What if we say this is the perfect time for being a data scientist?
Many job sites are increasingly posting the need for data scientists. In recent times the demand has grown up by 344% ever since 2013 which is definitely a huge number. This demand will continue to surge as we move towards a world where data will be the most important asset and organizations will have to constantly monitor data-driven insights.
Growing Significance of Data Scientists
Data scientists serve a vital role in both acquiring and retaining the new-age customers who are truly empowered due to the increased prevalence of the internet.
It is because of these professionals that data scientists can turn the maximum amount of data that has been sourced by the businesses into action. Although the need for data-driven analytics was always high it is only in recent times that their significance is being recognized by almost everyone.
With the growing prevalence of data in different fields, its applications are also diversified. Hence, data scientists can now make an impact almost anywhere in the organization.
So if you are looking at a career as a data scientist, then there are some skills that you need to master.
Here we are going to discuss the primary skills that will be needed by the data scientists to be successful in the field.
Essential Skills for being a Successful Data Scientist
Although we all know that education is important but to be an expert in the field, you will need to have skills that are beyond the realm of data science.
- Ability to Think Critically
As a data scientist, you need to have a keen vision and should be able to read between the lines and go beyond the boundaries to get to the right solutions.
Although most professions these days need a person to have that critical approach in this field the need becomes even more pronounced. You should be able to approach a problem in multiple ways and be able to come with the most relevant questions.
It is with that critical thinking approach that you will be able to draw similarities with different solutions and your organizational requirements. Objective data analysis becomes important when you deal with data interpretations in order to form an opinion.
Critical thinking also fuels curiosity that is essential to stay ahead of the growth curve.
This is another skill-set that finds relevance in almost all professions but when it comes to being a data analyst the significance cannot be understated.
Imagine, you being an able data scientist who can draw extremely powerful and unique insights from absolutely raw data. Such insights that cannot be matched even by a seasoned data analyst. However, you are not good at communication.
So, no matter how much you know or have been able to find out, you are not able to communicate it effectively neither to your team or your management. Basically, the effort turns in drag on resources and time.
Alternatively, there is another data analyst who has average expertise but is great in communication skills. He might not be able to play with data and numbers but definitely be able to play with words.
Now, you realize that having a skill that you are not able to exhibit is actually of no use to any organization. As an analyst, you will be required to constantly coordinate with the marketing team, development team and also management.
So, these form a group comprised of people from both the technical and non-technical fields. You need to cater to all.
As a data scientist, you need to have great problem-solving skills. This will form the core of your work every day. You will have to give meaning to absolutely raw data.
Additionally, you will also be required to give solve many problems on a daily basis. This means that you should have the knack to dig deeper into an issue to address the root of the problem before you jump to a solution.
A habit of in-depth analysis pays off in this field. A person who has that inbuilt ability to solve problems is able to identify the tricky parts that are hidden.
- Business Sense
A good data scientist needs to have a natural business sense. It is important for them to not only know their own field but also know the industry, the market and also the target segment of their business. This information helps to reach the right solutions.
This will help them deeply understand the data in sync with the vision and mission of their organization. Data science is thus much more than the mere crunching of numbers and turning it into patterns.
It involves giving meaningful insights to take the business to new heights and identify all the possibilities of growth.
- Python programming
Python has emerged as one of the most common and popular coding languages. It is thus important for data scientists to have apt knowledge of Python along with other languages like Java, Perl, C/C++.
Python can be used in almost all the steps of data science due to its flexibility and ease of use. It can also work on different data formats so that various SQL tables can be easily imported. It helps in the creation of data sets.
This may not be always required but is highly preferred in many cases. If at all you have experience in working with Hive or Pig, this will be a great add-on.
You should have great familiarity with many tools of cloud-like Amazon S3. Apache Hadoop has been ranked as the second most important skill for every data scientist by CrowdFlower.
You will come across many incidences and situations where the data encountered by your system memory is much more than its capacity. In such calling times, you will be required to spread your data across different servers. It is here that the role of Hadoop comes in.
Hadoop can be used to convey data to different points. It is also useful in data exploitation, filtration, sampling and also summarization.
- SQL Database
Every data scientist should be able to write and even execute complex SQL queries. SQL has been typically designed to help you to manipulate data in multiple ways.
Not only this, but it also leads to significant insights when you use it for querying a database. The precise and short commands help in saving a lot of time and effort that go into programming.
- Apache Spark
This has become highly popular big data technology across the world just like Hadoop with the only difference between the two is that former is faster than the latter as unlike Hadoop, Spark caches its computations in memory.
Hence in data science, Apache is useful as it helps in running these algorithms faster especially when you have to deal with massive data loads. Even data loss is reduced significantly.
- Data Visualization
A huge amount of data is produced every day which has to be transformed into meaningful patterns to draw real insights. These are the insights that will help to frame business strategy.
As a business analyst, you will be required to often visualize data with the help of tools like ggplot, d3.js, and even Matplottlib. You can make use of these tools for the conversion of complex results into an easily comprehensible format.
The real benefit of correct data visualization is that the top management is able to see the real picture of the market trends and analysis.
- Machine Learning and Artificial Intelligence
Both Machine Learning and AI are technologies of tomorrow. Going forward in the future, proficiency in these will give you an edge over other data scientists.
Many techniques of machine learning are very helpful in solving many problems of data science-primarily based on predictions of major organizational outcomes.
The role of a data scientist is gaining prominence with the growing amount of data that is being generated every day.
A huge amount of this data is unstructured and needs to be transformed into clear patterns so that one can draw clear meaning from it.
The role of any data scientist is to work on burgeoning amounts of data, draw clear patterns and trends and give inferences from it. Such insights help in business strategy formulation.
Many businesses are now hiring data scientists and it has been predicted that in future the demand will go high. In addition to formal education, you need to have the above skills for excelling in the profession.
Remember, anyone can be a data scientist but to be a good one you need to have that edge that will cut you different from the others. These skills will help you gain that advantage.
We hope the information was useful to you. Also, stay put and don’t stop till you meet your goals. Do share your experience and journey with us.
Digvijay Upadhyay has over 5+ years of experience as a Data Scientist at JanBask Training. Additionally, I am providing online training to professionals and writing technical and inspiring or helpful blogs related to Data Science, Business Analytics, Machine Learning, business intelligence etc.