by Ronald van Loon
Leveraging the use of big data, as an insight-generating engine, has driven the demand for data scientists at enterprise-level, across all industry verticals. Whether it is to refine the process of product development, help improve customer retention, or mine through the data to find new business opportunities—organizations are increasingly relying on the expertize of data scientists to sustain, grow, and outdo their competition.
Consequently, as the demand for data scientists increase, the discipline presents an enticing career path for students and existing professionals. This includes those who are not data scientists but are obsessed with data, which has left them asking:
What skills do I need to become a data scientist?
This article aims to answer this question. We will dive into the technical and non-technical skills that are critical for success in data science.
- If you are a potential data scientist, you can use the information herein, to carve a successful career for yourself in data science.
- If you are a data analytics director at an organization, you can leverage the information to train your existing team of data scientists, in order to make them more productive and efficient at their work.
This is an address for all those who love to wrangle and rumble with Big Data.
Technical Skills Required to Become a Data Scientist
Statistical analysis and the know-how of leveraging the power of computing frameworks to mine, process, and present the value out of the unstructured bulk of data is the most important technical skill required to become a data scientist.
This means that you need to be skilled at math, programming and statistics. One way of complying with the prerequisite is to have a resonating academic background.
Data scientists usually have a Ph.D. or Master’s Degree in statistics, computer science or engineering. This gives them a strong foundation to connect with the technical points that form the core of the practice in the field of data science.
There are some schools that now offer specialized programs, tailored to the educational requirements for pursuing a career in data science.
Those who don’t want to opt for this focused-but-extensive approach, can pursue other options. This includes focused Massive Open Online Courses (MOOCs) and boot camps. Some program-offering-options worth exploring are Simplilearn’s Big Data & Analytics certification courses. They can help deepen your understanding of the core subjects that support the practice of a data scientist, while also providing a practical learning approach which you will not find in the confines of the textbook.
Other technical skills required to become a data scientist include:
1) Programming: You need to have the knowledge of programming languages like Python, Perl, C/C++, SQL and Java—with Python being the most common coding language required in data science roles. Programming language helps you to clean, massage and organize an unstructured set of data.
2) Knowledge of SAS and other analytical tools: The knowledge of analytical tools is what will help you extract the valuable insights out of the cleaned, massaged, and organized data set. SAS, Hadoop, Spark, Hive, Pig and R are the most popular data analytical tools that data scientists use. Certifications can further help you to establish your expertise in the use of these analytical tools.
3) Adept at working with unstructured data: When talking about the skill of being able to work with unstructured data, we are specifically emphasizing the ability of a data scientist to understand and manage data that is coming unstructured from different channels. So, if a data scientist is working on a marketing project to help the marketing team provide insightful research, the professional should be well adept at handling social media as well.
Non-Technical Skills Required to Become a Data Scientist
We will now shift our focus towards non-technical skills, that are required to become a data scientist. These skills are part of a candidate’s persona and as such can be difficult to assess simply by looking at educational qualifications, certifications and so on.
1) A strong business acumen: If a data scientist does not have business acumen and the know-how of the elements that make up a successful business model, all those technical skills cannot be channeled productively. You won’t be able to discern the problems and potential challenges that need solving for the business to sustain and grow. You won’t really be able to help your organization explore new business opportunities.
2) Strong communication skills: You are a data scientist and understand data better than anyone else. However, for you to be successful in your role, and for your organization to benefit from your services, you should be able to successfully communicate your understanding with someone who is a non-technical user of data. You need to have strong communication skills as a data scientist.
3) Great data intuition: This is perhaps one of the most significant non-technical skills that a data scientist needs. Great data intuition means perceiving patterns where none are observable on the surface, and knowing the presence of where the value lies in the unexplored pile of data bits. This makes data scientists more efficient in their works. This is a skill, which comes with experience and boot camps are a great way of polishing it.
Data Scientists – The Unicorns
Shashi Upadhyay, the CEO of Lattice, the provider of AI-enabled big data inference engines, once referred to data scientists as unicorns, calling them “professionals with a diverse skill set that is not commonly found in a single individual.” This explains why data scientists are so valued, and why becoming one is so challenging. But, it is not impossible.
At least for the likes of us, who love wrangling and rumbling with data, nothing is impossible!
About the Author
Ronald van Loon is an Advisory Board Member and Big Data & Analytics course advisor for Simplilearn. His company, provides Big Data & Analytics certification courses, along with other leading certification programs.