Top Data Science Tools That Will Make You Job-ready

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Top Data Science Tools

There are numerous tools associated with data science. If you are someone who is new to the field, you will be overwhelmed and intimidated by the number of data science tools. This article is for those who are looking to move to a data science field or have just started and want to know which tools can help them start their career as a data scientist.

Data science has become one of the most attractive tech profiles in the present day. You will find various news reports and articles stating how data science is the career that you should go for. However, most articles won’t give you an idea of how you can start your career and just throw some random tool names at you.

When you are just starting out to become a data scientist, this question will surely haunt you, “which tools should I learn first?” You will be inundated with various suggestions to learn a long list of tools. But when it comes to most data scientists, they all have a similar learning pattern. If you learn more than four to five of the common tools that we are going to mention here, you should have a good career start, earning a salary near to the industry median. You will also get a good idea how to start your learning if you are wondering to take up professional data science courses.

Before starting with the tools, I hope you have a good concept of basic statistics and mathematics. Otherwise, just the knowledge of tools won’t be enough.

 

 

Data Science Tools

Let us look at the various types of tools used in data science. We will use the tool ecosystem as described in a report by O’Reilly, which will help us to understand the data science environment better.

Cluster 1 (Microsoft-Excel-SQL)

Corresponding preferred role: Business Intelligence

 

  • Excel – The basic database and data analysis tool that most data scientists started out with. As you move to a higher role in data science, this tool won’t find any significance but it will still remain core to your work profile.
  • VBA – It is essential for those who work with Excel most of the time. With VBA, you can move one step into the field of automation. You can create your own complex functions which otherwise cannot be done simply through Excel’s library functions.
  • MS SQL Server/SQL – As your data grows, it might be difficult to store in Excel files as it can become slow and difficult to manage. MS SQL Server is an RDBMS (regional database management system) which offers good functionality in storing and retrieving data. SQL is the language which is used to manage data in an RDBMS.
  • SAS/SPSS – SAS and SPSS are both analytical software suites with proprietary licenses. SAS has been developed by SAS Institute while SPSS has been developed by IBM. Due to the popularity of R and Python, SAS and SPSS usage have gone down. However, it is still used by many industries as they are easy to learn and implement.

Cluster 1 is popular among those who have just started their career in data science as it gives them a good learning curve. Excel, SAS, SQL and VBA are all easy to learn and does not require extensive programming skills.

Cluster 2 and Cluster 3 (Hadoop-Python-R)

Corresponding preferred role for:

Cluster 2 – Hadoop and Data Engineering

Cluster 3 – Machine Learning and Data Analytics

  • Python/R – Both Python and R has been gaining popularity over the years as the go-to programming language for data scientists. R is hugely popular for its complex problem-solving capabilities. Python, on the other hand, is easier to learn if you have basic knowledge of object-oriented programming languages.
  • Hadoop – With the growing importance of big data in analytics, Hadoop is an important tool for process huge datasets.
  • Cassandra/MongoDB/NoSQL – RDBMS supports structured and predictable data. This becomes a disadvantage while working with big data. Cassandra, MongoDB and NoSQL are useful for storing and managing unstructured data.
  • NumPy/SciPy – They are libraries for Python which can be used for high-level mathematical, scientific and technical computing. It is one of the widely-used tools in Python programming.
  • Apache Mahout/Weka – Apache Mahout and Weka are sets of machine learning algorithms popularly used for data mining purposes.

Cluster 4 (Mac OS-JavaScript-MySQL)

Corresponding preferred role: Data Visualisation

This is a relatively new cluster and is more or less centred on the Mac OS.

  • JavaScript – In terms of data science, JavaScript is the least popular programming language but is highly useful for data visualisation. It lacks the number of libraries in comparison to R and Python.
  • MySQL – It is important for server-side queries and helpful in extracting datasets from a relational database.
  • js – If you want to improve your data visualisation through JavaScript, you can use the D3.js library. It offers stunning and powerful data visualisation in web browsers.

 

 

There are various other tools associated with data science but as someone who is a beginner or intermediate learner, you can choose from one of the above clusters, cluster 1, cluster 2 and cluster 3 being the most popular ones.

Naukri Learning offers you various comprehensive online data science courses, which can help you to be an expert in the field and get better job opportunities.

About the Author

Hasibuddin Ahmed

Hasibuddin Ahmed

Hasib is a professional writer associated with learning.naukri.com. He has written a number of articles related to technology, marketing, and career on various blogs and websites. As an amateur career guru, he often imparts nuggets of knowledge related to leadership and motivation. He is also an avid reader and passionate about the beautiful game of football.
Topics : IT and Telecom

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