Data Scientists are big data wranglers with rare hybrid of skillset. Having only technical qualifications merely will not help you land a top gig as a data scientist. There are various other skills like computational abilities, communication skills, machine learning, statistics, etc. which are required to become an enterprise data scientist who can provide business value. Don’t worry, to become a data scientist one need not learn about lifetime’s worth of data related information. Wondering how to get your foot on the data science career path? We have compiled a comprehensive list of skills to match the data scientist job role.
Data Scientist-The most in-demand and very hard Big Data Job Role to fill in
As of March 2015, there were close to 60,000 job listings on LinkedIn for the role of Data Scientist and more than 250,000 people already listing themselves as professionals in data science.
“By end of 2018, US will face a 50% to 60% gap between requisite demand and supply of analytic talent.”- McKinsey Study.
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A data scientist study by EMC found that the best source for finding competent Data Science talent is -
By end of 2020, the data generated is expected to be 44 times more than it was in 2009, the demand for data scientists is increasing - to tame the big data wave by making sense of seemingly unintelligible big data. Data science is field of study that turns information to gold. Data scientists are transformative figures in organizations who leverage analytics through data science. Data scientists are gaining prominence amongst organizations who intend to stay ahead of the competition by leveraging big data analytics from the data explosion.
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"Data scientists are involved with gathering data, massaging it into a tractable form, making it tell its story, and presenting that story to others." - Mike Loukides, VP, O’Reilly Media.
"A data scientist is someone who can obtain, scrub, explore, model and interpret data, blending hacking, statistics and machine learning. Data scientists not only are adept at working with data, but appreciate data itself as a first-class product." - Hillary Mason, Data Scientist, Accel.
Must Have Data Scientist Skills
The role of data scientist is more advanced than other big data roles therefore a professional must possess more advanced degrees, experience in data analytics and a good computing background. Having expertise and experience in the skills mentioned below, will create a strong foundation for a prospective data scientist-
Educational Programs for a Data Scientist-
A formal education program for pursuing lucrative data scientist career is a Master’s degree or a PhD. There are notable exceptions to having a formal degree - as any person with an in-depth knowledge in Computer Science and strong educational background can become a data scientist. The most common subjects of study for a data scientist are Mathematics, Statistics, Computer Science and Engineering. Professionals who are not from computer science background need not worry as there are several educational institutions offering undergraduate programs for data science that are similar to computer science degrees.
Technical Data Scientist Skills
To pursue a successful big data scientist career, a professional must master diverse technologies, particularly the open source ones such as R Language, Java, C++,Python Programming, Hadoop and possess a good grasp of various NoSQL database technologies like MongoDB, HBase and CouchDB.
1) Python and R
As already stated in our earlier article, Statistics is the heart of data science programming and thus it is a must for a professional to develop expertise in Python and R language to become an “Enterprise Data Scientist” and not just a data scientist. It is necessary to learn R and Python programming on real big data system landscape like Hadoop, Oracle or SAP HANA so that professionals can build industry use-cases, related to Workforce Analytics, Customer Analytics, and Marketing Analytics using various data science techniques like machine learning, statistical computing, mathematical models, and algorithms.
As data science involves large scale data analysis, exploring large datasets, mining them and accelerating data driven innovation - a data scientist must learn Hadoop ,as it is a popular open source tool for managing and manipulating large datasets from multiple repositories. A data scientist must be familiar with various Hadoop components like Distributed File System, MapReduce, Pig, Hive, Sqoop, and Flume. Experience with Hive and Pig comes as an excellent selling point for data scientists. Experience in cloud tools like Amazon S3 along with Hadoop adds value to the knowledge base of a data scientist.
It is important for a data scientist to work with unstructured data whether it is in the form of audio feeds, video feeds, social media updates or biometric data. Data science majorly deals with analysing unstructured data and thus expert knowledge in various NoSQL databases like MongoDB or HBase is a must - to write and execute complex queries on unstructured data.
Become IBM Certififed Big Data NoSQL Database Developer
4) Machine Learning
A data scientist should have deep understanding on data mining, supervised/ unsupervised learning and pattern recognition. Some of the machine learning concepts that need to be mastered are Neural Nets, Decision Trees, SVM and Clustering. This expertise can be gained by taking a course that helps you get your hands dirty with data and juggle with it.
5)Data Visualization Tools
There is a saying-a picture is worth thousand words. It is necessary for a data scientist to master the skills of communicating data-driven insights in a visually effective manner. Data scientists should be capable of describing the findings in a manner that can be interpreted by both technical and non-technical audience. Thus, in-depth knowledge of various data visualization tools like Tableau, D3.js, and ggplot helps data scientists provide clear insight into their data-driven insights.
Non-Technical Data Scientist Skills
1) 3 C’s-
The role of data scientist is strongly driven by the 3 C’s- Curiosity, Common Sense and Communication Skills. In most cases the organization is not aware that it has a data driven problem, but the curiosity of a data scientist can bring in opportunities for deriving meaningful insights from data. To formulate any problem definition or hypothesis, common sense and business, domain knowledge of a data scientist play a vital role.
A great data scientist communicates with various people in an enterprise to ensure that the course of action for a given problem is on the right path. Organizations are in search of data scientists who can fluently and clearly convey the technical findings of a data-driven problem to non-technical teams.
A data scientist has to communicate and understand application requirements, business requirements, find out patterns and relationships between the mined big data and convey them to the marketing group, corporate executives and development teams. And to get all these things done the right way, a data scientist must have storytelling skills so that he/she can use the data to cogently tell a story effectively that is easy for everyone to understand.
A data scientist does not merely look around and play with data. A great data scientist must be innovative and creative with his/her thinking capabilities. He/She should have an eagerness to learn more and find out novel things with his/her out of box creativeness. The creativity of a data scientist helps them determine where data can add value and bring in profitable results for an organization.
3) Data Intuition
To become a successful big data scientist, it is not just enough to master technical skills but it is mandatory for a data scientist to have intuition about data. A good data scientist is not one who just inputs all possible features into a machine learning model and analyses the output. The foremost thing a big data scientist must do before giving inputs to the machine learning model is think if the data makes sense. The various kind of questions that a big data scientist should think of are-
- Which machine learning model should they use based on the data distribution?
- What does it mean if a data point is missing and what is the action they can take to deal with a missing data point?
- Are the features useful and do they actually intend to convey what they are meant to?
The answers to all such questions vary, based on the kind of problems a data scientist is solving and the manner in which data is logged. A successful data scientist has to look for all possible scenarios and adapt to them.
4) Business Expertise
Data scientists need to possess strong business expertise in the industry that they are working in, to gain a better understanding of what problems the company is trying to solve. The field of data science requires identifying the problems that are critical for a business and what are the new strategies that can be adopted to leverage the data to solve those problems.
A good equation for success in the field of data science is a combination of various educational programs, technical skills, and non-technical skills conjoined with years of experience. It is definitely not easy to land a gig as a Data Scientist with so many skills to master, particularly if professionals are keen on getting into top-notch IT companies.
With tough competition and even tougher skills to master, it is not very to become a Data Scientist. Go beyond taking Statistics and Math courses, participate in Hackathons or provide solutions to startups by tackling real world big data problems that they might have.
If you are really excited to get into a Data Science role, and wish to gain practical experience, then DeZyre has all day learning events coming up, that will allow you to build projects in Data Science using Python and R. Stay updated on your big data scientist career with Hackerday.
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