5 Basic Components of Data Science

Data science consists of many algorithms, theories, components etc. Before detail study of data science, we need to understand them. Five basic components of data science are discussed here.

1. Data

Data is a collection of factual information based on numbers, words, observations, measurements which can be utilized for calculation, discussion and reasoning.

The crude dataset is the basic foundation of data science and it may be of different kinds like Structured Data (Tabular structure), Unstructured Data (pictures, recordings, messages, PDF documents and so forth.) and Semi Structured.

Structured Data

The structured data is highly organized, formatted and searchable. The machine language can easily understand the structured data. Examples are, name, address, date, etc.

RDBMS, CRM, ERP are suitable for structured data.

Unstructured Data

The unstructured data is unformatted, unorganized, cannot be processed and analyzed by utilizing conventional methods and gadgets e.g. text, audio, video, social media activity etc.

Non-relational and NoSQL databases are best for unstructured data.

2. Big Data

Big Data is enormously big data sets. It consists of various V’s such as, volume, variety, velocity, vision, value, variability & visualization, etc. For instance, Facebook.

Data is contrasted and raw petroleum which is a profitable crude material, and as scientist separate the refined oil from the unrefined petroleum comparably by applying data science, scientist can remove various types of data from crude information.

The diverse devices utilized by information researchers to process big data are Hadoop, Spark, R, Java, Pig, and many more.

3. Machine Learning

Machine Learning is the part of Data Science which enables the system to process datasets autonomously without any human interference by utilizing various algorithms to work on massive volume of data generated and extracted from numerous sources.

It makes prediction, analysis patterns and gives recommendations. Machine learning is frequently being used in fraud detection and client retention.

A social media platform i.e. Facebook is a decent example of machine learning implementation where fast and furious algorithms are used to gather the behavioral information of every user on social media and recommend them appropriate articles, multimedia files and much more according to their choice.

Machine learning is also the part of Artificial Intelligence where the requisite information is achieved after utilizing various algorithms and techniques, such as Supervised and Un-supervised Machine Learning Algorithms.

A machine learning professional must have the basic knowledge of statistics and probability, data evaluation, and technical skills of programming languages.

Types of Machine Learning

There are following three types of Machine learning:-

3.1 Supervised Machine Learning

Labeled dataset is used in supervised machine learning. Here, you must input variables (X) and output variables (Y) then you apply an appropriate algorithm to find the mapping function from input to output.

Y = f(X)

Supervised machine learning can be categorized into the following:-

Classification – where the output variable is a category like black or white, plus or minus.

Naïve Bayes, Support Vector Machine, Decision Tree are the most popular supervised machine learning algorithms.

Regression – where the output variable is a real value like weight, dollars, etc. Linear regression is used for regression problems.

3.2 Unsupervised Machine Learning

In this type of machine learning, un-labeled datasets is used. Here, you have only input variables (X) and no output variables; therefore, algorithm can be utilized to discover the inherent grouping from the input data.

Un-supervised machine learning can be categorized into the following: –

Clustering – where you find out the inherent groupings like grouping clients by procuring behavior.

K-means clustering, hierarchical clustering and density based spatial clustering are more popular clustering algorithms.

Association – where you find out rules that label large slices of your data.

Apriori algorithm is used for market basket analysis.

3.3 Reinforcement Learning

Reinforcement learning is different from supervised learning, it is about to take an appropriate action in a particular situation to maximize the reward.

In supervised learning there are input as well as output variables, so, the model is trained with the correct response but in absence of training dataset, reinforcement agent learn from its experience and perform the given job efficiently.

In reinforcement learning, input should be an initial state and there are various output due to range of solutions to a specific problem but optimum solution is decided which based on maximum reward.

Read also: 4 Types of Machine Learning

4. Statistics and Probability

Data is controlled to extricate data out of it. The numerical foundation of data science is insights and likelihood as without having a reasonable learning of measurements and likelihood, there is a high plausibility of confounding the information and achieving an off base end. That’s why Statistics and Probability assume an essential job in data science.

Further Reading

5. Programming languages (Python, R)

Generally, data organization and investigation is finished by computer programming, therefore, in data science, the two programming languages are most prominent i.e. Python and R.

5.1 Python

Python is a high-level programming language which provides a large standard library. It is most popular language as most of the data scientists love this one.

It is extensible and offer free data analysis libraries. The best features of python are dynamic type, functional, object-oriented, automatic memory management and procedural.

5.2 R

R is a most popular programming language among Data Scientists which can be used on Windows, UNIX platform and Mac Operating System.

The best feature of R language is data visualization that would be tougher in Python but it is less beginner friendly than Python.

This language is used to do social analysis with use of post data. Twitter used this language for data visualization and semantic clustering and Google use to evaluate advertisement efficiency and make economic predictions.

5.3 Java

Java is an object-oriented programming language which provides a large number of tools and libraries.

It is simple, portable, secure, platform independent, object oriented and multi-threaded, that’s why, it is suitable for data science and machine learning. Java 8 with Lambdas and Scala provide better support to data science.

5.4 NoSQL

Typically, SQL is used for handling structured data from Relational Database Management System through programming but sometime you need to handle some unstructured data with no specific schema, for which, you must need to use NoSQL. It ensure improved performance in storing a huge amount of data.

Components of data science infographic — Components of data science

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5 Basic Components of Data Science