What Technology Tools Can Help Data Scientists?

esther carlson

esther carlson

November 13, 2019 · 13 min read

To say the field of Data Science is exploding is truly an understatement. It only takes one statistic to be convinced. Did you know that by 2025, an estimated 463 exabytes of data will be created each day which is the equivalent of 212,765,957 DVDs? Studying such volumes of data requires a range of data science tools.

The sources and categories of data include the internet, social media, communications, services, digital photos and the Internet of Things. The stats are staggering. Here’s one example: Venmo has 40 million users and processes $68,493 in transactions EVERY minute.

Raw data in and of itself isn’t meaningful. Organizations need data insights to achieve business goals that range from improving the customer experience, gaining a competitive edge, facilitating product innovation, making scientific discoveries, predicting or preventing equipment failures, and much more. 

Data scientists interpret data and extract insights that get shared with stakeholders who use them for decision-making purposes. Before the most relevant insights can be extracted, data must be prepared, processed and analyzed. 

To accomplish the discovery and delivery of key findings, data scientists need the right technology tools and services for each phase of the data science life cycle.

Infographic with Data Stats
Source: Raconteur

But First, What is Data Science?

Data science is the study and interpretation of data to discover insights that organizations use for making business decisions. Whether it’s a digital transformation initiative, research project or effort to improve the customer experience, studying and understanding data to discover what it’s revealing is essential to moving forward intelligently.

That’s where data scientists come in. They work across the data science life cycle that starts with data collection and cleaning, then continues with dashboard and report creation, data visualization and statistical interpretation. Finally, data scientists are responsible for communicating results to key stakeholders and convincing decision-makers about the validity of insights based on their knowledge of the business.

It’s important to note that one doesn’t have to be a data scientist to contribute to the discovery of data insights – particularly in large organizations. Data engineers may manage tasks associated with data preparation. Data analysts often play a key role in data visualization and communicating the data insights and their validity to key stakeholders. 

Due to the significant skills gap in the field of data science, another role has emerged. It’s the Citizen Data Scientist. 

Data science tech tools have evolved to the point that much of the data preparation can be automated as well as certain steps of the analysis. There have also been rapid developments in both the open-source ecosystem of tools as well as commercial, productized data science tools. All of these advancements in tools make it easier for citizen data scientists to play a key role in the various phases of the data science life cycle.

Image of Data Science Life Cycle
Source: Quora

Data Science Technology Tools and Services

Tools used by data scientists may be all-inclusive or apply to specific phases of the Data Science Life Cycle. There are platforms, core programming languages, frameworks, and standalone technologies.

The list is inclusive of machine learning (ML) technologies since ML is one of two paths that data science follows, the other is based on statistical analysis. Finally, those tools that can be used without having programming skills will be noted.


There are several platforms data scientists can use to manage, analyze, visualize and model data. Several of these platforms also provide machine learning capabilities that can be used by data scientists or other stakeholders – with or without programming skills.

Alteryx provides two tools data scientists use to discover, prep, blend and analyze data: Alteryx Server is a scalable platform used for analytics and Alteryx Connect is a collaborative data exploration platform.

Anaconda is an integrated platform widely used in data science and machine learning to develop, manage, collaborate and govern data. The development environment is based on the interactive notebook concept which accommodates both Python and R-based open-source packages. 

BigML is a machine-learning platform that helps data scientists create, experiment, automate and manage ML workflows. Interestingly enough, the mission of the company is to “make Machine Learning easy and beautiful for everyone.”

DataRobot is the data science and automated ML platform used by data professionals – with or without programming skills – to quickly and easily build predictive models in less time than when using traditional data science methods. 

KNIME Analytics Platform and KNIME Server software enable advanced data science. The analytics platform makes understanding data and designing workflows accessible to all. Data science teams use their enterprise-grade software to collaborate, automate, manage, and deploy their data science workflows as analytical applications and services.

MATLAB is a solution used to analyze data, develop algorithms, create models, and perform data analytics. It integrates computation, visualization, and programming in an environment that is easy-to-use and includes a programming language specifically for technical computing.

MLBase is a distributed machine learning (ML) system developed by the AMP (Algorithms Machines People) Lab at UC–Berkeley that can be used by those with or without coding skills. The open-source platform applies both statistical techniques and ML to transform big data into actionable knowledge and eases the challenges of applying ML to solve big data problems.

