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What is Data Science? Is data scientist is the upcoming thing in the future? Will data science destroy digital marketing? Can digital marketing and data science work for hand by hand? What is it? In this article, I will explain everything about data science.
What is Data Science?
Basically, I am trying to introduce with numbers and want to make you understand how to play with numbers. The first step for data science is to excel. You need to be good in it to grow in data science.
But first, try to understand what is data science? Data science means we have taken a marketing strategy by analyzing the data. We have taken large data and analyze it and then we take a decision.
So this is data science now it is all up to you where you make it. You can make it on programming, excel or any other platform. Now, there are two big things in data science.
AI and Machine Learning
Once, you collect that number of account then you can easily make it on excel and then work will be done. But think you have a lot of data. For example, you are trying to analyze Google’s data or trying to analyze the data of that company which has over 100 branches.
And the daily data from each branch is more than 1 million. So would you able to make it on excel sheet?
Maybe there will be many things which will be work on the monotonous basis.
The first addition will be done and then subtractions and the process will keep going. Do the same thing on Programming instead of excel so you will be called a data scientist, which is called machine learning.
Machine learning means machines are trying to learn how people behave. Like I just said OK Google. So in many phone, it has been opened and on some phone, it didn’t, So after that machine realized it didn’t open.
Whenever we do chat on WhatsApp they ask us for the rating. Some people give 2 star, some give 3 or 5 star and different. So on the behalf of that data, they try to analyze and data on a broader vision.
There are various factor on which they try to analyze like how was the connection, what was the device specifications and many more.
On excel we can only consider 2 or 3 factors and if we want to consider more than that so we have to set up big software. So that’s why this thing has been shifted to Programming. So we don’t need to spend money on complex software while analyzing the big data.
Engineers students know very well that we need to use stats in order to analyze bigger data. In the past when technology was not updated then stats were used to analyze data, This is the reason we are still learning about stats.
But now the software has made on which we have to put the stats and answer will come up on the screen automatically. But suppose we have to make a thing for which no software is available so in that situation we will make our own code like we generate formulas in excel.
Then we will use that code to execute the data of stats. The number one benefit is our product will improve. People will give ratings to WhatsApp so in this way, we can give data to the machine that you are no working fine and doing things in this way ultimately work will be improved.
If there’s a service industry as you know about the recent incident. If a specific store is getting bad reviews so the overall company can see that it is not a good thing. That’s why A big brand always ask you for filling a feedback form after using their services.
In the old days, the feedback form was used to fill manually on papers that’s why companies hire people for data entry. They just write down all the data on the excel sheet.But where all the data go after compiling?
Suppose there are 50 stores in Delhi 50 in other states and there would be one person whose duty is to collect the data of all the stored in Delhi and the same thing will happen with other states. Then thee would be one person who will put all the files together and then he will try to analyze the data in a broad term.
He will try to figure out what people like or not. Are they liking our new product or not?You even can’t imagine how big this thing is. In everything data science is using. It doesn’t matter you are selling a product on Amazon or you are buying a from there.
I am only talking about big companies, not small ones. I am talking about only those companies who have a big database. Data science are everywhere like if they want to improve their product,service or even their process.
For example, you have a machine which manufactures something. So a person would be there who will analyze where the machine is lacking and how much time it is talking and many other things.
A role of a data scientist is analyzing the data and then take a decision. The data scientist also does A/B Testing as we digital marketers do. If we are also running ads on the data-driven approach then we also called digital marketing scientist.
Education for Data Scientist
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 scientist, 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 necessary to process and analyze big data.The truth is, most data scientists have master’s degrees or PhDs 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.
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. In fact, 43 percent of data scientists are using R to solve statistical problems.
However, R has a steep learning curve. Especially if you have already mastered a programming language then it is hard to learn. Nevertheless, there are very good resources on the internet for getting started in R, such as Simple Learn Data Science Training with R Programming Language.
Python is the most common coding language commonly seen as essential in data science roles with Java, Perl, or C / C ++. Python is a great programming language for data scientists. This is why 40 percent of respondents surveyed by ‘averageReilly’ 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 dataset and you can literally find any type of data set on Google.
Although it is not always required, it is preferred in many cases. Having experience with Hive or Pig can also benefit you a lot. Familiarity with cloud tools such as Amazon S3 can also be beneficial. In a study conducted by CrowdFlower, 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’s 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 Database / Coding
Although NoSQL and Hadoop have become a major component of data science, 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.
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. 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.
That being said, data science is playing a big and prominent role in the functioning and development process of brands. So being a data scientist is a major position for anyone as they have the big task of managing data and providing solutions to problems both within and outside the organization.
What is data science? Today, data scientists are opening new grounds in terms of experimentation and research. They are experimenting with intelligence gathering technologies and developing sophisticated models and algorithms to help brands meet their biggest challenges. A data scientist will perform key tasks and roles, some of which include the following:
- Link new and different data to offer products that meet the aspirations and goals of your target customers
- Use screener data to track weather conditions and restart supply chains
- Uncover frauds and anomalies in the market
- Increase the speed at which data sets can be used and integrated
- Identify the best and innovative ways to use the Internet so that brands can create wider usage opportunities.