Relevance of data science in commercial organizations
Let’s take a brief look at the implementation of data science by several industries. Pharma companies use data analytics to speed up their drug discovery process by crunching relevant empirical data from the past.
Similarly, health monitoring devices are helping healthcare industries in delivering more personalized treatment to their patients. Data analytics serves a great purpose in the automobile sector by providing simulations for part testing, which helps minimize human error and fast-track the product development process.
Similarly, the AI-powered tools used in automatic vehicles use neural networks from other vehicles to serve their deep learning algorithm. The neural network uses relevant data for image recognition and object tracking. The financial industry is no exception to this technology either.
Several investment assistant tools scan the internet to find relevant data that could help identify promising stocks. Moreover, data analytics use this data to make accurate predictions about a stock’s performance. Additionally, such tools also search the internet to find volunteer groups that constantly post relevant stock information on their forum.
Now, the work of a data scientist is not limited to crunching information and making suitable models out of it. Data science is a service-based industry in business outsourcing organizations. Therefore, a professional data analyst must pitch his idea perfectly and explain to his client the relevance of his retrieval algorithm. So, you can expect good communication skills from a data scientist. You can learn what data analysts and data scientists do in data science classes from Great learning online and implement them in your organization.
When an organization hires data scientists, it looks for more than just hard skills. Many people are pursuing this profession today, but your efficient communication skills can make you stand out from the crowd. Let’s look at different classes of presentations today.
- One-on-one: As a seasoned data scientist, you will have to frequently sit down with the client to explain your approach and progress to him. This is the kind of presentation where you will have to present your data in the form of a story. This will
help establish a connection with the client and bring your firm more business. You must explain the reasoning behind your findings and present every detail in an articulated manner. To summarize, one-on-one presentations are more about intimacy and less about formal presentations.
- Small-intimate groups: If you are working on a massive project that involves a crucial investment decision, you need to present your findings in front of small-intimate groups like the company board. The board members like making objective decisions, so they look forward to your presentation. Now, board meetings have many agendas on their table, so your allotted time would be short. So, your presentation should be short and crisp and stick to the facts. Lastly, use your facts and findings to answer the board members’ investment queries.
- Classroom: This is a relatively large group of people with around 20-30 attendees. Classrooms can be the real test of your presentation skills. Here, your objective must be to summarize your findings in a story. Here, you don’t have to project your methods before your audience, and you have to summarize everything in 30 minutes. You can make a lucrative presentation consisting of ten slides and 20 talking points, stick to the basics and explain your talking points well.
- Storytelling: Storytelling is central to your presentation. It makes sure you don’t bore your audience with unnecessary details. It is the art of the speaker to present the data points in the form of an exciting story that engages the audience. Your goal should be to interact with the audience to get better insights into your methodology. Moreover, if you have engaged your audience, you will get compelling questions that drive your presentation forward. Storytelling is crucial because data science is a complex discipline.
There are a lot of technicalities and algorithms involved. You must understand that your target audience will largely consist of those who have no exposure to data science whatsoever. Hence, if you indulge in too many technical details, you will end up confusing your audience even more. Therefore, your goal should be to present your facts such that they seem credible and also appealing. Here are a few things you can incorporate in your storytelling art:
- Put together a frame: Before starting a story, you must explain the context and background to your audience. You may include a few talking points from historical events to engage your audience at the earliest offset. Next, elaborate on the background of your research. Such as what got you to build a particular statistical model, why it was necessary, etc.
- Convey your story properly: A good storyteller is thorough with his narration. Hence, practice your story several times before presenting it to the audience. Remember, fluency is the key. Include interesting words and trivia to make your narration more interesting. You can even include fun questions to ensure that the audience is listening. Lastly, you can also use props like slides, dram, charts, etc., to ensure that the message gets through properly. Although, use a prop only when it’s necessary for your plot.
Data scientists are highly sought after by modern industries. As discussed earlier, many sectors are heavily influenced by the relevance of data because of their shift to digital media. A professional data analyst is at the center of business affairs today. However, industries are starting to expect data scientists to have more than just hard skills.
Soft skills are equally important today to stay relevant in an organization. If you are willing to build a career in data science, sign in to Great Learning. Here you will find plenty of relevant courses like the data science certification course from Great learning that will help you build the best profile.