How to choose the best Data Scientist
What if I told you that data is really just a pile of figures that doesn’t make sense if you don’t know how to use it? Well, data analysis is a lot more than observing and translating your observation into plain English. Data Scientist gathers, process and perform statistical analyses of data. This might sound a bit complicated. We can break it down and say a data scientist is an umbrella term that defines people whose main duty is leveraging data to assist other people or machines in making more informed decisions.
Data Scientist is similar to ranch dipping sauce with Pizza. You may think you don’t want it, but if you have ever tried it you know that Pizza just tastes better. Many companies are making huge mistakes by assuming they need only data scientists and trying to hire them before there’s enough work for them to do. Luckily, that can easily be solved by understanding the various roles of a data science team and thinking like a data scientist.
So, how can you Start Thinking like a Data Scientist?
Many resources out there may lead you to believe that for you to analyze data you require comprehensive mastery of a number of fields, such as software development, data munging, databases, statistics, machine learning and data visualization.
Don’t worry. You don’t need to learn a lifetime’s worth of data-related information and skills for you to tease useful insights from data. Instead, you just need to follow some few steps to be able to analyze or think like a data scientist.
The exercise is very much a how-to, and each step also illustrates an important concept in analytics. Here’s what you should to help you become data literate, open your eyes to the millions of small data opportunities.
Step 1. Start with something that interests or even bothers you at work, form it up as a question and write it down.
Step 2. Think through the information that can help answer your question, and establish a plan for creating them. Write the relevant definitions and your order for collecting the data.
Step 3. Collect the data. It is important that you trust the data you collected. And, as you go, you’re almost certain to find gaps in data collection.
Step 4. Record the data collected and start drawing graphs to help you define the data. Good drawings (graphs, charts) make it simpler for you to both comprehend the data and communicate main points to others. There are lots of good tools to help, but it is good to draw your first picture by hand.
Step 5. Now go back to the question that you began with and develop summary statistics.
Step 6. Answer the “so what?” question. But this case demands more, as some analysts do. Get a feel for variation. Understanding variation leads to a better feel for the overall problem, deeper insights, and novel ideas for improvement.
Step 7. Now ask, “What else does the data reveal?”
After understanding the role of a data scientist, you need to know what to look for when hiring one who can effectively work and give maximum results. You need to find, not a good but great data scientist. So what separates a good data scientist from a great one?
- A sense of curiosity. Any scientist is curious, just like Good data scientists but the great ones take this feature to an extreme. They have a sense of curiosity about the world and are pleased when they find out how something works or why it works that way. They look for these answers in data and anything else that will help.
- A certain quantitative ability. Great data scientists simply see things that others don’t.
- Persistence. The great data scientists are persistent and in many ways. They understand that there’s almost never a perfect solution, and a simple imperfect solution delivered on time is much better than a hypothetically perfect one late. They like drilling in, looking at a macro level, finding insights, re-defining hypothesis, and looking at the most granular details.
- Technical skills. Great data scientist have the abilities to access and analyze data using the newest methods are obviously important. Great data scientists embrace uncertainty. They recognize when a prediction rests on solid foundations and when it is merely wishful thinking.