Self Evaluation – Making the Right Decision to Becoming a Data Scientist

DataScience
Aug 16, 2015
Self Evaluation – Making the Right Decision to Becoming a Data Scientist

Self Evaluation – Making the Right Decision to Be a Data Scientist

The Data Scientist, Data Manager, Statistician and Data Analyst are the most popular career paths. Now, top universities have introduced courses in Data Science, due to the popularity of jobs in this field. People are drawn to the data scientist profession without analysing the responsibilities of the job, positive attitude, business and technical skills. Those who simply jump into this profession for being it very lucrative without acquiring the right skills and qualifications, they experience lots of troubles in the long run. For a clearer image of the data scientist role, in order to judge if their personality is suited, you must also assess your industry experience. It helps you figure out what exactly are your most sellable skills that are required by various employers.

Self Evaluation

Before you start pursuing different job opportunities in the field of Data Science, you may evaluate yourself to ensure that you are a qualified candidate. Listing down your own qualifications and expertise will help you a lot. Also browse through the job descriptions you want to apply for and find about the environment of the workplace. After you have listed all these details, you can easily prepare to present yourself in the best manner.

Few Steps for Self Examination

  • Get prepared for an assessment and visualisation of skills, and abilities, involving an interview and quantitative research.
  • You may have to look at terms and job requirements to define them.
  • Also compare / contrast, and demonstrate structuring of benefits of models and any quantitative methods which may benefit the research while you are on the go
  • Present a reorganisation pitch which may involve redundancies.
  • Consider quantitative research interviewing departments opposing your idea, for metadata and for unreleased datasets. Get your mind ready to face the challenges
  • Create an app to assist in your assessment and this is the best way to portray your talent to your prospective employers. If you can do this, for sure you can sell your expertise at best price
  • Try some troubleshooting for a code of 2000 lines, without assistance. This is one of best practices you can make before you are assessed during a test by an employer
  • Try an ensemble approach on your project and get prepared to present it to a panel
  • Establish a working deadline with people in other departments divergent from your own work.

If you're not comfortable with these duties then perhaps reassess your career goals and reconsider if data science is the job for you, as these are potentially difficult duties, with a high level of responsibility for someone who is prepared for the role. Hence this is the best time and great way to evaluate whether you have made the right decision to opt the field of Data Science or not.

Data Science Brings Greater Responsibilities for Scientists & Engineers

In 2012 Data Science was the “sexy” profession but now Data Science involves a range of new duties and responsibilities which require analysis that can be positive, but still involve a high amount of work such as:

  • You may receive new opportunities for innovation
  • Being asked to assist with a project at risk,
  • Being a motivational speaker is also of some help.

Find the Most Suitable Workplace

If you're beginning, be careful about your choice of workplace. Take some time to find the workplace which suits you and provides you with a context to learn and improve your skills. Do some research on the firm online and study what they offer, and any data about their company, whether related to business or financial matters. A firm may claim to offer “excellent engineering” but inefficiency in data use, then they are unsuitable, and you need to find another firm. This is the opportunity to search for another mentor. The best teams are the ones supported by Finance and Operations (with some knowledge in data and variance) and avoiding teams are sponsored by Engineering or Marketing (without knowledge of data usage efficiency).

Some of the Recommendations to Be an Ideal Candidate for the Job

  • Lever emerging Data Stack (Py): Pandas, Ipython and scikit-­learn while leading a multidisciplinary team. Obtaining experience with programming, analytics and data processing
  • Apply the principles of design to data visualisation.
  • Make an effort to become better at speaking and writing in situations outside of conferences.
  • Go to meetings, write and publish online articles, blogs, and presentations because managers are most interested in online writing
  • Learn about Bayesian stats, algebra, convex optimisation and linear algebra
  • Also learn about frameworks as well as algorithms for data streaming.
  • Other topics to learn include useful programming and Scalding
  • Don't worry about learning about Business Intelligence. You will learn by experience

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