If you like to solve problems, here’s one: What would you do if your job becomes obsolete in the next decade? Would you upskill your knowledge or work harder in the same job role? Before coming up with an answer, you must know that the average income of Data Scientists is 50 percent higher than that of other IT professionals because the demand for Data Scientists in all industries has increased by 417 percent over the last year. Thus, you do not need to be a manager or a lead to earn big bucks; just acquiring the top data scientist skills will do.
This blog is divided into the following two sections, which will give you the gist of its context.
- What do Data Scientists do?
- 10 Technical/Non-technical Data Scientist Skills to Master
- Statistics
- Programming
- Machine Learning
- Linear Algebra and Calculus
- Data Wrangling
- Data Visualization
- Big Data
- Data Intuition
- Storytelling
- Collaborative Skills
To understand the implications of Data Science, let us go to the 90s when Java came with a bang. Every industry jumped to hire Java experts. Even a basic knowledge of Java ensured a job. However, slowly, Java professionals had to learn CSS, JavaScript, and more.
The reason?
The job role was redefined. Similarly, Data Science is the next major technology that will define/redefine the world’s work culture and economy. Thus, by setting the tone for the world market and triggering a data-driven revolution, Data Science will affect each sector and every life.
What Is Expected from a Data Scientist?
As a Data Scientist, you should be able to:
- Find challenging Data Analytics issues
- Showcase your Data Scientist skills on Big Data platforms and analytics tools to gain business insights
- Evaluate the available data sources and propose strategic procurement of new data sources to support a better way of problem resolution
- Support data collection, integration, and retention requirements based on the collected data
- Identify/create appropriate algorithms to solve business problems
- Craft experiments to corroborate your assumptions and make available scenario models as and when necessary
- Collaborate with business stakeholders to identify their business needs and convey the expected results
- Communicate data insights in a simple story to empower the decision-makers to act on
Top 10 Technical/Non-technical Data Scientist Skills You Must Have
Statistics
For a data-driven organization, your stakeholders depend on your data scientist skills to help them with decision-making. Statistics provide the necessary methods to dig deeper into data and gain valuable insights from them. In addition, the more statistics you know the more you will be able to analyze and quantify the uncertainty in a dataset. Thus, knowledge of statistics is one of the most important Data science skills for a data scientist, and hence critical for you to make a transition into a data scientist role.
Watch this Data Science Course video to learn more about the data science concepts:
Programming Knowledge
The most important skills of data scientists are procuring, cleaning, munging, and organizing data. To do this, they use statistical programming languages such as R and Python.
Over 50 percent of the Data Scientists are well-versed in R and/or Python. You can also choose other programming languages such as MATLAB, SQL, Java, etc.
Machine Learning
One of your main responsibilities as a Data Scientist is to identify business problems and turn them into Machine Learning tasks. When you receive datasets, you can use your Machine Learning skills to feed the algorithms with data. ML will process these data in real-time via data-driven models and efficient algorithms. Soon, the machine will learn and predict the data pattern and produce accurate results.
If you work in a large data-driven enterprise, you must know the ensemble methods, random forests, k-nearest neighbors’ algorithms, and so on.
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Linear Algebra and Calculus
If you equip yourself with the concepts of linear algebra and calculus, you can make minute improvements in the algorithm to significantly impact the end result. Though learning them is not a necessity, a few companies that churn out a large number of data, like Netflix, Amazon, etc., always look for Data Scientist candidates with exemplary linear algebra and calculus skills.
Data Wrangling
As a Data Scientist, the data you analyze is often confusing and difficult to deal with. Therefore, it is important to understand how to handle errors in a dataset. Corrupted data may be missing some regular values or may not have the required format.
Using data wrangling, you can remove corrupted data and sort it accordingly. The ability to process and use data for analytics is one of the most important skills of a data scientist.
Data Visualization
Communication of data is important for the stakeholders to make data-driven decisions. This means that you have to describe how your findings work out for the end audience, including technical and non-technical professionals. Thus, to do this, you need data visualization skills, which comprises data visualization coding and information transmission. You can start with data visualization by learning various data visualization tools such as Matplotlib, ggplot, and Tableau covered in our Data Analyst Course.
Big Data
Data Scientists deal with many confusing data samples that include structured and unstructured datasets. They use their data wrangling, programming, and other skills for a data scientist to clean, sort and manage them. In this way, they can uncover the hidden solutions to impending business challenges. Thus, as a Data Scientist, you need to interact with Big data and learn how to retrieve, manage, and analyze it.
To interface with Big data, you must use Hadoop or Spark to prepare, distribute, or process data. Most Data Scientists prefer Spark, not Hadoop because it enables fast real-time data processing. However, whichever Big Data tool you use, you have to learn and work on data exploration, data filtering, data sampling, data summarization, etc.
Data Intuition
When you become a Data Scientist, the organization will want you to be a problem solver and find the best possible solution for a problem statement. In such a case, you need to consider what is important, what doesn’t matter, and how to interact with engineers, stakeholders, and sometimes even end-users. So, how will you do all these?
Call it a business or data intuition, the most important aspect you need to understand is how to apply your data scientist skills and knowledge of math, statistics, programming, Big Data Analytics, etc., to come up with the most feasible solution.
If you have enrolled in a Data Science course, check if the training covers a real-world project. If not, please back out of the course. The projects you work on give you the opportunity to use, implement, and test your data scientist skills. Always remember that a good Data Scientist is not a person sent by God who knows everything. He/she knows what, how, and when to use something, such as an algorithm, to get the best results or value.
Storytelling Skills
One of the most important skills of data scientists that you need to learn is to enable your company’s decision-makers with clear-cut findings. This means you have to translate the quantitative results in the language they understand.
Not only do you have to speak the same language that the company uses but you also need to use data to tell the stories. You need to create a storyline based on the data so that everyone easily understands it. Thus, using storytelling will help you communicate your results to your employer and deliver value.
Collaboration
To become an accomplished Data Scientist, you need to channel your Data Science learnings to accelerate the pace of the output to ensure the sustainable growth of your organization. You cannot do this alone. You have to collaborate with your team (technical and non-technical), stakeholders, and end-users. Thus, if you have the required people skills, you can collaborate with others to observe their pain points and overcome organizational challenges.
According to Indeed, in just three years, the number of Data Scientists’ job posting has increased by 78 percent. According to Glassdoor, Data Scientists ranked first among the 50 best jobs in the United States. Moreover, almost 60 percent of global companies cannot analyze or classify their data. This is why they are in desperate need of Data Scientists. Thus, ‘now’ is the perfect time to start a career and acquire all the skills for a data scientist.
You also need to know that many people are entering this field without having proper Statistics, Machine Learning, and Analytical skills. To avoid this and take advantage of the current Data Science opportunities.