Certificate in Advanced MS Excel
Coupon code: ADVANCEXL | Offer price: 3840/-
Home > Blogs > 7 Tips from a Data Scientist to Data Analysts
The recent boom in the data industry has driven the demand for data science professionals at enterprise-level, across all industry verticals. There are job openings for data scientists, data engineers, and data analysts. And there seems to be a lot of confusion and varying opinions among people regarding the roles and skillsets driving this field. Although all these job titles sound similar and are related to data the devil is in the details.
Unfortunately, there are no defined skill-sets that can distinguish between the role of a ‘Data Scientist and Data Analyst. In fact, different companies have different definitions for both these roles, and there is a lot of grey area in between the two job titles.
Broadly analyzing, a Data Scientist is a professional who combines data handling and data visualization with sound business understanding to make smart business decisions. A data scientist is expected to deliver business impact and take insights from the raw, chaotic data thereby uncovering answers to the problems we did not know existed. Data science as a job profile demands skills such as data structuring, data mining, data visualization, analytical skills, programming skills, machine learning skills, and customer insights
The role of a data analyst, on the other hand, is to summarize data and provide futuristic inputs by identifying consistent patterns from the past and the current data. The primary role of a data analyst is to collect, curate, process, and arrange data from different sources. They are responsible for presenting data in the form of charts, graphs, and tables and use this structured data to build relational databases for companies.
The difference between skill-set, scope, and goals of data science and data analytics can be well understood from the image below -
Although there is a difference in the job responsibility of a data scientist and a data analyst, these two fields are exceptionally interconnected. They often work in close coordination to achieve the same goals i.e. of growth and development. For someone who aspires to become a data analyst, it is essential to understand the nuances of data science. And to help you with that, we bring you some solid advice from our star data scientist, Gunjan Narulkar.
Gunjan is currently working as the Chief Data Scientist at Data Semantics Pvt Ltd. As the head of the Hadoop and Predictive Analytics division at Data Semantics, he has a broad range of experience working with both data scientists and data analysts. And here are some word of advice from the expert data scientist for data analysts.
Numbers have an important story to tell. They rely on you to give them a clear and convincing voice.” –Stephen Few
Too often data storytelling is understood as effectively presenting data with visually-appealing data charts. However, data storytelling is much more than that. It is the art of weaving a rational story with clear logic that can strike the right chord with the stakeholders and give them enough insights to drive a decision.
More than the data presented, it depends on how the data is presented to a non-technical audience. Data storytelling follows a structured approach that involves a combination of 3 crucial elements, which are data, visuals, and narration.
As a data analysts, it is important that you learn the art of storytelling. The key skills required in a great storyteller are:
i) Knowing the audience and weaving the story to their understanding
ii) Clearly understanding the business problem and the solution derived
iii) Getting the right data at hand
iv) Strong presentation skills
v) Analyzing probable questions and preparing answers for them
Most top-notch data scientists code a lot and are comfortable handling a variety of programming tasks. To be a really successful data science expert, your programming skills should be a combination of computational and statistical abilities. You should be able to handle a large volume of real-time data and apply statistical models like clustering, optimization, regression, etc. to it.
Currently, the preferred language among data scientist is Python with the use of other languages such as R, Scala, Clojure, Java, and Octave.
Try to do a dummy project that highlights your strengths. Code wildly and to the point, you lose your sleep. As a data scientist, this will help you grow, learn something new, and most importantly hone your coding skills. Remember, the more toy problems you solve, the better equipped you will be to handle the real ones.
Data is all about numbers. To become a successful data scientist, the first thing you need to do is to get rid of your ‘fear' for number, i.e. mathematics. You can never succeed in your career as a data professional unless you are proficient at mathematics. Period.
As a data scientist, you will be working with a global organization to develop sophisticated financial models. For these models to be statistically and operationally relevant, large volumes of data will be needed. You will need to use your deep expertise in mathematics to develop these models that can shift key business strategies.
Don't think of mathematics as your enemy or get scared quickly by the complexity of the task at hand. Try to develop an intuition for mathematics as you learn about the different techniques and how these techniques can help you solve difficult problems. You can start with a basic course on statistics and mathematics with an enhanced focus on probability, algebra, set theory, functions, and graphs. Once your basic concept is strong, you can use technology tools to design complex financial models.
Domain expertise is something that makes a Data Scientist an expert! Having domain knowledge is not enough. As a data scientist, it is crucial to stay in front of the curve and understand which technology to apply and when. Unwavering focus on the domain helps us to understand the real problem which empowers us to create solutions that are useful on the ground, and not just "useless innovation".
A data scientist should always work closely with the business to measure and prove the effectiveness of the project on the ground. In addition to having an in-depth understanding of the problem, being aware of the latency, bandwidth, interpretability and other system boundary conditions, will help you understand what technology to apply.
A good data scientist is the one having traits of a good problem solver. Sometimes problem-solving needs assumption as you may not be able to test the solution on ‘real data'. To make such an assumption, you will need to bring critical thinking to the forefront and look at the problem from many perspectives. These perspectives give the data science experts a view of what they are supposed to be doing before pulling all the tools so that they can work to completely solve the problem.
Be creative and accepting of "out of the box" solutions because there are way more examples of success than failure using this method.
Many people entering the field of data science have this pre-conceived notion that data science is all about mathematics and statistics and they hone their ability to think that way. While learning new skills are essential, it is also vital that you work on sustaining your current skills as well.
In current times, the use of data science has found a broader horizon. And a broader horizon needs a wider knowledge in its ability to execute, and that is why the more things you know, the better it is for you. Remember your experience and contribution as an individual is what will help you climb up the corporate ladder.
One of the best approach to have a full-fledged career in Data Science is to pursue a certificate program/course that provides you a 360-degree knowledge, resources of portfolio preparation (capstone projects) and curriculum that covers the A-Z of Data Science. For example, courses like Manipal ProLearn’s Data Science course covers all those useful resources with its in-depth curriculum and practical learning methodology and helps you build a solid portfolio required for a career in Data Science. From beginner’s data science courses to PG diploma in data science, Big Data, Data Analytics, Machine Learning, etc., the choices are many. These courses can be done remotely and in addition to any degree, you are pursuing currently.
Also, once you’ve pursued an awesome course like the one listed above, what next? It’s essential for you to stay connected with Data Science resources - whether it be Popular Blogs, Podcasts, Useful Textbooks, Tutorials, or Video Channels.
Remember: Books are classic, but when it comes to fields like Data Science, AI/ML and Coding, it is the practical approach training that helps you uplift your skills!
A great data scientist is someone with the intelligence to handle data processing and an intuitive understanding of the business problem. While people with good maths skill can easily do the first part, the difficult part is to delve deeper into what you are doing. Someone with a deeper understanding and intuition of the model they are working on is likely to have a successful career in this field.”
And that’s a wrap! Hope this blog post proves to be insightful for you! Feel free to share your thoughts in the comments section! Also, If you seek to upskill your Data Science skills, feel free to check out our Data Science Courses here.