RapidMiner is a platform used by both data scientists and non-programmers to complete tasks across the complete life-cycle of prediction modeling. Data mining and machine learning tasks completed in RapidMiner include ETL (Extract, Transform, Load), data preprocessing and visualization, predictive analytics and statistical modeling, evaluation, and deployment.

SAS provides a range of software products for analytics and data science such as SAS Enterprise Miner (EM), SAS Visual Data Mining and Machine Learning (VDMML). The SAS® platform supports every phase of the analytics life cycle – from data to discovery, to deployment.

TIBCO Software offers an analytics platform that simplifies the end-to-end analytics lifecycle within big data ecosystems and facilitates the use of data science techniques at scale. Data science, line of business, and IT teams use the platform to collaborate on big data and other advanced analytics projects for increased efficiency and productivity. 

Abstract Image of 3 Monitors
Image by Gerd Altmann, Pixabay

Programming Languages

Programming for data science is unique in that it is data-centric and designed to help users process, analyze and generate predictions from their data as well as carry out algorithms specific to data science. Data scientists must master one or more data science programming languages to become proficient in their role.

Python is a high-level programming language with five key features: object-oriented, functional, procedural, dynamic type, and automatic memory management. It provides a large standard library as well as packages for natural language process and data learning.

Python is used by 83% of the nearly 24,000 data professionals who responded to a Machine Learning and Data Science Survey conducted by Kaggle in late 2018. Notably, 3 out of 4 of those same data pros believe that aspiring data scientists should learn Python first.

R is a programming language that is used with a UNIX platform, Windows, or Mac OS by statisticians and data miners for developing statistical software, performing data analysis and data visualization. This highly extensible language provides a wide variety of statistical and graphical techniques. 

While open-source R is considered a more difficult language to learn, it is better-suited for ad hoc analysis and exploring datasets than Python. 

Java is another programming language that is object-oriented and includes these key features: architecture-neutral, platform-independent, portable, multi-threaded, and secure. It is fast and scales easily for larger applications and has specific libraries and tools for machine learning and data science.

SQL is a domain-specific language data scientists use to manage data stored in a relational database management system (RDBMS). Data scientists must know how to work with data in database management systems which makes it essential that they understand SQL tables and know how to use SQL queries.


Hadoop and Spark are big data frameworks that include some of the most popular tools for performing basic tasks. While there is some crossover between the two, the frameworks also perform different tasks. 

Hadoop uses a distributed approach to store substantial amounts of data and process the data in parallel across clusters of commodity hardware which is far more efficient than running data on a single device. 

The newer and more advanced Spark is a fast, flexible framework data scientists use for large-scale SQL, batch processing, stream processing, and machine learning. While Spark does have the advantage of working much faster than Hadoop, it does not have a distributed data storage system.

There are use cases for using one of the frameworks or both. For example, Hadoop will be sufficient if a big data project is focused on analyzing a structured dataset. The project does not require the advanced streaming analytics and machine learning functionality provided by Spark. 

Here’s another example. Since Spark does not have its own system for distributed data storage, Spark will need to be installed to run over Hadoop for specific types of big data projects. This allows the Spark analytics applications to access data stored using the Hadoop Distributed File System (HDFS). 

Spark has emerged to be the front-runner of the two frameworks as it is considered to be a more advanced product than Hadoop. Designed to work by processing data in chunks “in memory” instead of using physical, magnetic hard discs, data processing can be completed more quickly – up to 100 times faster in certain circumstances.

Laptop with Data DIsplayed on Dashboard
Image by Carlos Muza, Unsplash

Tools for Specific Functions

While data science platforms are robust in their coverage of the life cycle, there are technology tools that address specific functions such as preparation, analysis or visualization of data. 

Altair Knowledge Works (previously Datawatch) provides a range of automated, self-service data analytics and machine learning tools. Citizen data scientists and data scientists use these tools for data preparation, data prediction, and real-time high-volume data visualization.

Cloud Dataflow is a cloud-based data processing service for both batch and real-time data streaming applications. Developers use the tool to set up processing pipelines for integrating, preparing and analyzing large datasets from sources like Web analytics or big data analytics applications.

