Data Science is the most emerging career with immense capability to empower practically every industry under the sun. In this hyper-connected world, people are hooked to the internet in more than many ways, uploading their data, stating their queries and acquiring their demand. This deluge of data both structured and in unstructured format from the web, is explored, analyzed with special data tools to give solutions to problems. With the ability to store this data in an efficient and cost-effective manner, every market segment is aspiring to exploit data in order to gain a competitive advantage. Data science has invariably lent its mileage from statistics and forecasting area to impact every business scenario and across all government sectors to provide customized data solutions to increase efficiency, bring profit and cut cost.
How to become a Data Scientist?
You need an undergraduate degree either in Computer Science, Statistics, Mathematics, Engineering or Physics. These subjects pave the way for a successful career in Data Science. Students who love to play with numbers especially Algebra and Calculus and have an inquisitive personality are most suitable for this career. Most data scientists either have a Master’s degree in Mathematics and Statistics or Computer Science or Engineering.
• Mathematics courses that emphasize algorithm analysis, linear algebra and data structures
• Applied statistics including probability distributions, hypothesis testing, and regression
• Computer Science courses related to database management and administration
• Programming courses in programming languages like Java, C++, R, and Python
There are many massive open online courses (MOOCs) providers like Coursera, Udacity, and edX that provide online data science courses, however, the most coveted certifications in data science are:
• INFORMS: Certified Analytics Professional
• Cloudera: Cloudera Certified Professional (CCP): Data Scientist
• Columbia University: Certification of Professional Achievement in Data Sciences
• Digital Analytics Association: Web Analyst Certification
• EMC: Data Scientist Associate Certification
• SAS: Certified Big Data Professional
• SAS: Certified Data Scientist
• TDWI: Certified Business Intelligence Professional
Given the high demand, there are some data specific courses that are being offered at the undergrad level. These courses build your strength in every department from applied statistics to giving hands-on training in using core analytic tools.
Data Science courses offer the following key software skills:
• Programming languages like Python, Perl, C/C++, and Java.
• Big data software platforms like Hadoop, Hive, and Pig.
• In-depth knowledge of at least a few analytical tools like SAS and R
• Knowledge of big data visualization tools like Tableau
• Familiarity with cloud tools like Amazon S3.
• Coding capabilities in SQL Database
With the advancement in data technologies, organizations hire data enhancement team with backgrounds in applied and business statistics, data science, and programming. The data is analyzed, and the results tweaked at every level to find a full-proof solution to the problem. In the business sector, data scientists work with the marketing and service team to understand the market, if they are working on data targeted to boost sales of the company. Likewise, a clinical data analyst works with doctors and research labs to develop customized treatment solution for patients based on their medical history.
Data scientists are investigators who work on data and should be curious enough to ask the right questions in order to understand the problem. Once you gain clarity, then you can look for an appropriate analytical approach to solve it. Critical thinking and being argumentative boosts your ability in exploiting data to solve your purpose. It is important to remember, that one can learn to use analytical tools but there are certain cognitive abilities that are key for a successful career in data science.
Key cognitive skills to become a data scientist
• Critical thinking
It refers to the ability to scrutinize, assess information objectively and make a rational judgment. You should be judgemental because if you cannot have a preconceived notion you will not know how to model your analysis. Your logical thought process can be the only problem-solving technique that you can materialize with the different software skills. You must have business acumen in order to justify your approach and above all, you must be a good critical thinker in order to be a data consultant.
This is the foremost trait that is in demand in any scientist. Your inquisitiveness will help you inquire thoughtful questions about the data, what does the data represent, what does it mean. Your curiosity will guide you in making the right decision while creating your analytical model. It will help you build your assumption and hypothesis as you explore your data for viable answers. You should have an open mind when working with data, there are times when you need to unlearn and reframe your predictive model. It is like finding a needle in the haystack, so you need to be patient and hardworking.
• Good Communication skills
Once you have the results in hand, you need good storytelling skills in order to make it appealing before your client. In today’s competitive world, presentation skills are part and parcel of this career. You must be able to explain effectively how you came to the result and be able to justify why your solution should be utilized.
Ability to work in a team is crucial because you coordinate with practically all verticals of the business in order to understand the problem at hand. You work with developers in order to translate your solution into viable data algorithms and marketeers to design result driven campaigns.
Do you like maths and have a logical aptitude? However, you can be a successful data scientist when you also have the above creative skills. Online Aptitude tests have become a litmus test for students when deciding about their career. These psychometric tests assess your personality and your inborn traits and find work areas where they are most applicable.