What is Data Science?
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 here in a secret way. Any 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.
In this blog, I will cover the following topics.
The need for data science.
What is Data Science?
By the end of this blog, you will understand what data science is and its role in drawing meaningful insights from the complex and large sets of data around us.
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, which 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 the 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 disaster. This will help you take appropriate measures in advance and save many precious lives.
Now that you understand the need for Data Science, let us understand 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 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 skillset includes statistical, 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 turn data into valuable business insights. Knowledge of the data basics of data science is very useful in today’s data-driven world of science.
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 real expertise in multiple domains like data inference, working with algorithms, among other skills. Data Science applications can spread in many industry industries.
The job of a data scientist is to prepare oneself to understand complex behaviour, trends, inference, analytical creativity, time series analysis, segmentation analysis, contingency models, quantitative reasoning, and more.
“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.
How To Become A Data Scientist In English:
So you’ve taken the plunge. You want to be a data scientist. But where to go? There are many resources here. How do you set the starting point? Did you not pay attention to the subjects you studied? What are the best resources for learning?
Try to get an undergraduate, graduate or certificate in data science or a closely related field.
Broadly speaking, you have 3 education options if you are considering a career as a Data Scientist degree.
Degree and graduate certificates provide recognized academic qualifications for structure, internship, networking and your resume. For this, you will have to invest a significant amount of time and money.
Improve your skills in statistics, data mining and data analysis.
Must-Have Skills You Need To Become A Data Scientist:
To become a Data Scientist you must possess the following skills:
Data scientists are highly educated – 88% have at least a master’s degree and 46% have PhDs – and there are some notable exceptions, but they usually require a very strong educational background to develop.
To become a data scientist, you can earn graduate degrees in computer science, social science, physical science, and statistics. The most common areas of study are mathematics and statistics (32%), followed by computer science (19%) and engineering (16%). A degree in any of these courses will provide you with the necessary skills 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 specialized skill, such as how to use Hadoop or Big Data querying. Therefore, you can enrol for a master’s degree program in the field of data.
2) R Programming
You should have an in-depth knowledge of at least one of these analytical tools, which R for data science is generally preferred. R is specially designed for data science needs.
You can use R to solve any problem in data science. In fact, 43 per cent of data scientists are using R to solve statistical problems. However, R has a steep learning curve. It is difficult to learn especially if you have already mastered a programming language. Nevertheless, there are many good resources on the internet for getting started in R with R programming language like Data Science Training of Simply Learn.
3) Python Coding
Python is the most common coding language commonly seen needed in data science roles, along with Java, Perl, or C/C++. Python is a great programming language for data scientists. This is why 40 per cent of respondents surveyed by O’Reilly use Python as their major programming language. because
Because of its versatility, you can use Python for almost all the steps involved in data science processes. It can take different formats of data and you can easily import SQL tables in your code. It allows you to create datasets and you can literally find any kind of dataset on Google.
4) Hadoop Platform
Although it is not always required, it is much preferred in many cases. Having experience with Hive or Pig can also be of great benefit to you. Familiarity with cloud tools such as Amazon S3 can also be beneficial. A study by CrowdFlower found 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 the memory of your system or you need to send data to different servers, this is where Hadoop comes in. You can use Hadoop to move data quickly to different points on the system.
5) SQL Database/Coding
Although NoSQL and Hadoop have become major components of data science, it is still 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 adding, deleting and extracting data from a database. It can also help you perform analytic tasks and change database structures.
You need to be proficient in SQL as a data scientist. This is because SQL is specifically designed to help you access, communicate and work with data. It gives you insight when you query a database.
It has short commands that can help you save time and reduce the amount of programming required to perform difficult questions. Learning SQL will help you understand relational databases better and boost your profile as a data scientist.
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