In a globally competitive world, businesses that haven’t invested in Data Science tools can have difficulty making timely decisions or applying actionable insights. Today, data scientists’ greatest asset is the technology platforms or the tools that they have access to.
The tools of the trade can mine and analyze petabytes of data in seconds and deliver the desired competitive intelligence, which would not be possible if done manually, no matter how brilliant a data scientist or business analyst is. However, the current options in the Data Science technology and tools niche are many and selecting the right solution for your business is itself a challenge.
If a business does not spend sufficient time in understanding their technology needs and researching the market for appropriate solutions, then the investments can go to waste without yielding any real value.
The NewVantage Partners conducted a survey and the survey results report that “91.7% of IT and business executives from 94 large companies confirmed they’re increasing their investments in data and AI initiatives such as Data Science programs.” Moreover, IDC’s prediction in August 2021 indicates “overall spending on big data and analytics systems will grow at a compound annual growth rate of 12.8% worldwide through 2025.”
Data Science Tools: What Purpose Do They Serve?
Typically, the data analytics teams apply knowledge from various disciplines such as computer science, statistics, machine learning, and deep learning for analyzing raw data and extracting valuable insights. To manage and successfully analyze zettabytes and yottabytes of many types of data on a daily basis, data scientists must use effective Data Science tools throughout their project life cycles.
The most important purpose of using these tools is to completely remove the need for sophisticated programming languages to implement Data Science procedures. These tools come equipped with smart features, algorithms, and user-friendly GUIs to assist the Data Science team complete tasks accurately and quickly.
The Most Desirable Functions in Data Science Tools
According to industry literature, here are some of the most important functions sought in Data Science platforms or software solutions:
- Effective Data Storage and Retrieval: In today’s environment of multi-type and multi-channel data, a data lake or a data mesh may be the most suitable data storage and retrieval technologies. While data lake is a one-stop solution for storing all types of (structured, semi-structured, unstructured) high-volume data, the data mesh is ideal for collecting data from disparate sources and analyzing them at the same time without the need for storage. The data storage and retrieval mechanisms are the most basic function of a business analytics system, and a business must plan and select a solution carefully based on their exact needs.
- Data Preparation: Before data can be analyzed by a standard data analytics software, the raw data must be cleansed for accuracy, completeness, and the correct format. This step is known as data preparation, and it is particularly important for high-speed sensor data or multi-channel and multi-type data arriving continuously from live sources. Data will not only be cleaned, but it may also be compared or combined with other data for further analysis. In the age of machine learning (ML), this function is either semi- or fully automated.
- Data Visualization: This function helps a data scientist, a business analyst, or even a citizen data scientist collect, analyze, and present the data in a manner that can be shared with top executives or the business users. The data visualization function is the single-most important feature to make business cases visible and convincing to a broad audience. Data visualization tools generally offer a wide variety of presentation features like charts, graphs, plots and so on to make complex business ideas visually appealing and understandable.
- Machine Learning: The most successful solution vendors have ML-powered Data Science platforms for data analysis. What this means is that ML algorithms are available for different stages of the data analysis process—right from data collection to delivering actionable insights or reports. These algorithms or “smart programs” help automate specific tasks at every stage of data analysis. Another critical function of ML algorithms is to train the machines to perform the tasks, so that human labor is preserved for more complex analysis processes. Today’s smart algorithms offer a variety of assistance to human scientists—all the way from identifying and matching images to deciphering speech. The solutions for different types of data are different, and the appropriate algorithms have to be chosen for specific tasks.
- Collaboration: This has become a critical function of a data analysis platform in recent times. As a lot of brainstorming, discussions, and collaborations happen between the data scientists, data engineers, data modelers, data analysts, and clients before and during large data analysis projects, the collaboration function is a must-have for all modern data analysis platforms. Moreover, in today’s fast-paced business world, users opt for real-time communications with version control and audit features for a more secure experience. This kind of functionality is especially useful for a large team project where daily communications have to be tracked, monitored, and controlled with access permissions.
- Work Tracking and Estimation: This time-task function allows project team members to track how much work has been done, how much needs to get done, and monitor the initial time estimates vs. actual time spent. This function can also help to track which tasks are succeeding and which ones are not.
A Quick Review of Data Science Tools on the Market
Data Science tools fall within two broad categories:
- Tools designed for programmers and other technical staff
- Tools designed for ordinary business users without any technical know-how
There are many types of Data Science tools. Here is a short list of a few of them:
- MATLAB: MATLAB is most suited for complex mathematical computations. This platform offers matrix functions, support for algorithms, and statistical modeling of data. Though a proprietary software platform, it helps in automating Data Science tasks such as data cleaning, data extraction, re-use of data for scripting and so on. In Data Science applications, MATLAB is for neural networks and fuzzy logic. The MATLAB graphics library can help create stunning visualizations. A versatile tool with varied features, MATLAB can help Data Science teams solve major problems—all the way from data preparation to applying deep learning algorithms.
- BigML: A popular Data Science tool, BigML offers a cloud-based GUI for processing ML algorithms. BigML provides the use of a wide variety of ML algorithms such as clustering, classification, and time-series forecasting, across various departments in an enterprise. The most notable feature of BigML is that it allows any user to create either a free account or a premium account through its web interface. The user can create “interactive visualizations of data” and export the visuals to another mobile or IOT device. BigML also helps automate model tuning and reusable-script workflows.
- SAS: SAS is designed for statistical operations. The data scientist can use in-built statistical libraries and tools for Data Modeling and Data Management. Though SAS has very powerful tools, its main drawback is that it is expensive and caters mainly to larger enterprises. With today’s open-source offerings, SAS can be a premium choice.
- Apache Spark: Designed for batch and stream processing, Apache Spark is a powerful analytics solution. Its built-in APIs facilitate repeated access to data and predictive analytics. What this means is that Apache Spark can process real-time data in batches. Spark’s APIs include interfaces to Java, Python, and R. Spark’s superior cluster-management technology makes it suitable for high-speed application processing.
This article presents a set of Data Science tools meant for tech-savvy users. And the article Data Management Technology: Trends & Challenges discusses the challenges surrounding Data Management technologies.
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