What Everyone Must Know About DATA SCIENCE

Every day, up to 2.5 million terabytes of data are generated by the over 6 billion (and increasing) devices connected to the internet. Millions of more gadgets are believed to be connected by 2020. It results in an estimated 30 million gigabytes of data each day. A large amount of data need to be secure so that people and businesses feel safe with their data. 


This should pique your interest as a recent graduate or an IT professional. If you’ve been following the news lately, you’re probably aware of India’s enormous layoffs in the OCR technology sector. As a result, one thing that has become increasingly important at this point is the need to reskill to something more gratifying and authoritative – Data Science.


The Data Science Lifecycle


The Data Science Lifecycle is based on five stages.

Capture: Data Acquisition, Data Entry, Signal Reception, and Data Extraction are all steps in the data capture process. This stage entails gathering unstructured and structured data in its raw form. 


Maintain: Data Warehousing, Data Cleansing, Data Staging, Data Processing, and Data Architecture are all things to keep in mind. This stage entails taking the raw data and converting it into a usable format. 


Process: Data mining, clustering/classification, data modeling, and data summarization are all steps in the process. Data scientists assess the produced data for patterns, ranges, and biases to see if it will be useful in predictive analysis.


Analyze: Data analysis approaches include exploratory/confirmatory, predictive analysis, regression, text mining, and qualitative analysis. This is when the lifespan gets really interesting. This stage entails executing a variety of data analytics because of data analysis.


Communicate: Data reporting, data visualization, business intelligence, and decision-making are all topics that should be discussed. In the final step, analysts offer their findings in easily readable formats such as charts, graphs, and reports.


Data Science Prerequisites

Before stepping into the field of data science, you should be conversant with the following technical jargon.


  1. Artificial Intelligence

The backbone of data science is machine learning. A good understanding of machine learning (ML), as well as a basic understanding of statistics, is required of data scientists. AI technology is improving every day because of the increase in technology.


  1. Creating models

Mathematical models allow you to perform quick calculations and predictions based on what you already know about the data. Modeling is a subset of Machine Learning that entails determining which algorithm is best for solving a particular problem and how to train these models.

The AI model is so sufficient that it can make many models because of its advanced technology. The number of models increasing can be important so that people can view it.


  1. Information about statistics

The foundation of data science is statistics. You can extract more intelligence and create more relevant outcomes if you have a solid understanding of statistics.


  1. Computer programming

The most extensively used programming languages are Python and R. Python is particularly popular due to its ease of use and support for a wide range of data science and machine learning libraries. Python is used because of its popular use in other different programs.  R. Python is advance technology so that it can run on many devices. 


  1. Database

A good data scientist should know how databases function, how to maintain them, and how to extract information from them.


What Does a Data Scientist Do?

A data scientist examines company data in order to extract useful information. A data scientist, to put it another way, addresses business problems by following a set of techniques that include:


The data scientist determines the problem by asking the correct questions and acquiring understanding before beginning data collecting and analysis.

After that, the data scientist selects the appropriate collection of variables and data sets.


The data scientist collects organized and unstructured data from a variety of sources, including company data, public data, and so on.

Cleaning and validating the data is necessary to ensure uniformity, completeness, and accuracy.

Where Do You Fit in Data Science?

Data science allows you to focus on and specialize in a certain part of the discipline. Here are some examples of how you can get involved in this interesting, fast-growing profession.


Data Scientist


Determine the problem, the questions that need to be answered, and where the data may be found. They also collect, clean, and present important data.


Programming skills (SAS, R, Python), storytelling and data visualization, statistical and mathematical skills, Hadoop, SQL, and Machine Learning knowledge are all required.


Data Analyst

Analysts are responsible for bridging the gap between data scientists and business analysts by organizing and evaluating data to answer the organization’s questions. They convert the technical analyses into qualitative action items.


Statistical and mathematical skills, programming skills (SAS, R, Python), and data wrangling and data visualization experience are all required.


Data engineers 

Job Role: are responsible for building, installing, clinical management systems. and optimizing the company’s data infrastructure and data pipelines. Engineers assist data scientists by assisting with data transport. Its transformation in preparation for queries.


NoSQL databases (e.g., MongoDB, Cassandra DB), programming languages like Java and Scala. The frameworks are all required (Apache Hadoop).


Tools for Data Science


The data science field is demanding, but fortunately. there are numerous tools available to assist data scientists in their work.


SAS, Jupyter, R Studio, MATLAB, Excel, and RapidMiner are some of the data analysis tools available.

Informatica/Talend, AWS Redshift for data warehousing

Jupyter, Tableau, Cognos, RAW are some of the data visualization tools available.

Spark MLib, Mahout, and Azure ML Studio are examples of machine learning tools.