5 basic skills to become a successful Data Scientist
“Data Science Jobs”, “Data Science Courses”, “Data Science Masters Programs”, “Data Science Certification”, Data Scientist Compensation, blah blah blah…data science reverberates everywhere. It’s once been declared “the hottest job of the 21st century.”
Our world is becoming entirely data-driven. Everything is becoming more and more dependent on data analysis (the basics of data science, from our health maintenance activities to product/service purchase decision. As a result, the need for data skills scientists has skyrocketed.As a result, job opportunities and salary trends in this field are showing a sharp rise every year.
The numbers are impressive, right? But 6 in 10 candidates lack the confidence to move into data science.
Indeed, I am convinced that you have taken a step back from pursuing a career in data science. Especially if you come from a non-IT background, this would definitely be your scenario.
Well, a friend of mine had 5 frustrating years in the marketing industry. She has no background in CS or computer science. She earned her bachelor’s degree in a business-related field from a mediocre university. Yet, she is currently a successful data scientist and advancing rapidly. She wasn’t a coding or stats pro. In fact, I’ve seen techies struggle to become data scientists. Thereby. Technical and statistical skills alone will not be enough to make a data scientist successful. Let’s dig a little deeper into this.
The main reasons why aspirants refrain from becoming data scientists are:
- I don’t know statistics well / or I never studied statistics in my studies
- I’m not a coding ninja / I’ve never even encountered level 0 coding.
But did you know, so far, 75% of successful data scientists come from non-statistical and non-technical backgrounds?
So what core skills do you need to be a data scientist?
- machine learning
- deep learning
- Advanced statistics
- Artificial intelligence
- Table, etc.
- Python, R
Well, these are the most popular answers for basic data scientist skills. You can easily acquire these skills once you enter any professional’s data science apprenticeship program. But that’s definitely not the right answer. Here I will offer the most appropriate but least exposed answer to the same query.
1. You should own Endless Curiosity – Be A Critical Researcher
A Data Scientist is not a simple data analysis profile, where the insertion of data and the effective work on advanced Excel will determine the result. Even it’s not like job profiles with goals, where you have to hit a few numbers in a given time frame. It is rather a profile of a researcher who must also make business decisions.
Yes, to be a successful data scientist, you must first be a researcher. You must have an endless curiosity for all sorts of possibilities associated with data. The greatest data scientists are always wildly out-of-the-box thinkers. They have the ability to predict even the undiscovered possibilities in a data set.
And yes, you must have the courage to do research… research… and more research, followed by critical thinking.. It doesn’t matter whether you do the same on the raw data or on the information.
2. You must be an excellent storyteller
No, I’m not talking about an editorial or a writer’s role. Still, I insist that if you want to succeed as a data scientist, hone your storytelling skills. In fact, a person with endless curiosity, a love for emerging technologies, and extraordinary storytelling abilities was born to be a great data scientist.
But why tell stories?
Well, as a data scientist, your responsibilities don’t end with preparing reports. You need to present the same in front of your target audience (which can be internal or external stakeholders). And you will be responsible for making your audience understand the profitable sites of your presented ideas. The success of a project can be made or broken by the ability to tell a compelling story that engages and draws the audience’s attention to what you want to bring to them.
Therefore, a good storyteller is always a potential candidate for an established career as a data scientist.
3. You’ll need business acumen to succeed as a data scientist
So far, I have put enough emphasis on intellectual skills. Let’s take a look at the practical skills of data scientists. Data science is not limited to the thesis; it requires all the assessments. You need to know the target business area inside out to turn your knowledge into a solution.
Let’s take a simple example. Suppose you are predicting a possible customer base for an e-commerce product. You need an idea of the latest fashion trends and customer directions on price, quality, even color, designs, etc.
Now consider that you are doing a similar project but for a financial product, for example, “a retirement plan”. Will your knowledge of customer-based e-commerce work here? ?
Here you need adequate knowledge of regional financial laws, GDPs, taxes and other charges, credit scores, interest percentages, etc.
As a data scientist, you are not limited to a countable number of data sets to analyze. Therefore, your domain experience should be old enough. Superior is your expertise in the field; the greater the chances of becoming a well-paid and successful data scientist.
90% of job seekers and career counselors neglect this skill (domain knowledge). Ultimately, this results in a failed data science career transformation effort.
But what about programming and statistical skills? In the end, they have already proven to be a crucial pillar in the field of data research.
Here, let’s look back. So far I have mentioned that advanced programming and statistics are not mandatory for successful data scientists. I did not say that these talents were not necessary.
Sure, you need those skills, but you don’t have to be a ninja in the same way. So the next two data scientist skills that I will mention are the following.
4. You must acquire a certain level of mathematical understanding
Please note that I didn’t even use the word “statistics” here. To get started with data science, the ability to understand high school level math is sufficient. Then you will gradually learn the application of advanced level mathematics through appropriate training and practical applications.
Just for the crazies, let’s take an example of a data science project where you’re going to launch a new credit card offer program for customers earning more than 20 LPAs. You need to design a multi-scale investment plan for such a credit card system. Obviously, you cannot offer the same benefits to customer communities with annual spending of 80,000 and the community with annual spending of 5 lakhs or more (via credit card).
So the main types of mathematical calculations you need to do like
● Average, median, mode
● Standard deviation, etc.
● Basic probabilities, etc.
Obviously, for other mathematical needs like correlational calculus or ANOVA, you will get training while pursuing an out-of-the-box data science course.
5. Last but not least, you need the ability to learn programming
To take the first step to becoming a data scientist, you just need the ability to hone yourself with programming knowledge. Prior programming knowledge is not required at all.
In fact, it’s possible, even after you’ve already been a programming ninja, that you won’t succeed as a data scientist because programming knowledge isn’t a big deal here. On the contrary, you have to be the ninja to play with your programming abilities to analyze the data to get unique and profitable insights. Otherwise, with enough interest and considerable guidance, anyone can develop coding skills at any age.
For example, imagine you need to find the average, minimum, and maximum of all employee salaries.
How do you calculate it?
Medium: You will take the sum of all salaries and then divide it by the number of employees.
Example 1: For example, as shown in the table below:
It’s simple, isn’t it? What if you were asked for details of 1 million employees? This is where it gets tricky: use programming to make your life easier. The same results can be achieved with a single line of code.
Example 2: You need to separate even numbers in a list. The most common method is to use the for loop, as shown in the code below.
But, if you are good at programming, you can do the same thing with just one line of code.
List comprehension method
Both codes offer the same result, but it’s better to reduce the code and receive the outputs faster when working with large datasets.
To succeed as a data scientist, you must understand the requirements of the field. Remember these things. Be a demanding data scientist,
● You don’t have to be a statistician or a mathematician- a moderate level of mathematical understanding is sufficient.
● No need to be a developer- instead, be an expert at processing data using basic to moderate level coding concepts.
● No need to be an author– to be that communication expert who can help each of his audience members grasp the recognized insight from the point of view desired by his (audience).
Being a successful data scientist doesn’t mean you have to work for a FAANG/MNC. Just be an extraordinary researcher with a critical mind, basic coding abilities, broad mathematical understanding, and a unicorn storyteller. In fact, with such abilities, not only can you become a highly paid data scientist, but you can also start your own business. You can learn and earn tremendously even doing freelance data science projects.
I shared a data science career secret. Use it to set yourself up for future success.