What is Data Science: Basic Introduction and Its Application

What is Data Science

Data-Science

Data science is the field of study that consolidates area mastery, programming aptitudes, and information on arithmetic and measurements to remove significant experiences from data. Data science specialists apply AI calculations to numbers, text, pictures, video, sound, and more to deliver artificial intelligence (AI) frameworks to perform undertakings that usually require human knowledge. Thus, these frameworks create experiences in which examiners and business clients can convert into substantial business esteem.
Even though data science is certifiably not another calling, it has developed extensively in the course of the most recent 50 years. A stumble into the historical backdrop of data science uncovers a long and winding way that started as ahead of schedule as 1962 when mathematician John W. Tukey anticipated the impact of current electronic processing on data investigation as an exact science. However, the data science of today is a long way from the one that Tukey envisioned. Tukey's forecasts happened a long time before the blast of huge data and the capacity to perform perplexing and enormous scope examinations. All things considered, it wasn't until 1964 that the primary PC {Programma 101} was divulged to people in general at the New York World's Fair. Any analyses that occurred were more simple than the ones that are conceivable today.
By 1981, IBM had delivered its first PC. Mac wasn't a long way behind, delivering the primary PC with a graphical UI in 1983. Consistently, processing appeared to develop at a lot quicker pace, enabling organizations to gather data all the more without any problem. Notwithstanding, it would be about twenty years before they would begin to change over that data into data and information.

Why do we need Data Science

Until relatively recently data was structured and small in size. It was able to be analyzed either manually or with the use of simple tools and algorithms. Today thanks to technological developments we are increasingly producing more and more data. This is often semi-structured, or completely unstructured.

For example, it is estimated that over 80% of enterprise data is unstructured. This is only going to increase. To help us make sense of this growing mass of unstructured data we need more complicated analytical tools and sophisticated algorithms. Data science is the process of using these advanced tools to make sense of large amounts of unstructured data. As the amount of unstructured data, we produce increases, data science only grows in importance.

Steps Followed In Data Science

1. Gathering data-The initial phase in the process is to accumulate data. It tends to be organized, unstructured, or semi-organized.

2. Data munging-Once you have your data, it's an ideal opportunity to chip away at it. The 'crude' data is cleaned and changed into a reasonable configuration to receive the most incentive in return. This is likely the longest advance. Data researchers report data cleaning is about 80% of the time spent overall cycle.

3. Analyzing data-After cleaning the data right now is an ideal opportunity to examine it by applying calculations and vital measurable models.

4. Data visualization-When a lot of data are to be managed, assembling visualizations or diagrams is the most ideal approach to investigate and impart results.

5. Producing predictions-Machine learning calculations help you getting experiences and foreseeing future patterns. Something other than making predictions, this progression can assist you with building new items and cycles.

6. Reiterate Insights help to grow more highlights to consistently improve model yields and convey convenient execution and exact outcomes.

Also, Check Out Data Mining

Applications of Data Science

Application-of-Data-Science

As it is clear at this point, Data Science is a broad term, and so are its applications. Almost every application on your smartphone thrives on data. Along these lines, it's not out of the question to express that it's essentially difficult to list down all the applications of data science in view of its sheer ubiquity.

Let’s have a look at the broad fields that are using the magic of Data Science:

1. Internet Search
How googles return such *accurate* search results within a fraction of a second? Data Science!

2. Recommendation Systems
From “people you may know” on Facebook or LinkedIn to “people who’ve bought this product also liked…” on Amazon to your daily curated playlists on Spotify to even “suggested videos” on YouTube, everything is fueled by Data Science.

3. Image/Speech/Character Recognition
This pretty much goes without saying. What do you think is the intelligence behind “Siri”, if not Data Science? Likewise, how would you think Facebook recognizes your friend when you upload a photo with them? It’s not magic; it’s science – Data Science.

4. Gaming
EA Sports, Sony, Nintendo, Zynga, and other giants in this domain have taken it upon themselves to take your gaming experience to another level. So that they can upgrade as you move up to higher levels nowadays Games are developed and improved using Machine Learning algorithms so that they can upgrade as you move up to higher levels.

5. Price Comparison Websites
These websites are fueled by data. For them, the more the merrier. The data is fetched from the relevant websites using APIs. PriceGrabber, PriceRunner, Junglee, Shopzilla are some such websites.

Advantages of Data science

The field of knowledge Science has many benefits. Following are a number of the pros of knowledge Science:

➣Data Science is Fun
Data Science is a rare field that provides you the opportunities to work with many things together like mathematics, coding, research, analysis, etc. So, if you're keen on doing all this it can be an extremely fun job for you that will never be boring. But the only catch is, being a growing field requires some hard work, learning as well as unlearning because in this field anytime the simplest solution to a problem will become just a good one.

➣Multiple Job Designations
Being in demand, it's given rise to a large number of career opportunities in its various fields. Some of them are Data Scientist, Data Analyst, Research Analyst, Business Analyst, Analytics Manager, Big Data Engineer, etc.

➣Ease Job Hunting
There is an urgent need for Data Scientists in the market, as there's a substantial gap between the demand and the skills of Data Scientist. The LinkedIn report shows that the most promising job in America in 2019 is Data Scientist, it's one of the fastest-growing jobs published in Dec 2019, the average annual growth rate of the job of a Data Scientist since 2015 is 37%, and the top industries hiring for this role are Information Technology and services, financial services, internet, and computer software. one of the reports from IBM says that the demand for Data Scientists will soar by 28% by 2020.

