Last updated on October 20th, 2021 at 06:17 am
Data archives have increased exponentially, and it’s posing great challenges to various industries. Fortunately, they have gone beyond ‘What is Data Science?’ to finally adopt Data Science analysis, to derive something meaningful and productive from the existing pile of information.
Today there are many takers for data science training, and people have learnt some important lessons. Let’s discuss some of them:
It’s important to understand business as a whole
At times people give too much emphasis on technical knowledge and out the domain knowledge on the backburner. This way they end up creating a sophisticated model without really understanding the business needs. Such models don’t add much value to the business, regardless of their accuracy.
As a data scientist, one needs to understand a business through the eyes of data. Only having the technical knowledge won’t help you articulate your ideas to colleagues in the context of business. So, besides Jargons, it’s important to learn the commonly used terms pertaining to a business.
It’s important to have a penchant for details
Data scientists can’t carry out data cleansing and transformation without having an eye for details. Data in real-world scenarios is never arranged perfectly, and one needs to isolate a lot of noise from it, to arrive at something meaningful. So, a detail-oriented mindset is a must to succeed in Data Science. Without that, you may not derive insightful results from your Exploratory Data Analysis. You may put your heart and soul into the data cleaning process, but still the data might not be reliable enough to be used by your Model.
Framing logic and designing an experiment
Machine learning problems are not that complicated, as you just need some data for training purpose in order to build your model. In case of Data science, there is a well-structured workflow that provides a larger picture of the undergoing processes (Data cleaning to Model interpretations). There is a component called Experiment which is a part of the workflow. It includes the logic for hypothesis testing and Model building.
Therefore, data science helps in framing a logic and designing an experiment for real-world scenarios, to test certain assumptions and evaluate the Model. You can understand more about this aspect by opting for a Data Science course.
Communication skills
If you are a Data Scientist, you better enhance your communication skills, as it will help you sail through. As mentioned earlier in the write-up, there is no point in acquiring the technical knowledge and crunching the data all day long, if you can’t communicate your ideas to stakeholders in a business-friendly language. This affects your credibility as well as your professional relationships. In short, it’s a lose-lose situation.
As a data scientist, your biggest challenge is to put forward your most complex ideas and insights in a layman’s language, so that even a 15 years old can understand them. Your language should make your colleagues feel empowered, so that they can invest emotionally and intellectually.
Art of Storytelling
If you think that Data Science is all about crunching data and building models, then you are mistaken. It’s also about weaving a compelling story based on data analysis, to indulge the stakeholders. Depending on the project goals, the story should cover the following questions:
> What’s the reason to analyze the data?
> What insights can be obtained from the results?
> Can any action plans be derived out of the analysis?
Often the art of storytelling is ignored over data-driven analysis. Lousy storytelling or boring presentations, can greatly undermine the valuable results from even some of the best models.
It’s important to set a benchmark for comparison
It’s naïve to assess the efficiency of a Model without comparing it with other models. Without a benchmark, it always difficult to define ‘What is good’, and the results can’t be fully trusted.
Art of Risk management
Every Model is built, keeping in mind the best and the worst-case scenarios. You are required to explain your model’s limitations to stakeholders, and how much risk can the company potentially bear if the model goes to production. This is where Risk Management comes into picture. If you understand what’s at stake and have a plan to minimize the risk involved, then only you can take stakeholders into confidence. To understand more about the art of risk management, you may opt for a Data science course.
Intensive Data Science training can help you realize the above-mentioned lessons. If you are an aspiring Data Scientist, we hope this write-up served the purpose.
