Are you curious about data science? Are you interested in the work of your data scientist coworker? Do you want to become a data scientist? This is your primer into this exciting field!
This article is for anyone who wants a no-nonsense, easy explanation of what data science is, how it works, and what it is used for. Maybe you heard of data science and you wanted to learn more. Maybe you work with a data scientist and want to better understand their role, or you even have the goal of becoming one. This article, featuring uncomplicated term definitions and depicting examples, was made for you.
To understand what data science is now, it is important to understand where it started. Data science has been around for longer than most of us realize. In 1974, the famous computer scientist, Peter Naur, proposed data science as an alternative name for computer science. And funnily enough, in 1985, C.F. Jeff Wu used the term as an alternative name for a completely different field, statistics, in one of his lectures. If it isn’t obvious, there is some pretty killer foreshadowing here.
Many people still insist that data science is just calling statistics by a different name. In 1985, that could have been true during a C.F. Jeff Wu’s lecture. However, I don’t believe that to still be the case. Because of the huge volumes of data and the increased complexity of computing, many of the problems a data scientist faces today cannot be done without the help of computer science and some advanced understanding of the unique domain they are operating in.
How Data Scientists Create Value With Data
Let’s take that apart a bit farther; how do we work with data to generate some value? The ways that data scientists drive value from data is for the most part derived from the data science lifecycle. All organization’s data usually follows this path.
The first way that we generate value from data is through collecting it. While it isn’t necessarily a core role of all data scientists. Some data scientists use their skillset to collect data. This can be done through building systems for data intake like webpages or surveys or it can be done through writing code that collects data from different places online.
The next way that we create value from data is through analysis. Simple analysis starts with basic statistics. For example, we may want to look at the average spending of an online customer vs. an in-store customer. Insights like these can help us to make better informed decisions about how we merchandise or market. Often the best way to convey these insights is through beautiful data visualizations.
The final main way that data scientists create value from data is through automation. If we put some of these models that we build into production, often they make recommendations at a pace that far exceeds humans. A great example of this is on Netflix. They have machine learning algorithms that recommend you videos in real time. For a real person to do that same service, it would take thousands and thousands of people and thousands and thousands of hours. In this case, it really only takes a few algorithms to do it almost instantaneously
Tools Data Scientists Use
Now you should hopefully understand where data scientists create value. But what tools do they use and what does their work look like?
In my mind, the most important tool in a data scientist’s toolkit is programming. Most data scientists use either Python or R with Python being the more popular of the two. Other languages are used, but it is usually for a specific domain or use case. Data scientists are able to access the data, manipulate it, create visualizations, build models, and productionize their models all through coding. Programming is a data scientist’s all purpose tool.
Data scientists also have specialist tools that they use. For getting and manipulating data, data scientists will often use SQL. This allows data scientists to communicate easily with databases where the data is stored. Another specialist tool would be something like Tableau or Power BI which provide a graphic interface for creating data visualizations and dashboards.
What Is A Data Science Project Like?
We build these models but what does the end-product of a data scientist’s work look like? Honestly, this varies pretty greatly by the role. Data science deliverables generally come in three flavors:
1) a dashboard that guides business stakeholders to their own insights,
2) a deliverable that makes a recommendation or a prediction on a specific problem
3) a trained model that users can get real-time predictions from.
I think it is really important to understand that within this domain, there aren’t really ever clear right and wrong answers. There are just shades of certainty and uncertainty. What I mean by that is that a model that we build gives us an estimate about what will likely happen. The confidence of our model helps us to decide if we should take action or not. In theory, any model can be wrong even if it is predicting if the sun will rise the next day
Conclusion
Hopefully this article helped you to better understand data science and some of the types of problems data scientists can help solve. If you think this article would be helpful to one of your friends, someone you work with, or someone who is looking to become a data scientist, I would appreciate it if you forwarded it along. So , I Recommend ONLEI Technologies is the best training institute in India Provide Data Science Course and Job Oriented Course .
Important Links
Comments
Post a Comment