Top 5 Technical skills Every Data Scientist Should Master in 2019

By POOJA BISHT |Email | May 16, 2019 | 8073 Views

Skillset is the most important thing an employee carries with himself while working in an organization. Organizations are always looking at the skill set possessed by the candidates and identify a potential candidate who can work for them. With regard to a Data Scientist apart from the soft skills and the business acumen technical skills also becomes a very important part. A Data Scientist must be upgraded with the latest software and should know how to use them. As technology is changing and Big data has arrived, organizations are continuously evolving themselves to a higher level where with each day new software are being added to the organizations. The role of a Data Scientist encompasses the usage of various software and programming languages which he must master. This article describes the top 5 technical skills that a Data Scientist must master in order to be successful in his field.

Below are the Top 5 Technical skills Every Data Scientist Should know and master in 2019.  

  • R Programming

You will need the programming language R for statistical computing in Data Science. R makes good sense for its usage by Data scientists, statisticians, and Data analysts and so has its great popularity among the Data Scientist community. Nearly every of the organization today uses R due to the following salient features provided by it:

  1. R helps in the Visualization of your data. The Visualization of the Data provided by R through the is effective and appealing. The visualizations are clear and understandable.
  2. You do Statistical analysis of your data using R which is very easy to analyze. The statistical features provided by R is effective and diverse.
  3. Data scientists do Predictive modeling with the help of R. The future events are predicted with the help of historical and present events using statistical computing through R.

  • Apache Hadoop
The processing of Big data is a big concern for companies today. The big amount of data that is getting flooded through various sources in a company's database needed to be processed faster. Hadoop is the software for processing of this Big data only where with the help of distributed processing of large data sets across various clusters of computers is done using Hadoop. 
The execution of big data through Hadoop is faster which is also the reason why many of the companies today are using it.

  • Python
The inbuilt libraries of Python along with the features of object-oriented programming, structured programming, and functional programming makes it popular among the data science community. Programming in Python is simple and the features provided by it are very helpful for the data science professionals. 
 Pandas is the  Data Analysis Library of Python and is used for importing data from Excel spreadsheets and in processing datasets for data analysis which is useful in Data Science.
Also, Python is the most sought after programming languages and the majority of the companies today are mentioning it as the prominent skill in their data scientist job description.

  • SQL
SQL, or Structured Query Language, is used for managing datasets which are stored in relational database management systems. SQL is the language that almost every company uses for data science. Working on SQL is easy. There are many things which analysts and data Scientist do with SQL.

  1. You can do the insertion of your data using SQL.
  2. Deleting and updating rows and columns is easy in SQL. So if you want to delete some part of your data or want to modify the existing you can easily do with the help of SQL.
  3. Accessing data is simpler using SQL. You can easily access the data using the simple commands used in SQL.
  • Data Visualization software
Data Visualization is an integral part of Data analysis. You will be required to know the necessary skills for visualizing of your data. There are many softwares available online for Data Visualization and nearly every company uses some or the other Data Visualization software to visualize their data. Analysis and presentation of the data using these Data Visualization SOFTWARES are easy, effective and appealing.  A common example of Data Visualization SOFTWARE is Tableau, Sisense.

Source: HOB