Introduction and a New Convection About Data Science

Introduction

Today Data Science has become a huge topic and also stands at the top of the list of jobs in the market. People learning it and doing it as well as demand has grown to peak stage. What is it that is making this job so desirable and wanted by many people and how has this got to this stage of huge demand.

The companies have come up with many new things also the data has increased enormously that people like to know what is it and one man or a group can’t do it. It requires a lot of work and crunching all the data to get insights from it has become the ultimate goal of the companies so that they can grow and bring many innovative products. All the companies, industries, fields, researchers everyone is trying to get things out of the data so that it brings a new era, new transformations, and developments in the respective domains.

Introduction to Data Science and related technologies involved in it

Data Science is the science of dealing with all the data growing in every field, industry, and domain. It is the process of extracting the data from structured, unstructured data using the Data mining technique (for getting the information from raw data). It is a lot of science involving getting the necessary information out from tons and millions of tons of products, services, and their data in it for making better products, developments, and many more. Data is never-ending and the same is for data science technologies and related things.

What are the technologies used in it? What is data science made up of?

The above picture gives the basic subjects to be known in data science. Then what are new technologies to be learned and get acquainted with into the broader picture of data science? The below picture gives how data science is dealing with new technologies to get the information from the data. It excites to be part of new technology and also can see a big mountain in front to cross and go to next stage.

Along with all these technologies a lit bit of domain knowledge/business knowledge would help in making better insights into the data. There is nothing impossible, it is the courage that counts. Small things make big differences.

Now let’s get into ML, What is Machine Learning?

Machine Learning is the method of dealing with data and automating the tasks by training it so that it gives new suggestions and detects when a similar type of data is provided to it. It comes under the AI umbrella and it identifies the data and makes the decisions faster and saves time and human effort so that there is less need for human intervention in it.

Did anyone get a question about automation and machine learning?

RPA vs ML

Artificial intelligence and RPA are different. RPA is a software robot that mimics human actions, whereas AI is the simulation of human intelligence by machines. RPA is a rule-based software that has no intelligence and automates repetitive tasks. RPA just does what task is assigned to it and decreases the time taken by humans whereas AI brings new things and evolves and yes RPA is also part of AI then it is not used in Data Science.

The machine learns about data and RPA just performs the repetitive tasks. Many people might have come across the bots and would have known what do they do. They just do the task given to them and still, RPA developers are helping data scientists make their work easier. Machine learning gives new insights from the data trained to it and in RPA it is not like that.

Machine Learning its Types and Use of it

Machine Learning is classified into Supervised, Unsupervised, Semi-supervised, and Reinforcement Learning. The first two play a major role in the data science industry and also the others come under Data Science. Supervised Algorithms deals with the labeled data, unsupervised with unlabeled data, Semi-supervised combines where both types of data exist (labeled and unlabelled), Reinforcement Learning or algorithms is like trial and error method where when the task is done the correct way it gets rewarded else it gets penalized or punished.

Supervised Learning is further divided into Classification and Regression problems, Unsupervised is divided into Clustering and Dimensionality Reduction problems. Some of the algorithms used in Supervised are Linear Regression, Logistic Regression, KNN, Decision Tree, Random Forest, SVM, Boosting techniques, etc. Some of them used in Clustering are Kmeans, Market Basket Analysis, DBSCAN(Density-based spatial clustering of applications with noise), Hierarchical clustering, etc and Dimensionality Reduction can be done using PCA, SVD, LDA, t-SNE…etc.

The below diagram describes the use and benefit of applying these algorithms to the data and also explains which type of algorithm or technique has to be used while solving the problem based on its requirements.

Deep Learning

Deep Learning is the extension of ML also sometimes called advanced ML algorithms. Still is there is a lot of difference between ML. Deep Learning works based on neural networks as the human brain does. There are a lot of networks in Deep Learning. Some of the DL algorithms are CNN, RNN, LSTM’s, GAN, RBFN, MLP, SOM’s, DBNs, RBMs, Autoencoders and Decoders, etc.

NLP(Natural Language Processing) is mainly used in text extraction and understanding it.

All these technologies combine ML, DL, NLP comes under AI (Artificial Intelligence) umbrella.

Computer Programing Languages used for Data Science

Many programming languages can be used for Data Science. The most popular data science languages used nowadays are Python and R. Even though all the other programming languages can be used for data science. Python has become the most popular and data science language because of the in-built libraries it has for data science.

Data Science has many topics and it has many things which can be applied everywhere and so this is making a huge impact everywhere and making the data easier to understand and use so that there are new developments and directly proportional to the growth of the country.

This is about the short description and introduction on Data Science and its need in this big data world which is evolving every day without any pause. Even in times of pandemic the Data science analysis and its use have increased a lot which has made things easy for storing and making analysis on the numbers(for counts, infected people, recoveries, deaths, and all ..). Data Science is such an important part because of which we can to predict and take necessary actions. Thanks to Data Science, data scientists, and everyone who is being a part of eradicating the virus from our country and world.

Let me know if you have any queries or anything to say about the article. Thanks for reading.👩‍🔬👩‍💻👩

Have a nice day. 🕊