The scale is also changing. Originally, data science was nothing more than a number work done by scientists after hard experimentation. Data science is now the most important part of the workflow. Companies today incorporate mathematical analysis into business reporting and build dashboards to get a quick picture of what’s going on. Also, the speed is increasing. Analytics jobs that were once annual or quarterly now run in real time. Businesses want to know what’s happening right now so managers and employees can make smart decisions as well as leverage all that data science has to offer.
Here, we introduce key tools that add accuracy and science to your never-ending data flow analysis.
A collection of words, codes, and data has become a ‘lingua franca’. Static PDFs filled with unchanging analysis and content are still valuable because they create permanent records, but data scientists want to tweak the underlying mechanisms. Jupyter notebooks allow you to do more than just view information.
Jupyter Notebooks were first developed by Python users who wanted to borrow the flexibility of Mathermatica (software for computation). Today’s standard Jupyter notebooks support more than 40 programming languages (R, Julia, Java, and C being the main languages).
Because the Jupyter Notebook code itself is open source, it can serve as the basis for many interesting large-scale projects, such as curating data, supporting learning, and sharing ideas. In universities, lectures are conducted using laptops. Data scientists use it to exchange and communicate ideas. ‘JupyterHub’ is responsible for providing all sorts of data science ideas by providing a containerized central server with authentication. So you don’t have to install or maintain software on your desktop or worry about scaling your computing servers.
Notebook lab spaces
Jupyter notebooks do not run alone. You need a home where data is stored and analyzed. Several companies are currently supporting this for promotional purposes or for a nominal fee. Google’s Colab, GitHub’s Codespces, Azure’s Machine Learning lab, JupyterLabs, Binder, CoCalc, Datalore, etc. This is an example. However, setting up your own server on a lab bench is not that difficult.
Although these services are similar, there are important differences. Most of them support Python in some way, but after that the local environment settings matter. For example, Microsoft Azure Notebooks supports F#, a language developed by Microsoft. Google’s Colab supports Swift, which is also supported in machine learning projects using TensorFlow. In addition, this lab space has differences in menus and other small functions.
The R language, developed by statisticians and data scientists, is optimized for loading working data sets and then applying algorithms to analyze the data. You can also run R directly from the command line, but most use RStudio to handle the work. R Studio can be said to be an IDE for mathematical operations.
At the heart of RStudio is an open-source workbench for exploring data, modifying code, and creating sophisticated graphics. Because it tracks the user’s operation history, it is possible to rollback or repeat the same command. Debugging is supported when the code is not running. You can also run Python. The RStudio developer is adding features for teams that want to collaborate on shared data sets. Version control, roles, security, synchronization, etc.
Sweave and Knitr
Data scientists writing papers in LaTeX will not be burdened with the complexity of Sweep and Knitter. Both packages are designed to integrate the data processing capabilities of R or Python with the format of TeX. The goal is to create a single pipeline that transforms data into reports with charts, tables, and figures.
This pipeline is dynamic and flexible, but ultimately creates a permanent record. As data is organized, organized, and analyzed, charts and tables are modified. When the results are complete, the data and text are stored in one package that binds the original input and final text together.
Integrated Development Environments
“Genius is 99% hard work and 1% inspiration,” said Thomas Edison. Even 99% of data science consists of organizing data and preparing it for analysis. Here, the IDE is the basis for supporting both mainstream programming languages such as C# and data science languages such as R.
For example, Eclipse users can organize their code in Java, then switch to R and analyze it with rJava. Python developers use Pycharm to integrate Python tools and orchestrate Python-based data analysis. Visual Studio handles plain code with Jupyter notebooks and specialized data science options.
As data science workloads grow, several companies are developing low-code and no-code IDEs for working with data. RapidMiner, Orange, and JASP are examples of tools optimized for data analysis. It utilizes a visual editor, and in most cases you can do everything by dragging an icon. Of course, this can be supplemented with some custom code.
Many data scientists today specialize in a specific area, such as marketing or supply chain optimization, and tools have evolved accordingly. Some tools are focused on a specific area and are optimized for the specific problem the user is facing. Marketers, for example, have numerous options called customer data platforms (CDPs). It integrates with stores, advertising portals and messaging applications to create a consistent, uninterrupted stream of information for customers. Built-in backend analytics provide marketers with statistics to understand campaign effectiveness.
In addition, Voyant analyzes text to measure readability and identify correlations between phrases. AWS Forecast is optimized to use time series data to predict the future of your business. Azure’s Video Analyzer uses AI technology to find answers in video streams.
The rise of cloud computing is a godsend for data scientists. Because sometimes the need to maintain your own hardware to do the analysis is gone. Cloud service providers lend (hardware) to users whenever they need them. If you need a huge amount of RAM for just one day, this is an excellent choice. However, if the project requires continuous analysis over a long period of time, it may be cheaper to purchase your own hardware.
More recently, options specific to parallel computing have emerged (data scientists use GPUs designed for video games in the past). Google, for example, has created specialized Tensor Processing Units (TPUs) to speed up machine learning. Nvidia calls its chips a Data Processing Unit (DPU). Startups such as d-Matrix are also designing hardware specialized for artificial intelligence. Some tasks will be fine with a laptop. But for large projects that require complex calculations, there are now many faster options.
No matter how good a tool is, it is useless without data. Some companies sell curated data collections. There are also companies that sell cloud services (AWS, GCP, Azure, IBM). There are companies that give back data (OpenStreetMap). The US government agency (the Federal repository) sees data sharing as part of their job, and some want to charge a fee for the service. All of these can save people the effort of finding and organizing their own data.
Source: ITWorld Korea by www.itworld.co.kr.
*The article has been translated based on the content of ITWorld Korea by www.itworld.co.kr. If there is any problem regarding the content, copyright, please leave a report below the article. We will try to process as quickly as possible to protect the rights of the author. Thank you very much!
*We just want readers to access information more quickly and easily with other multilingual content, instead of information only available in a certain language.
*We always respect the copyright of the content of the author and always include the original link of the source article.If the author disagrees, just leave the report below the article, the article will be edited or deleted at the request of the author. Thanks very much! Best regards!