➣Customize the Products
Data Science helps organizations to customize their products by understanding the user requirements more efficiently to personalize the user experience. Also, the organizations can improve their sales and increase the revenue because Data Science helps the organizations in estimating that when and where their products sell best.

➣A Highly Paid Career
As Data Scientist continues being the sexiest job, the salaries for this position are also grand. According to a Dice Salary Survey, the annual average salary of a Data Scientist $106,000 per annum.

➣Cost Optimization
The cost to the companies is optimized very efficiently using Data Science. It can also help to extend individual productivity and resource utilization.

➣AI is the Future
While talking about technology, the world is moving at a good speed. With the help of AI, we try to make the machine as smart as humans. AI makes use of Data Science to figure out the solutions to complex problems by extracting insights from the data. some of the advances of AI which may have a good impact on the future are the automation of transportation (e.g self-driving cars), Robots playing an important role in many hazardous jobs.

Disadvantages of Data science

Everything that comes with many advantages also has some drawbacks. So let’s have a look at some of the disadvantages of Data Science:

➣Data Security
Data is the core component that can increase the productivity and the revenue of industry by making game-changing business decisions. But the information or the insights obtained from the data is misused against any organization or a group of people or any committee etc.

➣Complexity
The various tools and techniques are used for Data Science can sometimes cost a lot to an organization as some of the tools are very complex and require expert knowledge or training to use them. Also, it's very difficult to select the correct tools according to the circumstances because their selection is based on the proper knowledge of the tools as well as their accuracy in analyzing the data and extracting information.

➣Term is Misleading
By the name Data Scientist, everyone will generally think of someone scientifically doing things with the data, but that's not an actual case. Data Science is actually apart from a business than Science. The term Data Science can also include Data Analysis, Data preparation, Data Management, etc. The term Data Scientist can be better understood by “Statistical inference”, that is, drawing conclusions from data with the assistance of statistics.

➣Doesn't Allow to Expertize
The field of Data Science uses many various skills to handle the data and to form data-driven decisions for an organization. A Data Scientist must have knowledge of various skills like programming, machine learning, statistics, business strategies, etc but the Data Science won't allow them to go in-depth of any individual field.

Faqs about Data Science vs Machine Learning and Artificial Intelligence

Are Machine Learning and Data Science the same?
Machine Learning and Data Science are not the same. They are two different domains of technology that work on two different aspects of businesses around the world. While Machine Learning focuses on enabling machines to self-learn and execute any task, Data science focuses on using data to help businesses analyze and understand trends. However, that’s not to say that there isn’t any overlap between the two domains. Both Machine Learning and Data Science depend on each other for various kinds of applications as data is indispensable and ML technologies are fast becoming an integral part of most industries. 

Which is better, Machine Learning or Data Science?
To begin with, one cannot compare the two domains to decide which is better – precisely because they are two different branches of studies. It is like comparing science and arts. However, no one can deny the obvious popularity of data science today. Almost all the industries have taken recourse to data to arrive at more robust business decisions. Data has become an integral part of businesses, whether it is for analyzing performance or device data-powered strategies or applications. Machine Learning, on the other hand, is still an evolving branch that is yet to be adopted by a few industries which only goes on to say that ML technologies will have more demand relevance shortly. So, professionals of both these domains will be in equal demand in the future. 

Is Data Science required for Machine Learning?
Since both Machine Learning and Data Science are closely connected, a basic knowledge of each is required to specialize in either of the two domains. Having said that, more than data science the knowledge of data analysis is required to get started with Machine Learning. Learning programming languages like R, Python, and Java are required to understand and clean data to use it for creating ML algorithms. Most Machine Learning courses include tutorials on these programming languages and basic data analysis and data science concepts. 

Who earns more, Data Scientist or Machine Learning Engineer?
Both Data Scientists and Machine Learning Engineers are quite in-demand roles in the market today. If you consider the entry-level jobs, then data scientists seem to earn more than Machine Learning engineers. An average data science salary for entry-level roles is more than 6 LPA, whereas, for Machine Learning engineers, it is around 5 LPA. However, when it comes to senior experts, professionals from both domains earn equally well, averaging around 20 LPA.

What is the Future of Data Science?
Putting it slightly differently – Data Science is the future. No businesses or industries for that matter will be able to keep up without data science. A large number of transitions have already happened worldwide where businesses are seeking more data-driven decisions, more is to follow suit. Data science quite rightly has been dubbed as the oil of the 21st century which can mean endless possibilities across industries. So, if you are keen on pursuing this path, your efforts will be highly rewarded with not just a fulfilling career and fat pay cheques but also a lot of job security.

Can a Data Scientist become a Machine Learning Engineer?
Yes, Data Scientists can become Machine Learning. It will not be very difficult for data scientists to transition to a Machine Learning career since they would have anyway worked closely on Data Science technologies that are frequently used in Machine Learning. Machine Learning languages, libraries, and more are often used in data science applications as well. So data science professionals do not need to put in a humongous amount of effort to make this transition. So yes, with the right kind of upskilling course, data scientists can become machine learning engineers.

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