Data science continues to be an area of exciting growth and potential, and professionals in this field utilize a wide variety of data science tools in their work. Here we will elaborate on the field of data science as a whole and touch on some of the most common tools that these scientists use while performing their jobs.
What is Data Science?
Data science is an exciting specialty field that focuses on using scientific methods to collect data and turn it into useful information that companies can use to improve their bottom line.
The area of data science overall encompasses a variety of subspecialties such as data scientists, data analysts, data architects, data engineers, statisticians and several others. Each of these professionals uses concrete data science tools to accomplish their relevant tasks. Below we’ll cover some of the most commonly used tools by data science professionals.
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Critical Data Science Tools
Coding and programming are two very important skills that data science professionals need to possess. The following tools are used by these professionals to accomplish their tasks.
RapidMiner takes data scientists through the entire process of prediction modeling. Prediction modeling is the science of using current statistics to make useful predictions about future events.
RapidMiner takes the user through each step of prediction modeling, beginning with data preparation and then moving on to model building and finally to the final stages of validation and deployment.
RapidMiner can be further broken down into the following subcategories of tools:
RapidMiner Studio: Standalone software that takes the user through the entire process of prediction modeling.
RapidMiner Server: Allows model deployment and project management through an environment that supports smooth teamwork.
RapidMiner Radoop: Uses Hadoop to process big-data analytics.
RapidMiner Cloud: A cloud-based storage repository that allows easy sharing of information between devices and supported users.
DataRobot is an automation machine designed to provide the following benefits to users:
Model Optimization: This allows for the automatic detection of the best processing and employment software for the user’s particular needs.
Parallel Processing: This aspect of the DataRobot tool utilizes precise algorithms to distribute the user’s information to large sets of data across thousands of multi-core servers.
Deployment: Facilitates easy deployment with no need for the user to write new code.
BigML takes the user through a series of steps it iterate different orders. The six steps it utilizes are as follows:
Sources: Gathers information from a variety of locations.
Datasets: Creates a set of data from the defined sources.
Models: Creates predictive models.
Predictions: Uses the predictive models to create future predictions.
Ensembles: Combines various models together.
Evaluation: Compares models against specific validation sets.
Google Cloud Prediction API
This platform is designed for mobile applications that use the Android platform. Google Cloud Prediction API offers the following subset of tools:
Recommendation Engine: Offers predictions for future interests based on a user’s past habits.
Span Detection: Categorizes emails as being spam or non-spam.
Sentiment Analysis: Analyzes product comments depending on whether the tone used in them is negative or positive.
Purchase Prediction: Utilizes a user’s spending history to predict future purchases and amounts that may be spent.
Paxata is similar to an Excel style spreadsheet that is easy to use and provides a visual guide designed to bring data together, locate missing data, fix dirty data, and share data projects across teams. It does all of this while eliminating the need for code or scripting.
Trifacta focuses primarily on data preparation and offers both a stand-alone version as well as a licensed professional version. Trifacta performs data cleaning by taking incoming data and providing a variety of statistics based on the column from the computed data.
Trifacta uses the following steps in this process:
Discovering: A first-glance look at the data you submit.
Structure: Assigns variables to the data presented so that it begins to take shape.
Cleaning: Utilizes imputation, text standardization, and other steps to get the data provided model ready.
Enriching: Adds more data or performs engineering techniques on existing data to improve the quality of the analysis given.
Validating: Performs final checks on the data.
Publishing: Exports the data to a user-specified location for further use.
Narrative Science is a data science tool that creates targeted reports from the statistics it is given. Some of the most useful features of Narrative Science include the following:
- The program can incorporate newly computed data with past data to create statistics that are unique to a set target.
- The program can use a particular domain to create benchmarks and trends.
- The program can provide reports that are highly targeted to a specific audience.
MLBase is not a program but is rather an open project put together by the AMP Lab at University of California, Berkeley. The goal of the large-scale data science tool is to help provide secure solutions to a large subset of common problems.
MLBase utilizes the following subset of data science instruments:
MLib: Works as the core distributed library.
MLI: Calculates precise algorithms for high-level programming purposes.
ML Optimizer: Provides solutions to a search problem by using ML algorithms to accomplish this task.
Automatic Statistician is not a product but rather a sophisticated research-based organization. It takes in data from a variety of sources and creates detailed reports using natural language processing. This research project is still in the development stages but looks to be a promising data science tool in the years to come.
Data science continues to be a field of continued growth and development. Data scientists of all types and sub-specialties require access and knowledge of a wide variety of data science tools to help them perform their job-related duties correctly. We’re sure to see a steady increase in data science career opportunities for those who are technically inclined.
Data science combines the best of business, science, and mathematics to offer real-world solutions to pressing issues that companies face on a daily basis. We hope that you have enjoyed this overview of some of the most commonly used data science tools.