Data Science has been established to be the most sought after job today. If you wish to establish your career in Data Science you should equip yourself with the everyday tools in the life of a data scientist. Data Science in an interdisciplinary field and requires the use of tools from various disciplines.
Have a look below to know about the most used Data Science skills!
The language Python is easier to learn as it reads like a regular human comprehensible language. It is a high-level language and supports statistical modeling, data mining, and visualization. Moreover, Python easily codes and debugs. Mostly, a newbie data scientist uses Python as it is easy to learn and has a variety of uses.
R is another programming language that is used a lot in Data Science. Compared to Python it is more difficult to learn. Mostly, academics and research fields used it, but now it is gaining popularity among other fields. It is easy to write statistical models on R. Complex formulas can be easily used in R. The language, however, is not suggested for inexperienced programmers as it is difficult to learn and understand.
To know more about Python and R read our article on ‘Comparing Python and R. Which one should you use?’
Structured Query Language manages data held in relational database management systems. Such databases almost always store structured data. Therefore making SQL indispensable to a Data Scientist. SQL uses include:
- Data insertions
- Updating and Deleting
- Schema creation and modification
- Data access controlWriting an SQL for them yields easily reproducible scripts and keeps you closer to the data.
Another programming language that Data Science uses is Java. Data science uses Java because of its broad user base. Moreover, it is easy for most programmers to understand. Java forces programmers to be explicit about types of variables and data they deal with which is an essential in Data Science. Moreover, the Java Virtual Machine is a good platform to write codes that look identical on multiple platforms which suit big data. Also, Hadoop is extensively used in Data Science which is written in Java.
Hadoop is an open-source software framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. Hadoop’s distributed computing model is capable of processing huge amounts of data. Also, it stores data without any processing. Additionally, it is free and you can easily grow your Hadoop system by simply adding more nodes.
This might seem like an unlikely candidate for data science tools. However, it is capable of lot more than we assume. Moreover, it is easy to use and is widely available. It is not the tool for analyzing massive, unstructured data but is surprisingly powerful when used for a variety of data analytics projects at a small scale.
Data Science uses SAS as it has many uses. It can read data files created by other statistical software packages. Moreover, various data formats imports into SAS with ease. SAS builds a well-rounded, self-sufficient environment that is based on an organization’s databases. A data analyst can transform these data sets into useful information that is subsequently delivered to decision makers at the right moment to maximize the utility of the information.
Tableau is a data visualization tool and a data scientist is often required to use visualization tools. Data visualization helps you explore the data to ascertain its underlying structure, communicate your findings in an intuitively understandable way. Tableau helps you do just that.
The tools mentioned above are the most used by a Data Scientist. The possession of knowledge of these tools is sure to boost your data science career. However, if you already possess these tools, the next step for you is to crack the interview for Data Science. Read our blog on ‘Top 15 Questions for a Data Science Interview’ to ace your interview process.