5 Ways Data Scientists Can Improve Analytical Thinking Skills
As a data scientist, along with a practical skill set, what is equally important is the ability to think critically, as it can help you understand the business use cases better and improve your problem-solving ability. In other words, polishing your structured thinking is another essential aspect which can, to a large extent, determine the ability of a data scientist to think quickly and produce the expected outcomes within the given time. In most cases where even your business clients do not have a clear picture of the outcome that they are expecting, it is data scientist who comes to the rescue of companies by meticulously strategizing and delivering the outcomes through data. “Having a good understanding of strategic management, the data scientist will understand and be able to help businesses extract more value from data collected, by proposing how machine learning models can be used in the existing processes to assist the execution of business strategy,” highlighted a leading data scientist on the importance of strategic thinking. In this article, we look at ways in which data scientist can polish their structured thinking abilities: Start with the answers first: When a problem statement is presented before a data scientist, in the majority of the cases, the initial crucial time is spent on finalising and looking at means to achieve their goal, rather than concentrating on the goal itself. Without a proper understanding of the business cases, the chances for data scientists to a device solution which do not meet the client expectation is even more. Hence, there is a great need for a data scientist to understand the business use case and client expectations before deciding a course of action. “To communicate in a structured way with a busy executive, you should start with the answer to the executive’s question first, and then list your supporting arguments. For many people, it’s natural to build up to a conclusion by first reciting all of the facts, recounting all of the analyses that have been done or reviewing all of the supporting ideas. Then you get to the punch line,” a Medium user points out. Relying on mind mapping tools: This mode of idea mapping is best suited for a data scientist who relies on decision tree, random forest, boosting etc, as it will help data scientists to present their ideas and course of action in a visually appealing way while in an easy to comprehend manner. By doing so, it becomes easier for you to explain ideas better and for the purpose of management and client briefing. It is also worth noting that there are a number of free tools Xmind, Coogle, Freemind etc, which can help you chart your ideas better. Charting the hypothesis: To every probable question, there are always probable outcomes that you need to consider. Hence, the need for data scientists to chart the possible outcomes, understand possible loopholes and developing a solution accordingly is very crucial. “It is one more reason to follow through with hypothesis-driven reasoning in your projects every day! You don’t need to be a data scientist to let yourself be guided by this way of thinking and it helps steer away from the client from hasty conclusions,” a leading consulting team notes on the importance of hypothesis-driven thinking. Observe the trends: Having a thorough understanding of the industry and having a keen eye on the recent trends will help data scientist in identifying the business drivers. Hence, a data scientist should make it a routine practice to observe internal trends in the day-to-day work, “Understand the unique information and perspective that your function provides and define its impact on the corporate level strategic thinking,” notes Harvard Business Review. Practising Frequently: The more business challenges you take up, the chances for you to break down complex nuances, understand the business case scenario and knowing the mechanism to achieve your goals is more. Thus, from regular and frequent practice, determining the strategy, improving your problem-solving ability, a better understanding of client expectations, estimation of profit or loss are some of the aspects that you can improve.
As a data scientist, along with a practical skill set, what is equally important is the ability to think critically, as it can help you understand the business use cases better and improve your problem-solving ability. In other words, polishing your structured thinking is another essential aspect which can, to a large extent, determine […]
In this article, we look at ways in which data scientist can polish their structured thinking abilities and the importanc of it.
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["AI", "machine learning", "R", "Go", "programming_languages:R", "programming_languages:Go"]
Akshaya Asokan works as a Technology Journalist at Analytics India Magazine. She has previously worked with IDG Media and The New Indian Express. When not writing, she can be seen either reading or staring at a flower.