As the world enters the era of big data, the need for its storage capacity has also increased. This was the main challenge and concern for enterprise industries until 2010. The main focus was on building frameworks and solutions to store data.
Now that Hadoop and other frameworks have successfully solved the storage problem, the focus has shifted to the processing of this data. Data science works in a secret way here. Whatever ideas you see in Hollywood sci-fi movies can actually be turned into reality by data science. Data Science is the future of Artificial Intelligence. Therefore, it is very important to understand what data science is and how it can add value to a business.
By the end of this blog, you will be able to understand what is data science and its role in drawing meaningful insights from the complex and large sets of data around us.
What is Data Science ?
Data science is a study that deals with the identification, representation and data science extraction of meaningful information from data sources used for business purposes.
With the sheer volume of facts being generated every minute, there is a need to extract useful insights so that the business can stand out from the crowd. Data engineers set up databases and data storage to facilitate data mining, data munging, and other processes. Every other organization is chasing profits, but companies that devise efficient strategies based on fresh and useful insights always win the game in the long run.
The data scientist skill set includes statistics, analytical, programming skills and an equal measure of business acumen. Most data scientists have a strong background in mathematics or other domains of science and a PhD is a distinct possibility. Without the role of a data scientist, the value of big data cannot be harnessed. So in today’s data-driven world there is a huge demand for data scientists who transform data into valuable business insights. Knowledge of data basics of data science is quite useful in today’s data driven world of science.
Data Science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data science practitioners apply machine learning algorithms to numbers, text, images, video, audio, and more to produce artificial intelligence (AI) systems to perform tasks that typically require human intelligence. Huh. In turn, these systems generate insights that analysts and business users can translate into tangible business value.
Broadly, data science can be defined as the study of data, where it comes from, what it represents, and the ways in which it is converted into valuable inputs and resources for creating business and IT strategies. can go.
Why We Need Data Science ?
Let’s understand why we need Data Science
Traditionally, the data we had was mostly structured and small in size, which could be analyzed using simple BI tools. Unlike the data in traditional systems that was mostly structured, most of the data today is unstructured or semi-structured.
This data is generated from a variety of sources such as financial instruments, text files, multimedia forms, sensors and instruments. Simple BI tools are not capable of processing this huge amount and variety of data. This is why we need more complex and advanced analytical tools and algorithms for processing, analyzing and drawing meaningful insights about it.
This is not the only reason why data science has become so popular. Let us dig deeper and see how Data Science is being used in various domains.
What if you could understand the exact needs of your customers from past data such as browsing history, purchase history, age and income. No doubt you had all this data before, but now with vast amounts of data and data, you can train models more effectively and recommend products to your customers with greater accuracy. Wouldn’t this be amazing as it would bring more business to your organization?
Let us take a different scenario to understand the role of data science in decision making. How about if your car has intelligence to take you home? Self-driving cars collect live data from sensors, including radar, cameras and lasers, to map their surroundings. Based on this data, it decides when to speed up and when to slow down, when to move forward, when to take a turn etc. For this, they use advanced machine learning algorithms.
Let us see how data science can be used in predictive analytics. Let’s take weather forecast as an example. Data from ships, aircraft, radar, satellites can be collected and analyzed to build models. These models will not only forecast the weather but will also help in predicting the occurrence of any natural calamity. This will help you take appropriate measures in advance and save many precious lives.
Now that you have understood the need of Data Science, let us understand what is Data Science.
How does data science work?
Data Science encompasses a plethora of disciplines and specialization areas to produce a holistic, complete and sophisticated look at raw data. Data scientists must be proficient in everything from data engineering, mathematics, statistics, advanced computing and visualization to effectively sort through tangled chunks of information and communicate only the most important bits that drive innovation and efficiency. will help you run.
What does a Data Scientist do?
In the past decade, data scientists have become essential assets and are present in almost all organizations. These professionals are data-driven individuals with high-level technical skills, capable of creating complex quantitative algorithms to organize and synthesize large amounts of information used to answer questions and drive strategy in their organization. This is coupled with the communication and leadership experience required to deliver tangible results to the various stakeholders in an organization or business.
Data scientists need to be curious and results-oriented with exceptional industry-specific knowledge and communication skills that allow them to interpret highly technical results to their non-technical counterparts. He has a strong quantitative background in statistics and linear algebra as well as programming knowledge, focused in data warehousing, mining, and modeling for the construction and analysis of algorithms.
What is the use of data science?
Data Science helps us to achieve some major goals which were either not possible or required a lot of time and energy a few years back, such as:
- Anomaly detection (fraud, disease, crime, etc.)
- Automation and decision making (background checks, creditworthiness, etc.)
- Classification (in email servers, this can mean classifying emails as “important” or “junk”)
- Forecast (Sales, Revenue and Customer Retention)
- Pattern detection (weather patterns, financial market patterns, etc.)
- Recognition (face, voice, text, etc.)
- Recommendations (based on learning preferences, recommendation engines can tell you about movies, restaurants, and books you might like)
Comparing Data Science with Data Analysis:
Data scientist and data analyst are different in the sense that data scientist starts by asking right questions, data analyst starts with data mining. Data scientist requires considerable expertise and non-technical skills whereas data analyst does not require these skills.
Data Science is a multidisciplinary science and having a data science career means that you need to gain genuine expertise in multiple domains like data inference, working with algorithms, among other skills. Data science applications can span across multiple industries.
The job of a data scientist is to prepare oneself to understand complex behaviors, trends, inference, analytical creativity, time series analysis, segmentation analysis, contingency models, quantitative reasoning, and more.
“The data scientist is better at statistics than any software engineer and better at software engineering than any statistician.”
There is no clear definition of exactly what the roles and responsibilities of a data scientist include. This can include anything from optimizing the sales funnel to finding the right strategy for the company to enter the next lucrative international market. So it’s a bit difficult to try to define the job of a data scientist in a simple way. There can be a lot of ambiguity about this.