15 Web Applications Every Data Scientist Must Use

Data science and analytics is on its way to becoming the backbone of the global IT industry. The range of technologies associated with it is expanding at a rapid pace, thus spurring the need for data science courses.

According to a survey conducted on data science opportunities, more than 90,000 job openings in data science and related fields are being advertised in India alone. Data analytics strategies benefit organizations through the profound insights they provide about customer behavior, revenue strategies, and systems performance. According to Gartner’s Top 10 Strategic Technology Trends for 2018, virtually every web application and service is bound to incorporate some level of AI and data science in the future.

However, with the sheer number of responsibilities on a data scientist’s plate, it is difficult to take the time out to search for tools of the trade that can assist them in their work. To make this job easier, here’s a list of web applications that every data scientist could benefit from:

1. Algorithms.io:

Algorithms.io is a platform developed by LumenData Company to provide machine learning support to data scientists. The support is provided in the form of a service that streams data from all the devices connected to it. This web application is used to turn raw data into actionable events and real-time insights that enable companies to deploy machine learning strategies for simplifying data.

2. Apache Kafka:

Apache Kafka is a distributed streaming platform that efficiently processes streams of data in real time. Data scientists can use this web application to build real-time data pipelines and streaming apps as it empowers them to subscribe and publish to streams of records, store streams of files in a fault-tolerant manner, and to process record streams as and when they occur.

3. D3.js:

D3.js was created by Mike Bostock to create a web platform that would write data manipulation codes for data scientists. Data scientists make use of this tool with a JavaScript library for performing operations on data-based documents, to use SVG (Scalable Vector Graphics) to add attraction to their data, HTML, and Canvas. One of the critical features of D3.js is that it places importance on maintaining web standards to utilize the entire potential of modern browsers without the trouble of being tied down due to a formal framework.

4. Bokeh

Bokeh is a Python-based visualization library that targets modern web browsers for presentations to help its users in creating interactive plots, data apps, and dashboards. This web application provides data scientists with an elegant and concise construction of graphics that is quite similar to D3.js. Its capabilities are not just limited to plotting graphs and charts, but also extend to the high-performance interactivity that is usually associated with large and streaming data sets.

5. DataRobot:

DataRobot is a sophisticated automation platform that aids data scientists in building better and faster predictive data models. With this tool, data scientists can test, train, and compare thousands of different models with a few lines of code. DataRobot’s natural model deployment feature enables it to automatically identify the engineering and pre-processing required for each modeling technique.

6. DataRPM:

DataRPM uses its technology centered around meta-learning to automatically make predictions about asset failures. DataRPM builds manually verified, machine learning-based models that provide predictive maintenance. Its system of flow uses recipes like segmentation, feature engineering, prediction steps, and influencing factors to broadcast prescriptive recommendations.

7. Feature Labs:

Feature Labs is one of the data science applications that develops smart solutions for the massive amount of data that is dealt with by data scientists. By tailoring on-boarding solutions and building use cases, it helps data scientists make an efficient start to their predictive models. By discovering new insights, Feature Labs gains an understanding of your data and thus helps generate better forecasts for your business.

8. ForecastThis:

This tool allows data scientists to generate forecasts by using their data on their simple API (Application Programming Interface) and spreadsheet add-ons. By automating predictive models, it can scale to problems of almost every shape and size. ForecastThis uses algorithms that create understandable models of market functions, thus lending credibility to any strategy that helps you successfully enter the market.

9. Fusion Tables:

This cloud-based data manipulation and management service focuses on ease-of-utility, collaboration and visualizations, and empowers data scientists to gather, create, and share tables containing data. Fusion Tables can search hundreds of other Public Fusion Tables from the internet and offers to incorporate the same into users’ projects. Data scientists can also use this application to visualize their data by directly importing it on the tool.

10. Shiny:

Shiny is a web application framework developed by RStudio that is used by data scientists to turn analyses into interactive web applications. An ideal tool for data scientists who need assistance in web development, Shiny combines modern web’s interactivity with R’s computational power. Built entirely on R, it has easy-to-write apps and does not require any prior knowledge of HTML, CSS, or JavaScript.

11. Gawk:

This tool allows data scientists to take care of simple data-rearranging jobs while using just a couple of lines of codes. Its data-driven framework is adept at searching files for line or other texts units that contain a number of patterns. Since it is designed to interpret special-purpose programming languages, Gawk makes reading and writing programmes easy for data scientists.

12. KNIME:

KNIME (Konstanz Information Miner) is a tool designed on an open platform that allows its users to navigate complex data freely. It enables data scientists to uncover data’s hidden potential, predict the future, and search for other meaningful insights. It has more than 1,000 modules, which allows data scientists to deploy and scale their predictive models easily.

13. Logical Glue:

Logical Glue is an award-winning white-box artificial intelligence and machine learning platform that aids in increasing profit and productivity for organizations. Its simple deployment and integration coupled with meaningful narratives bring data insights to life through graphical representations.

Logical Glue also enables data scientists to access new technologies with artificial neural networks and fuzzy logic to build the most accurate predictive models.

14. Natural Language Toolkit (NLTK):

NLTK is a platform that builds Python programmes that directly work with data related to the human language. Its user interface provides data scientists with more than 50 lexical and corpora resources. It also includes a suite of libraries that text process files for tokenization, classification, tagging, stemming, parsing and more.

15. OpenRefine:

OpenRefine offers a powerful web interface to data scientists who wish to transform, clean up, and extend data with web services.  It allows users to explore large datasets easily, and to clean, transform, reconcile, and match various kinds of related and unrelated data. By using OpenRefine, data scientists can also incorporate all this data into separate new databases.

When it comes to data scientists of today, not only are they required to be proficient in tools of the trade, but they are also expected to possess a working knowledge of statistical programming languages for databases, data processing systems, and visualization tools. However, the list of web apps discussed above makes the job relatively easier. These tools are chosen based on factors like ease of use, features, and popularity, and are designed to assist data scientists at every step.