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What is Data Science? A Beginner's Guide to Data Science

What is Data Science? A Beginner's Guide to Data Science

Data Science
Data Science

Let us know about Data Science :


As the world is entering in the era of big Data collection, the needs for its Storage capacity has also increased. This was the main challenge and concern for Enterprise Industries in 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 secretly here. Whatever ideas you see in Hollywood sci-fi movies can actually be transformed 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.

Let's understand why we need Data Science?

Need of Data Science
Need of Data Science

Traditionally, the Data we had was mostly Structured and small in size, which could be analyzed using simple BI tools. Unlike Data in traditional systems that were mostly Structured, most Data today is unstructured or semi-structured.



This Data is generated from various sources such as financial sources, text files, multimedia forms, sensors and devices. 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.



Why Data Science has become so popular, This is not the only reason. Let us understand in depth and see how Data Science is being used in various domains.



What will happen if you can understand your customers, exact needs from their past Data like browsing history, purchase history, age and income. There is no doubt that you had all this Data before, but now with the huge volume and Data, you can trained the model more effectively and can recommend the product with more accuracy to your customers. Wouldn't it be amazing because 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 the intelligence to take you home? Self-driving cars collect live Data from sensors including radars, cameras, and lasers to map their surroundings. Based on this Data, it decides when the speed should be fast and when the speed should decrease, when to proceed, and when to turn. For this, they use Advanced Machine Learning Algorithms.



Let us see how Data Science can be used in Predictive Analytics. Let's take the weather forecast as an example. Data from ships, aircraft, radars, satellites can be collected and analyzed to make models. These models will not only forecast the weather but will also help in predicting the occurrence of any natural disaster. This will help you take appropriate measures beforehand and save many precious lives.



Now that you have understood the need for Data Science, understand what Data Science is?


What is data science?

What is Data Science
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 enormous amount of fact that arises every minute, there is a need to extract useful insights to make the business 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 lagging behind 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 equal measurement of business skills. Most Data Scientists have a strong background in mathematics or other domains of science and have a distinct possibility of PHD. Without the role of a Data Scientist, the value of big Data cannot be used. So in today's Data-driven world, there is a huge demand for Data Scientists who turn Data into valuable business insights. Knowledge of Data basics of Data Science is quite useful in today's Data driven world of science.


Comparing Data Science with Data Analysis:


Data Science Future
Data Science Future

Data Scientists and Data Analysts are different in the sense that the Data Scientist starts by asking the right questions, the Data Analyst starts with Data Mining. A  Data Scientist requires considerable expertise and non-technical skills where as a Data Analyst does not require these skills.


Data Science is a multidisciplinary science and having a Data Science career means that you need to acquire multiple domains such as Data estimation, working with algorithms, real expertise among other skills. it can spread across many industries from Data science application.


The job of a Data Scientist is to prepare oneself to understand complex behavior, trends, inference, analytical creativity, time series analysis, segmentation analysis, contingency models, quantitative reasoning, and more.


"A Data Scientist is better at Statistics than any Software Engineer and better at Software Engineering than any Statistics."


There is no clear definition of what exactly is involved in the roles and responsibilities of a Data Scientist. This could include anything from optimizing sales funnels to getting the right strategy for the company to enter the next Lucrative international market. So it is a little difficult to try to define the Data Scientist's work in a simple way. There can be a lot of ambiguity about this.


How to become a data scientist?


So you have taken the plunge. Do you want to be a data scientist?

  • Education

Data Science : Education
Data Science : Education

Data Scientists are highly educated - 88% have at least a master's degree and 46% have a PhD - and there are some notable exceptions, but typically require a very strong academic background to perform. To become a Data Scientists, you can earn a graduate degree in computer science, social science, physical science, and statistics. The most common fields of study are Mathematics and Statistics (32%), followed by Computer Science (19%) and Engineering (16%). A degree in any of these courses will give you the skills needed to process and analyze big Data.


The truth is, most Data Scientists have a master's degree or PhD and also take online training to learn a particular skill such as how to use Hadoop or Big Data querying. Therefore, you can enroll for a master's degree program in the field of Data.


  • R Programming


Data Science : R programming
Data Science : R programming

You must have in-depth knowledge of at least one of these analytical tools, which R is generally preferred for Data Science. R is specifically designed for Data Science requirements. You can use R to solve any problem in Data Science. To solve statistical problems 43 percent of Data Scientists are using R . However, R has a steep learning curve. Especially if you have already mastered a programming language then it is difficult to learn. Nevertheless, there are very good resources on the internet to get started in R, such as Simple Learn Data Science Training with R Programming Language.

  • Python Coding

Data Science : Python Coding
Data Science : Python Coding

Python is the most common coding language commonly seen as essential in Data Science roles with Java, Perl, or C / C ++. For Data Scientists Python is a great programming language . This is why 40 percent of respondents surveyed by 'average Reilly' use Python as their main programming language. Because due to its versatility, you can use Python for almost all the steps involved in Data Science processes. It can take various formats of Data and you can easily import SQL tables into your code. This allows you to create a data set and you can literally find any type of data set on Google.

  • Hadoop Platform

Data Science : Hadoop Platform
Data Science : Hadoop Platform

Although it is not always required, it is preferred in many cases. Experience with Hive or Pig can also beneficial for you. If you are familiar with cloud tools such as Amazon S3 can also be beneficial. In a study conducted by Crowd Flower, 3490 LinkedIn Data Science Jobs ranked Apache Hadoop as the second most important skill for a Data Scientist with a 49% rating.



As a Data Scientist, you may face a situation where the amount of your Data exceeds your system memory or you need to send Data to different servers, this is where Hadoop comes in. You can use Hadoop to quickly move Data to different points on the system.


  • SQL (structured query language) Database / Coding

Data Science : SQL Language
Data Science : SQL Language

Large component of Data Science is  NoSQL and Hadoop, it is expected that a candidate will be able to write and execute complex queries in SQL. SQL (structured query language) is a programming language that can help you perform operations such as editing, deleting and extracting data from a Database. It can also help you perform analytical tasks and change Database Structures.

As a Data Scientist you need to be proficient in SQL. This is because SQL is specifically designed to help you access, communicate, and work with Data. When you query a database it gives you insight. It has brief commands that can help you save time and reduce the amount of programming required to perform difficult queries. Learning SQL will help you better understand relational databases and boost your profile as a Data Scientist.

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