To get an understanding as to how machine learningtends to work on a stream of data in order to create insight, linear algebra isvery essential. From song recommendation in Spotify, or transferring selfieusing deep transfer learning to a friend suggestion on Facebook involves theuse of matrix algebra and matrices. 

Essential topics to learn in linear algebra are:

•    Basis span,vector space, orthonormality, orthogonality, linear least square

•    Matrixmultiplication rule, matrix inverse, inner and outer products.

•    Basicproperties of vector and matrix: transpose, rank, linear transformation,determinant, scalar multiplication.

•    Specialmatrices: identity matrix, symmetri matrix, unitary matrices, square matrix,dense matri, unit vectors, triangular matrix, etc.

•   Eigenvectors, singular value decomposition, Eigenvalues,diaganolization.

Linear algebra techniques are used by all neuralnetwork algorithms in order to process and represent the learning operationsand network structures.

 

DiscreteMaths

 

All modern data science is done with computationalsystems where discrete math is considered to be the heart of such acomputational system. It is very important for a fresher in discrete maths tostudy these concepts in order to use data structures and algorithms daily inanalytics project:

•   Combinatorics, countability, counting functions

•   Propositional logic, basics of deductive and inductive

•    Sets, powersets, subsets

•    Basic datastructures: graphs, stacks, array, queue, trees, hash tables

•    Basic ofproof techniques: proof by contradiction, induction.

•    Recurrenceequations and relation

•    Graphproperties: degree, components, max/min cut concepts, connected components,graph coloring

•    Notationconcepts, the growth of functions

It is very crucial to understand and know theproperties of fast algorithm and graphs in any social network analysis in orderto traverse and search the network. In every choice of algorithm, it is verymuch needed to know the space and time complexity.

 

Variables,Functions, Equations, and Graphs

 

This area tends to cover mostly the basics of mathsi.e. from an equation of a line to binomial concepts like:

•   Inequalities, sums, series

•    Basicproperties, real and complex problems

•   Trigonometric identities, basic theorems, and geometry

•    Cartesianand polar coordinates, graphics and plotting, conic sections

•   Exponential, polynomial functions, logarithm, rational numbers.

Binary search is something which helps in theunderstanding the concept as to how a particular search runs faster on amillion-item database once it is sorted. Recurrence equations and logarithms isrequired in order to understand the dynamics of it. Concepts like exponentialdecay and periodic functions are required for the concept of time series.

ForSASTraining in Pune & Mumbai, TechData Solutions offers classes.We understand the use of machine learning is become the part of evolution ofevery invention hence offer machinelearning training in Mumbai & pune as well. You can get to know moreabout us and our courses on our website.