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Whether you're a newbie or a pro, refresh these data science mantras.
In every industry today, data science is a hot topic. Rightfully so, because it is bringing industries like artificial intelligence, machine learning, big data, and data visualization to life. To be a successful data scientist, having the will to learn and unlearn is crucial. So if you're about to begin your data science career, these 7 mantras will help you stay steady through the big picture, and if you're an experienced professional, here are some tips you can include in your day-to-day data work.
If you start working on data, create models, and prepare descriptive analysis assuming the data is clean, you can come up with wrong hypotheses. Instead, looking for discrepancies in data can present a lot of important patterns. For example, if a column has more than 50% values missing, an analyst 'A' will think about dropping the column. But if the same error was in a data collection instrument, spotting it would help the business improve. Finding out such errors will open opportunities for questions that might lead to a bigger picture.
In data science, one has to tell stories through data. The company's board of directors and stakeholders will be expecting statistics plans and insights from you which will only be understood by them if you ace effective visualization to show them your data's story and effective communication skills to narrate your opinions. When you spend a lot of time collecting, cleaning, exploring, and modeling data, finding interesting patterns and presenting it will mundane visualization will be ineffective.
Remember this, every business problem is different and it should be optimized differently. For example, if a client wants you to optimize for active users, you should judge better and advise him to optimize the percentage of active users instead to know how the client's product is performing. Having the right metric in place before modeling a data science project is crucial in getting accurate insights.
Embrace the scientific side of data, not just the technological side. According to Colin Melody, senior manager in data science at Deloitte, data science must remember the scientist part of their job. "At all times, you are looking to provide evidence which supports an idea. This means, from end-to-end, you must challenge your assumptions, your data, test, and retest, refine, and start again. There is a myriad of tools and technologies available for data scientists and, while it is not necessary to know how to use all of them, try to get a sense of what it might take to grow your toolbox."
While you're mastering all the concepts needed to be a pro data scientist, master when to take your knowledge out. Data science is a field that has new and different advancements every day. There is a possibility that you won't know everything and waiting for it will not accomplish anything. The wait for "know enough" is not a constant factor. The term is too subjective to risk building a good project or apply for a role. So once your foundation is ready, be out there and apply your knowledge wherever possible.
Data science is not an independent field. That means data science is an interactive interdisciplinary field that depends on other fields like maths, statistics, and scientific learning. Any data science sub-field will require you to tap into machine learning, artificial intelligence, and NLP. So it is advisable to keep yourself up-to-date with everything surrounding the field, not just the bare minimum.
The data part of data science is obvious, but it's also about the entire problem and the solution you're trying to find out. Understanding the needs of the end-user will help you solve the problem. It will not be easy at first, but with an inquisitive mind, ponder over the dataset and build the model step by step by understanding how the end result will interact with your model.
