You might have heard about data science, don’t you? (of course)
If you haven’t yet, then you might be interested in getting an overview of data science.
Before starting, we will touch the basic hierarchical model of data.
Here, we have DIKI pyramid showing relationships between data, information, knowledge, and insights.
- DATA is an unorganized and raw information. It carries the unprocessed information. — FACTS & FIGURES
- INFORMATION is a processed data which has purpose and value. — WHO WHAT WHERE & WHEN
- KNOWLEDGE is an understanding of data via exploring it. — HOW & WHY
- INSIGHT is an empathy or finding a bigger picture of a scenario. — MEANING
Let’s suppose you own a restaurant. Now, your target is to maximize the profit. So, you started doing some cool things.
- First, you record the food items you sold. It’s your data.
- With your data, you’ll find numbers of food items sold. It’s an information.
- Doing further analysis, you’ll understand why certain foods are popular. That’s knowledge.
- To increase profit, you’ll deduce least sold food items with the popular one. This’s insight.
So, we can say that data science is a way of uncovering an insight from the data. When data couples with technology it can solve the complex problems and help in decision making.
Defining the Data Science
Data Science is an interdisciplinary field that combines computer science, mathematics, and domain expertise. It has wide applications in business, health, government, social sciences and many other areas.
Foundation of Data Science
So, to turn your data into meaningful insights, you need a data science team. The team who excels in the three foundation skills:
- Domain Expertise – to define the problem of a particular field.
- Mathematics – to help the understand complexity and increase the problem-solving capability.
- Computer Science – to create an environment that efficiently handles data.
Above all, the data science exists at the intersection of these three foundation skills.
1. Domain Expertise
The science behind data is about discovery and building knowledge out of it. First, you need to ask the right questions about the problem. Then, generate hypotheses that apply to data. Eventually, you can test the hypothesis with statistical methods.
Everyone wants to know, what’s going to happen with their sales. Indeed, it’s an obvious question. But, domain experts can ask more specific questions, such as the following:
- What will be our sales next week?
- Can we increase productivity in the marketing department using our sales data?
- Does this product attribute from sales data, contribute to our product sales?
Certainly, questions first then data.
In the real world, data science is about applying mathematics. Once you get and clean your data, the next process you do is pull the insight out of it. You need to apply specific math and statistical methods to address complex problems. So, mathematics is important for your data science team.
Amazingly, Data + Math = Machine Learning
Not to mention, the best teams often develop machine learning (AI) tools for predictive models. But, you don’t always need to have them to solve every problem.
3. Computer Science
Of course, data science will happen within computer systems. The data science team members work with various computing tools (programming). Besides that, your team needs to have specific technical knowledge (data structures and algorithms).
As a matter of fact, your team member might work with Big data, AI (Artificial Intelligence), and Databases. It requires different skill sets and processing tools. Having the right data science work environment is important as having a physical work environment.
Finally, we can say, data science is a team sport. As a result, your data science team needs to incorporate with skillful players. Together your team solves specific challenges and problems. This creates new opportunities and possibilities for a better world.