How to become a Data Scientist – A 10 point checklist
The buzzword today and a highly lucrative career option – Data Scientist. A data scientist is a highly coveted professional with advanced skills in data tech. Usually, you find people with MS/Phd degree who’d be recognized in the industry as Data Scientists. At the same time, in the ever evolving and dynamic world of data, one needs to be abreast with every latest innovation/breakthrough. Hence, you find most of them almost always involved in one or the other online course for professional growth.
For the novices, who wish to make career in this field, we’ve come up with a sure-shot 10 point checklist, which will enable you to begin your baby steps in this direction.
- FORMAL EDUCATION – All data scientists you’re likely to meet are on the upper spectrum of education. 80% of them are qualified engineers while the others may have a masters in subjects like computer science, statistics or Mathematics. Having said that, though it is a norm, its not a qualifying criterion.
- BASIC ANALYSIS USING MS EXCEL – As cliché as it may sound, it’s a huge advantage to have your grasp on analysis using advanced MS Excel. It lays a foundation of the career you’re charting for yourself. Especially with additional features which Microsoft has armed Excel with, it can give you beautiful analysis reports with very less efforts. Learn Basic to Advanced MS Excel here.
- SQL PROGRAMMING – Even though NoSQL and Hadoop have become a large component of data science, it is still expected that a candidate will be able to write and execute complex queries in SQL. This is because, SQL still serves as the foundation of database management – you can access, communicate and perform simple tasks using SQL on data-sets. Its very much like statistics as a base for data science.
- PROGRAMMING or CODING SKILLS – Currently, the flavor of the market is Python (according to sources), but you can take your pick from SAS or R as well.SAS has been the undisputed market leader in the enterprise analytics space. It offers a huge array of statistical functions has a good GUI for people to learn quickly and provides brilliant technical support. R and Python are the two most popular programming languages used by data analysts and data scientists. Both are free and open source, and were developed in the early 1990s—R for statistical analysis and Python as a general-purpose programming language. For anyone interested in machine learning, working with large datasets, or creating complex data visualizations, they are godsends. Learn to code in SAS, R or Python.
- DATA VISUALISATION TOOLS – The business world produces a vast amount of data frequently. This data needs to be translated into a format that will be easy to comprehend. As a data scientist, you must be able to visualize data with the aid of data visualization tools such as Matplottlib, Tableau or MS Power BI . These tools will help you to convert complex results from your projects to a format that will be easy to comprehend. The thing is, a lot of people do not understand serial correlation or p values. You need to show them visually what those terms represent in your results. Learn data visualisation using Power BI.
- PLATFORMS LIKE HADOOP – Although this isn’t always a requirement, it is heavily preferred in many cases. Having experience with Hive or Pig is also a strong selling point. Familiarity with cloud tools such as Amazon S3 can also be beneficial. A study carried out by CrowdFlower on 3490 LinkedIn data science jobs ranked Apache Hadoop as the second most important skill for a data scientist with 49% rating. As a data scientist, you may encounter a situation where the volume of data you have exceeds the memory of your system( The BIG-DATA) or you need to send data to different servers, this is where Hadoop comes in. You can use Hadoop to quickly convey data to various points on a system. That’s not all. You can use Hadoop for data exploration, data filtration, data sampling and summarization.
- MACHINE LEARNING – A large number of data scientists are not proficient in machine learning areas and techniques. This includes neural networks, reinforcement learning, adversarial learning, etc. If you want to stand out from other data scientists, you need to know Machine learning techniques such as supervised machine learning, decision trees, logistic regression etc. These skills will help you to solve different data science problems that are based on predictions of major organizational outcomes.
- ARTIFICIAL INTELLIGENCE – A lot of people confuse AI with some simple and available applications. AI is much more and much deeper. Probably that’s why its called Deep Learning. AI tools will teach you advanced applications which will give amazing boost to your career as a data scientist. Though not recommended in the beginning of your career, we will surely suggest you to go for these courses at some point in your career.
- ABILITY TO WORK WITH UNSTRUCTURED DATA – Unstructured data are undefined content that does not fit into database tables. Examples include videos, blog posts, customer reviews, social media posts, video feeds, audio etc. They are heavy texts lumped together. Sorting these type of data is difficult because they are not streamlined. Most people referred to unstructured data as ‘dark analytics” because of its complexity. Working with unstructured data helps you to unravel insights that can be useful for decision making.
- BUSINESS ACUMEN AND COMMUNICATION SKILLS – Although you may get sceptical about this point, as most of the data scientists tend to keep to themselves behind a wide computer screen, the fact is, developing a business acumen and good communication skills are indeed of great importance if you wish to progress . To be a data scientist you’ll need a solid understanding of the industry you’re working in, and know what business problems your company is trying to solve. In terms of data science, being able to discern which problems are important to solve for the business is critical, in addition to identifying new ways the business should be leveraging its data.
Companies searching for a strong data scientist are looking for someone who can clearly and fluently translate their technical findings to a non-technical team, such as the Marketing or Sales departments. A data scientist must enable the business to make decisions by arming them with quantified insights, in addition to understanding the needs of their non-technical colleagues in order to wrangle the data appropriately.
We have tried to compile the list of most needed skills which will not only help you secure an exciting job as a data scientist but also make you secure a long and fulfilling career in it.
We are most open to add/edit this list. Do provide us suggestions/views on the article.
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