A data scientist is a person who extracts actionable insights from data by using programming, statistics, machine learning, and domain knowledge.
That is a very generic description, however, the field of data science is so broad that it's tough to define the role without going into the specifics.
To give you an example of what a data scientist can do, take a closer look at the last selfie you took. Look at your face; what emotion are you showing? Are you happy? Sad? Crying? Laughing? All at the same time? For you, answering those questions is trivially simple; however, getting a computer to do it is a whole different problem.
And that's where data scientists come into play.
Data scientists take unstructured data (like video, photos, text files, etc) and structured data (like database rows, spreadsheets, etc) and figure out what it all means. By analyzing this data (some call it "big data"), they help companies make better decisions, such as understanding what customers want, how they feel about their products, or even predicting future trends.
They help find the hidden answers in the data, which is what makes this profession so appealing to some.
What does a data scientist do?
Most data scientists collect, organize, and study data to uncover useful insights. At a high level, here's a simple way to break that process down:
Collecting Data: They gather information from various sources, like websites, databases, or devices. Depending on the project, the sources of information might be very different, but the point is that once the data enters the domains of the data scientist, it's all 1's and 0's for them to process.
Cleaning Data: Before being able to use the data, they need to ensure the data is formatted correctly, doesn't have any holes, and that the values actually make sense within the context of their source (i.e., that there are not too many "outliers"). They fix these mistakes and make sure the data is ready to use.
Analyzing Data: They use tools and techniques, like exploratory data analysis, charts, or algorithms, to find patterns and trends.
Sharing Insights: Once they're done with their analysis, the last step is sharing the results. Data scientists explain their findings in easy-to-understand ways, often with visuals, so that others can take action based on the data.
For example, using these steps, a data scientist might help a company predict which products will sell best next month based on historical sales data and customer trends.
How do you become a data scientist?
There is no single way to become a data scientist, however, the journey usually involves these steps:
#1. Learn the Basics: Start with math (like statistics) and programming (Python or R) to understand and process data efficiently.
#2. Practice with Data: Begin with small projects, like analyzing trends or creating charts, and gradually tackle more complex goals.
#3. Take Courses: Use online classes and tutorials to learn Data Science step by step.
#4. Build a Portfolio: Solve real-world problems and share your work to showcase your skills and attract opportunities.
#5. Get Experience: Seek internships or entry-level roles to apply and grow your skills.
In the end, you have to keep in mind that this is a marathon, not a race. Rushing through knowledge or cutting corners for the sake of speed will only limit your options and your understanding by the time you actually do get the job.
With curiosity and practice, anyone can start exploring the world of Data Science.
