Researchers suggest that one of the most high profile jobs in this modern age is of a data scientist. With the tremendous response for Big Data technology, more and more companies in almost all verticals are readily embracing this new method of analytics to drive in more competitive approach. This is why, there has been a significant rise in people opting for data science as an academic discipline, which is a well salaried profession too. Many analytics professionals pertaining to various IT roles are adapting to ingrain new technical and non-technical skills as companies are looking for seasoned professionals who can make their Big Data project a successful venture.
A data scientist is often looked as a professional who is better in statistics in comparison to any software engineer and is better in software engineering than any statistician. A company expects its data scientist to apply their analytical skills, industry know-how, contextual understanding and work on existing set of assumptions to unravel out of the box solutions for counter-attacking emerging challenges. On any given day, a company may ask their data scientist to pull off these job roles:-
1. Collect massive amounts of data from internal and external sources.
2. Completely clean and filter data for eliminating unwanted information.
3. Initiate an undirected research and come up with industry related questions without definite limits.
4. Prepare data for using in predictive and prescriptive modelling by deploying advanced analytics programs, machine learning along with statistical methods.
5. Brainstorm for finding unique data driven solutions and intelligently using various Big Data tools to tackle critical business risks and challenges.
6. Fully exploit the data from various dimensions to ascertain possible strengths, weaknesses, threats and opportunities.
7. Creatively use new algorithms and build customised tools to solve problems and keep the work on auto-pilot mode.
8. Timely convey the data predictions and research findings to the IT and concerned management for taking timely actions.
9. Come upfront to suggest cost efficient methods and proposing changes to be made in the implementation of various Big Data tools to make wise amendments in existing strategies or work processes.
It is often perceived that becoming a data scientist requires you to be a master of various trades such as data munging, databases, software development, machine learning and statistics along with data visualization. But apart from these technical skills, companies look for various other non-technical competencies, which in still their confidence that the candidate has necessary soft skills and solid understanding of the industry requirements.
1. Basic Tools
Irrespective of the scale of operations of the company you apply for, being adept in the basic analytics tools is an indispensable necessity. You must be well versed with statistical programming languages like Python, R, SQL database or coding. Having knowledge of the popular components of data science, NoSQL and Hadoop will give you added preference. Java has also emerged as one of the most demanded skills as the immensely popular Hadoop is written in Java programming language. However, Apache Spark and Scala are also garnering popularity these days but are not yet included in the programming languages necessary for getting job as a data scientist.
2. Basic Statistics
In addition, basic understanding of statistics is mandatory while applying for data scientist job. Familiarity with maximum likelihood estimators, distributions, statistical tests, etc. along with statistics applied in machine learning is important. All types of companies requiring data scientists will expect their candidate to be an expert in basic statistics, specifically companies that don’t have data focused products, depend on the data scientists for providing insights that stimulate the decision making process in various departments.
3. Multivariable Calculus & Linear Algebra
While being interviewed, you may be asked to derive statistics or machine learning results in different situations or may be asked questions regarding multivariable calculus or linear algebra, as they form a crucial part of the above techniques. Despite having readymade implementations in sklearn or R, you may be required to have thorough knowledge of this stuff as companies may require customised in house built implementations. Clear understanding of these vital concepts is essential in companies where product is data driven and minor tweaks in the predictive performance and optimisation of algorithms made by you can rope in more profits for the company.
4. Machine Learning
When you are mulling to join a company, which collects huge volumes of data or where the product is data driven, then it becomes necessary for you to be a master in machine learning. Machine learning buzzwords like k-nearest neighbours, ensemble methods, random forests, etc., must be familiarised with. Knowing loads of contemporary Big Data handling techniques and rich understanding of algorithms can land you on your dream job.
5. Data Munging
Dealing with imperfections in data must be an area of expertise for you. When you start analysing data, they may be in various forms that may be messy and include missing values, different date formatting, inconsistent string formatting, etc. This is a must have skill when you are working in companies in its initial stage or where the product is not data related.
6. Data Visualisation & Communication
Newly found companies who are taking decisions based on data for the first time or companies where data scientists are entitled to help other departments in taking data driven decisions, thorough visualisation and communication of data is a major necessity. You must be able to confidently present your findings (whether technical or non-technical) or the way the Big Data techniques work to the audience. On the visualisation front, a data scientist must be an expert in using data visualisation tools such as d3.js and ggplot. Apart from being familiar, you as a data scientist must know the principles that form a base for effectively encoding data and communicating this information.
7. Software Engineering
Having a strong software engineering background becomes critical for a business that is at young stage. Initially, you will be solely responsible for designing the Big Data project, handling data logging and developing data driven products to strengthen the IT base of the organisation.
1. Thought Process Of A Data Scientist
Companies want their data scientist to have a creative approach, who must be confident in solving crucial data related problems. You may encounter some mind boggling case studies while interviewing, where you will have to exhibit your skills while innovatively dealing with hostile situations. You must be able to analyse what areas of business needs to be tackled first on the basis of the Big Data analytics results and must find quick and reliable ways of communicating to the right authority.
2. Intellectually Curious
It is a generally accepted notion that intelligent data scientists are participative in a way that they are motivated out of curiosity not only for answering questions, but they don’t hesitate in raising their queries. When they are intellectually curious, companies are benefitted as their data scientist uses information efficiently in making meaningful data discoveries.
3. Extensive Knowledge Of The Industry
Having a strong hold on the industry basics is crucial for any data scientist as it helps the companies in analyzing the upcoming threats and opportunities beforehand. Data scientist is in a better position to suggest implementation of new techniques and synchronize the existing resources for yielding better results in the concerned areas. In addition to helping businesses solve critical problems, data scientists must be well versed with the techniques of discovering newer ways for leveraging the data insights.fbase of the organisation