Top tools used by data scientists
In recent years, data has grown to be the most valuable commodity in business – even more valuable than oil. However, the data is only helpful if companies can interpret and manage it correctly. That’s where data scientists come in. They use various tools to make sense of raw data, offering insight to help an organization move closer to its goals. There are a number of tools that data scientists employ in their work – some more popular than others. In this article, we discuss some of the top tools used by data scientists.
Data scientists use several programming languages, but Python is the most popular. For instance, Google used Python to create the TensorFlow framework. Other companies known for using Python include Netflix and Facebook. One of the reasons why Python is so popular among data scientists is that it integrates well with most cloud service providers. It can also be extended with models written in C or C++. Python is usually a perfect fit when there is a need to integrate data analysis with web applications. It also comes in handy for implementing algorithms.
Many data scientists claim that Python is easier to learn than other languages such as R. If you have been considering exploring careers in data science, then this is useful information to know! Python also has a wider variety of data science libraries. These include NumPy, statsmodels and pandas. Courses from accredited institutions, such as the program offered by Baylor University, teach these skills and are a way to start your career on the right path.
Data visualization is a crucial task for data scientists. It refers to the graphical representation of data through elements such as charts and graphs. That way, the non-technical mind can understand complex concepts. There are several data visualization tools for data scientists, but Tableau is one of the most commonly used tools.
Tableau has various useful features, so many data scientists are drawn to it. One of its prominent features is data blending. Using Tableau, a data scientist can combine related data from multiple sources and analyze the collection in a single view.
Another critical feature is real-time analysis. This enables a data scientist to efficiently work with dynamic data. Even when the data is fast-moving, Tableau can extract valuable information with interactive analytics. Most importantly, Tableau allows collaboration. As you may know, data analysis is collaborative in nature. With Tableau, various individuals can share data and make follow-up queries.
Git is a version control system that helps data scientists track source code changes. This is incredibly important, especially when multiple people are working on the same project. Many people wonder whether there is a difference between Git and GitHub. There is a difference: Git is the underlying technology for tracking and merging changes in a source code.
On the other hand, GitHub is a web platform built on top of Git technology that offers additional features such as user management and automation. Git is especially useful to data scientists who work in organizations that follow the agile software development framework. It makes the development process faster and easily adaptable to changes.
Docker has gained popularity among developers in recent years. While data scientists are not software developers going by the textbook definition, Docker has some features they can use. Docker is a container-based platform built from a script that can be version controlled.
Docker is so appealing to data scientists because it is lightweight. Unlike virtual machines, Docker containers don’t carry the payload of an entire OS instance. It only carries the OS processes necessary to execute the code. Docker improves productivity as containerized applications can be written and run anywhere. Other benefits include shared container libraries, automated container creation, and container portability.
You have most likely heard of the term big data. Big data refers to a data set that is too large or complex to be dealt with by conventional data-processing methods. Data scientists are responsible for uncovering patterns and relationships in these large data sets through advanced algorithms. One of the tools that data scientists can count on when it comes to handling big data is Apache Spark. Apache Spark is a big data distributed processing framework known for its speed. It uses in-memory caching and optimized query execution for fast queries.
Besides fast processing, Apache Spark is also famous for its flexibility. It can support multiple languages, including Python and Java. It can also process real-time streaming data, so it’s ideal in situations where instant outcomes are required. It is also known to give better analytics than other frameworks such as MapReduce.
Structured Query Language (SQL)
A data scientist’s primary role is to study and analyze data. But where does the data come from? It comes from a database. Therefore, every data scientist needs a tool to extract data from databases. Enter Structured Query Language (SQL). SQL is the standard programming language for managing relational databases. Even modern big data systems such as Hadoop use SQL to process structured data.
There are several reasons why SQL is so widely used in data science. One of them is that it’s incredibly powerful. It can be used to carry out tasks such as creating new tables and inserting data into tables. Another reason is that it is sharable. It therefore makes it easy for data scientists to work with others in the organization who need access to the same data. For instance, a data scientist working with the engineering team can seamlessly share information using SQL. Some of the uses of SQL include data cleaning, visualization and preparing data for analysis.
Jupyter Notebook is a widely used web application that allows data scientists to create and share documents. A single document can contain elements such as explanatory text, live code, equations and visualization. It is a handy collaboration tool mainly because it supports several programming languages. It also makes it easy for data scientists to organize and clean data.
Selecting the best data science tools
Data scientists are spoiled for choice when it comes to the tools they can use for their day-to-day tasks. Some factors to consider when selecting tools include flexibility, scalability, security and ease of use.