Today, many big businesses like Amazon, Google and Facebook collect and analyze data to make their product and services better. Small businesses even use data to make valuable decisions to grow their company.
In 2019 it was reported that Google and Salesforce spent $20 billion on self-service data science.
Data science, which is related to data mining, big data and machine learning, has become such an important part of science, engineering, technology and everyday lives.
There are many different elements of data science like collecting, analyzing, storing data, A/B testing and many more.
But what is data science? What does a data scientist do? Since data science is such a broad and in-depth topic, we’ll go over the basics of data science the why it’s important.
What is Data Science?
Data science refers to the use of methods, processes and systems to extract knowledge and data from both structured and unstructured data.
An American computer scientist William S. Cleveland wanted to combine both computer science and data mining and make statistics more technical. Using computer power to collect data, making it more powerful.
Journal of Data science explains it as “Almost everything that has something to do with data: collecting, analyzing, modelling.. yet the most important part is its applications – all sorts of applications”
What data science aims to do is solve problems and achieve goals by collecting and analyzing data to make solutions. It prefers data-driven learning instead of knowledge-driven learning.
What does a Data Scientist do?
With so many topics for data science, what exactly does a data scientist do? The answer depends.
It all depends on the resources of the company, but to put it in broad terms. A Data Scientist works with business owners and stakeholders to understand their goals and how they can use data to solve their problems and achieve their goals.
Data scientist have many roles and responsibilities but generally includes the process of gathering and analyzing data to create algorithm, models and machine learning to help the business achieve their goals.
The Northeastern University lists these as the roles and responsibilities of a data scientist:
- Ask the right questions to begin the discovery process
- Acquire data
- Process and clean the data
- Integrate and store data
- Initial data investigation and exploratory data analysis
- Choose one or more potential models and algorithms
- Apply data science techniques, such as machine learning, statistical modelling, and artificial intelligence
- Measure and improve results
- Present final result to stakeholders
- Make adjustments based on feedback
- Repeat the process to solve a new problem
Data science hierarchy of needs
Similar to Maslow’s Hierarchy of needs (the different stages go through to find happiness). There’s a similar pyramid or hierarchy of needs of data science. Saying that you need a solid foundation at each level for each to move up.
So when individuals or companies are working on a data science project, they should work on each level before moving on to another level. If they skip a level or move too fast, the foundation or level below it may not give proper data.
So take a look at the data science Hierarchy of needs from Hackernoon :
At the bottom, if your data collection or Collect, so what kind of data do you have, how will you collect, what kind of data is available etc. collecting some sort of data that will be used as data for analyzing.
We then move up to data flow, how you pretty much move and store your data. Is the flow of your data reliable? Is the data easy to access and analyze?
It moves up to the higher levels where you can analyze and transform your data to come up with data-driven decisions. It allows you to efficiently test and experiment with your data to come up with the best solution. At the higher levels, you can establish the Machine learning algorithm
At the top is AI, deep learning
As mentioned before, depending on how big the company is or how many resources are available, a data scientist may be doing all levels at a startup.
But for bigger companies, usually, the lower levels are completed by Software engineers and data engineers, where the data scientists complete more of the top-level needs
Why data science is important
Now that we have a basic understanding of what it is and what data scientists do, why is data science so important?
Data science allows business owners to make valuable decisions to grow their investment. It grabs multiple data sets that may not have any value or use on their own, but it combines them with other data points to generate useful insights that are beneficial for both customers and business.
To make the right decisions, you need the right data. As data science continues to grow and become an important part of our everyday lives, it continues to be an important part of finding solutions to both small and large problems.
Data Science has influenced many different industries. For example, Virtual home assistants like Alexa, Google, and Siri have all influenced the tech industry for both businesses and consumers.
Closing thoughts
Data science and data scientists play a crucial role in the decisions and development of both big and small businesses. Data science includes the tasks of collecting and analyzing data to make solutions and algorithms for everyday problems.
As data science continues to grow, so does the impact it has on our everyday lives without us not even knowing it. Data is collected from us when we use big company products like Google, Amazon, and Facebook to develop and benefit their company and shareholders.
As we continue to move forward, so will data science, technology, products and artificial intelligence.
How ParseHub can help data scientists
At ParseHub, our goal is to help everyone make better decisions by collecting online data. A free powerful web scraping tool can help data scientists to collect the needed data to make solutions and algorithms.
