One can hardly call Data science scientific it can be considered as an unreliable friend. so What to do with data
It was noted that the way the media portrays this work is basically incorrect; a data analyst does not simply enter data into ready-made algorithms
70 years history in 2 episodes and 1 photo
Data science can be classified as a wide range of complex mathematical operations, and the most part of it was invented in the past but gained a
second wind sue to the increasing use of significantly improved technical
devices: increasing data day by day, more computing power, more reasonable
results at a lower price.
As the cost of storing and processing data decreases, the volume of data daily collections have increased: A simple rule of thumb for supply and demand, or can be called the price of Data. The price goes down, the volume goes up. Someone will have to do something about it all in order that’s where Data Science comes in.
It is one of the most reliable and in-demand career paths for the work of skilled professionals. The term ‘data science’ suggests a specific approach to being a solution to the problem search. Here, some details; what can we do with it, or anything?
It actually sounds a little too much, not just the wisdom of the job to set up a personal expertise, but also as a business plan: let’s invest a lot of money to collect all this data, one day something good will come out of it.
Unfortunately, the industrial revolution in the XIX century gave us schools and universities to train a large number of blue-collar workers to provide the same answers to well-packaged questions, and little has changed since then.
What about training people to ask the right questions instead of letting the machines find the answers?
Data science can be a career dead-end
Although many flavors of data science are gaining new popularity, such as Artificial Intelligence and all the other marketing methods associated with it, this activity is only suitable for beginners.
A good prospect of a salary of 80k + annual average may sound appealing, but estimates hide the difficulty of the problem. To be truly successful with data one has to be exposed to certain problems, influential and well-defined, rather than being a general expert of information or the worst science, especially old from an educational point of view - as the first picture shows.
Data and algorithms are powerful tools. However, like any other tool, they can be as good as the human user.
Developing Business Science to succeed
How so one successful with data? Focus on the problem to be solved, the job-to-be-done, rather than data.
For those who focus on cases for commercial use, Business Science proposes all the right ideas:
1. Business problem defined, researched & resolved
2. A scientific method, driven by data
3. Business impact: measurable, purpose the result.
For not-for-profit and other use cases, the idea is the same: start for a question / hypothesis, use a solid approach, and return to learning methods / question and verify if there is a proven impact or not. Cleanthen repeat without interruption.
In recent years, there has been significant growth in the Internet of Things (IoT), due to the fact that 90% of the data is generated in the current world. Every day, 2.5 quintillion bytes of data are generated, and IoT speeds are being accelerated. This data comes from all sources such as:
• Sensors used in the mall to collect customer information.
• Post on social media.
• Digital photos and videos taken from our phones.
• Buying data from e-commerce transactions.
This data is known as big data.
After reading this article finally you came to know the importance of data science. For more blogs/courses in data science, machine learning, artificial intelligence, and new technologies do visit us at InsideAIML.