Databricks is an Apache Spark-based Unified Analytics Platform with data engineering and data science capabilities that use a variety of open-source languages. In addition to Spark, the platform provides proprietary features specific to Amazon Web Services (AWS) for security, reliability, operationalization, performance and real-time enablement.

Dataiku is a collaborative platform that enables self-service analytics and the operationalization of machine learning models in production. In effect, it enables enterprise AI, which includes building, deploying and monitoring predictive data flows to solve problems like fraud, churn, supply chain optimization, predictive maintenance, and more.

Datawrapper is a digital tool that simplifies the creation of interactive visuals of data and offers an extensive number of options for visualizing data. Many news channels and organizations use Datawrapper to represent data in interesting ways.

D3.js is a Javascript library used to manipulate documents based on data and make interactive visualizations on a web-browser. Several APIs can be used to create dynamic visualization and analysis of data in a browser without being limited to a proprietary framework.

Domino is a comprehensive end-to-end Data Science Platform designed for expert data scientists. The platform incorporates both open-source and proprietary tool ecosystems and provides capabilities to collaborate, reproduce, and centralize model development and deployment.

Excel is a standard analytics tool that is widely used for data processing, visualization, and complex calculations by non-programmers because of its ease of use. While Excel might not be thought of as a powerful data science tool, it has an Analysis ToolPak that is used for machine learning. To learn more about the extent of what can be accomplished in Excel, read this article.

IBM data science tools include SPSS (including SPSS Modeler and SPSS Statistics) and Watson Studio, a tool used to incorporate and build on IBM’s previous Data Science Experience (DSX) product.

Microsoft provides several software products for data science and ML. It offers Azure Machine Learning (including Azure Machine Learning Studio), Azure Data Factory, Azure HDInsight, Azure Databricks, and Power BI for use in the cloud. Machine Learning Server is used for single-premises workloads.

Paxata is a self-service data preparation application and an automated machine learning platform that is used to prepare data at scale without having to code. The enterprise-grade Paxata automates data preparation and ML to make working with data more efficient for non-technical users.

SAP offers SAP Predictive Analytics (PA), which has several data science components, including Data Manager for dataset preparation and feature engineering, Automated Modeler for citizen data scientists, Expert Analytics for more advanced ML, and Predictive Factory for operationalization. SAP PA is integrated with SAP HANA, an application that uses in-memory database technology to accelerate the processing of massive amounts of real-time data.

Scikit-learn is a library that is based in Python and used for implementing machine learning algorithms. Widely used for analysis and data science, it is simple to use and easy to implement.

Tableau is a data visualization platform that offers powerful graphics to quickly enable interactive visualizations of data. Popular with data pros who do not have coding skills, Tableau makes it easy to complete data visualization and reporting tasks, including the creation of graphs, charts, maps, and more.

Trifacta is used for the preparation, cleaning, and transformation of data – with or without a data scientist – and aligns IT and business teams around the data and analytics. This free, stand-alone software offers an intuitive GUI for performing data cleaning and is architected to be open and adaptable to changes in technologies that precede or follow it in the life cycle. 

Visualr is a data visualization tool with powerful features, drag-and-drop functionality, and flexible connectivity to a range of database types. While not considered a mainstream tool, Visualr is known for being fast, reliable and economical.

Data Science Will Continue to Evolve

This concludes our introduction to platforms, frameworks, programming languages and tools that data scientists and other data professionals are using to perform their work throughout the data science lifecycle. There is no shortage of tools, only the shortage of data scientists.

Sources for these tools included Gartner’s 2019 Magic Quadrant for Data Science and Machine Learning Platforms, the 14 Most Used Data Science Tools for 2019 and Top 10 Data Science Tools In 2019 To Eliminate Programming.

Data science technology continues to evolve – especially in the areas of automation and the proliferation of tools that do not require programming skills. More business users will take ownership for performing analytics while data scientists focus on the expert-level data science tasks.

Based on the existing platforms and tools, it’s clear that machine learning is becoming an integral part of data science. This integration raises the level of sophistication in terms of what is possible to achieve in the field of data science.

To learn more about how Dis.co facilitates using distributed computing to run data science studies in parallel, request a demo or start your free trial today.

esther carlson

esther carlson

November 13, 2019 · 13 min read