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What are the prerequisite skills required to become a data scientist in 2019?

According to the “Future of Jobs 2018” report, it is said that with advancement in machines and artificial intelligence it is likely that these technologies will replace 75 million jobs by 2020. Data scientist remains to be one of the trending careers, and those interested in pursuing data science as a career must first have the right skill set to land a job in this field.  

As more companies become data-driven, professionals skilled in data science must keep updating their skills based on the current industry’s demand. A NASSCOM report estimates that the data analytics sector will be in an uprising stage to $16 billion by 2025. Thus, more organizations will, therefore, look for data professionals, increasing the demand significantly. Skilled candidates in a data field such as data scientists and data engineers will increase by 40% by 2020.  


Data science is not something one can achieve overnight, it is a field that requires constant learning. But nowadays anyone can acquire these skills if one is dedicated to it. If you want to learn data science, you will learn it. The only challenge is to find out what skills to learn and from where to learn.  

Consider following these prerequisites towards a data science career.   


Most of the data scientists are highly qualified. 

  • PhDs – 46%
  • Master’s degree – 88%
  • Bachelor’s degree in physical science, social science, mathematics, and statistics – 32%
  • Bachelor’s degree in computer science – 19%
  • Bachelor’s degree in engineering – 16%

A degree in any field mentioned above will help kick start a career in data science. However, having the degrees will not suffice. An organization expects to hire expert professionals who have exquisite skills both theoretically and practically. Since data science is a field which requires extensive data related tasks, practical knowledge will be preferred rather than theoretical knowledge.  


The truth is, organizations are more interested in practical skills. Knowledge without practical skills is like an empty vessel.  

Being a data science professional their main objective is to clean, analyze, manage, and organize a huge amount of data. An individual having the passion to leverage data and obtain valuable insights for the organization can become a data scientist. Choosing the right data science certification to get you skilled is dependent on factors such as prior programming experience and having fundamental knowledge in mathematics, and statistics, etc. For instance, Data Science Council of America offers rigorous reskilling and learning opportunities in data science. Starting out a career in data science can be tough without prior guidance or a proper career path. This credentialing body offers fundamental knowledge along with new age skills that are in demand in the current industry. Apart from this platform, one can also refer to other online platforms like Hortonworks certification or Cloudera. However, if you take a sneak peek into job portals, you will find that recruiters are keen on professionals with practical and domain knowledge. Depending on the level and knowledge that one has, you can choose any platform that best suits your learning style.  

Essentially these are the skills and technologies you need to acquire to become a data science professional.  

Skills required: 

  • Statistics and Mathematics – statistics and mathematics are the foundation in data science. Without prior knowledge in these topics, it can be challenging for one to get to core data science skills. For statistical analysis, one needs to master topics like linear algebra, regression methods, and Bayes theorem, etc.
  • Programming in R and Python – R and Python programming languages are widely used by most data scientist. Depending on one’s ease of programming skill, a data scientist may choose his/her tool. R is majorly preferred for statistical analysis and Python is used for general purposes. Working with machine learning and large datasets can get tough if you are not skilled in both the programming languages. Python is great for data manipulation and R work wonders on ad-hoc analysis.

Learn predictive analysis to build predictive analytics models – predictive models are built to predict the future of the organization based on the current data collected. To get there one needs to get skilled in technologies mentioned underneath.

  • Outlier analysis
  • Missing value analysis
  • Exploratory data analysis
  • Sampling techniques – R & Python
  • Decision Tree
  • Decision Tree Regression
  • Decision Tree Classification
  • Error Metrics
  • Error Metrics Regression
  • Error Metrics Classification
  • Linear Regression
  • Logistic Regression
  • Random Forest
  • KNN
  • Naive Bayes
  • Cluster Analysis
  • Text Mining
  • Deployment

Data Visualization tools – for any outcome or results to be comprehended one needs to understand data and represent it in a manner a layman can understand. This can be in the form of charts, graphs, pictorial representation, etc.

  • Tableau
  • RapidMiner
  • FusionCharts

Machine Learning – machine learning teaches the computer to handle tasks without human intervention. This is done with the help of machine learning methods like supervised machine learning algorithms and unsupervised machine learning algorithms.  

Databases – an expert in data science requires the professional to be able to retrieve data. Data plays a major role in the field of data science, for this having knowledge in databases is a vital concern, e.g. MongoDB, SQL, and MySQL, etc.  

Expert professionals who can speak data while explaining businesses value to executives can do anything with data. Let’s be honest, data scientists are the ones who discover trends, alter the future trajectory of an organization, and predict valuable insights for the future of business decision making.


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