Fri. Aug 14th, 2020

Tips and tricks for a great data science career

In recent years, there has been a huge increase in the demand for data scientists.

In recent years, there has been a huge increase in the demand for data scientists. Why is this? Because, at organizations of all sizes and from all verticals, a large amount of data flows in everyday and at a pace that is increasing exponentially. From sales to inventory, employee punch-in timings to productivity, and many other parameters, the types of data are diverse. And if analyzed properly, these could reveal some extremely useful insights that would guide an organization in taking the right strategic decisions.

This burgeoning need to analyze data is the reason behind the rising demand for data science professionals. They work to process and analyze the large volumes of data to extract insights, and their efforts could transform not only IT systems but also agriculture, healthcare, mobility, and retail, among others.

Statistics prove why a data science career is a very attractive option. Consider the following:

  • LinkedIn in 2017 named data science as the fastest-growing job.
  • A Glassdoor survey in 2019 ranked ‘data scientist’ as the top job in the US for the fourth successive year. Glassdoor also estimated the average annual salary for a data scientist at USD 120,931.
  • According to the US Bureau of Labor Statistics (BLS), there will be a 27.9% increase in employment in occupations leveraging mathematical science – which includes data science – from 2016 to 2026, adding up to an estimated 11.5 million new jobs by 2026.

Data science professionals are extremely valuable for businesses. They must first precisely understand the decision or issue facing the business, and then be able to pull out what is important to solving it. They help in managing and interpreting data, for which they work on analytics modeling and business analysis, among others. They can uncover solutions to different intricate problems through their specialized knowledge in different niches of data science. Given this, even traditional organizations not typically inclined toward technology are being compelled to sit up and take notice.

The key responsibilities handled by professionals in a data science career include the following:

  • Understanding problems related to data analytics whose solutions could be valuable to an organization
  • Identifying the right datasets and variables to be analyzed
  • Extracting data – structured and unstructured – from a variety of sources
  • Assessing the data to find new opportunities and solutions
  • Ensuring the data is accurate, complete, and uniform, through proper cleaning and validation
  • Uncovering trends and patterns through data analysis
  • Big data mining through the right algorithms and models
  • Visualizing the data and presenting it to the relevant stakeholders in an easily comprehended form

In order to be able to do this, a data science professional must possess a strong sense for business. This needs to be combined with a background in analytics, computer science, mathematics, modeling, and statistics. He or she has to design, develop, and deploy the most relevant solutions to solve the issue at hand for the business by leveraging his or her abilities to interpret data and derive meaning from it.

Important technological skills for this field include:

  • Programming: competence in Java, Python, R, and SQL
  • Data reporting and visualization
  • Database management: Hadoop and related tools
  • Mining data to explore and discover knowledge

Aside from technology, what also matters is strong skills in communication and interpersonal behavior. A professional in this field must work in teams and present results to others in the company, in a form they can understand, appreciate, and derive value from.

Given the breadth of skills and responsibilities, there are a number of job roles available in a data science career. Some of the most common roles are given below:

  • Data Analyst
  • Data Architect
  • Data Engineer
  • Data Scientist
  • Machine Learning Engineer
  • Business Analyst
  • Data and Analytics Manager
  • Database Administrator
  • Statistician

To improve his or her job prospects in data science, a candidate would do well to pick up one of the best data science certifications. A certification proves to a potential employer that the candidate possesses the right knowledge and is competent with the latest skills, technologies, and practices in the industry. It also proves that the candidate is serious about a career in data science and is looking to learn more and grow in the field, both of which are desirable attributes in someone to hire. There are a number of data science certifications available for candidates today, and some of the leading institutions offering these include Cloudera, Data Science Council of America (DASCA), and Dell EMC.

The buzz around data science shows no signs of abating for at least some years into the future. To become an excellent data science professional, a candidate must be able to work with data by leveraging sophisticated techniques of statistics and visualization, so that they can derive useful insights. The diversity of the field means a candidate must combine knowledge of mathematics and statistics with that of programming, and experience on the right kind of projects along with regular upskilling will make for a great career.

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