Data Scientist's Toolbox
Explore the essential components of a data scientist's toolbox, including programming with Python or R and SQL, the importance of domain research for data cleaning, and effective storytelling to communicate insights clearly. This lesson helps beginners understand the key skills needed for data science expertise and how to prioritize learning these tools for successful career readiness.
We'll cover the following...
You might be asking, “What tools should I learn to become a data scientist?” You’re expected to refer to programming or software tools. First, do not assume that programming will be enough to enter the data science world. We have two more tools as well—research and storytelling.
Research
Research in data science is understanding the domain to use the data appropriately. For example, let's say you are working on a healthcare project in which you have to analyze data from patients who developed Alzheimer's, but you don't know what Alzheimer's is. Should you research Alzheimer's before presenting your data?
A person new to data analysis might answer no, saying, "I have to analyze data, which I have. Why would I study Alzheimer's? Studying will take a lot of time, and I am not a doctor. I have been hired as a data scientist. I will analyze the data and present my results." But the truth is, not knowing ...