The boom in data science continues unabated. The work of collecting and analyzing data was once only for a few scientists in the laboratory. Now every business wants to use the power of data science to streamline its organizations and make customers happy.
The world of data science tools is growing to support this demand. Only a few years ago, data scientists worked with the command line and several good open source packages. Companies are now creating solid professional tools that handle many of the common tasks of data science, such as data cleansing.
The scale is also shifting. Data science was once just a numerical job for scientists after hard work conducting experiments. Now this is a constant part of the work process. Businesses are already integrating mathematical analysis into their business reports and building dashboards to generate smart visualizations to quickly understand what’s going on.
The pace is also accelerating. The analysis, which used to be an annual or quarterly job, is now performed in real time. Businesses want to know what’s going on right now so that managers and employees can make smarter decisions and use everything data science has to offer.
Here are some of the best tools for adding precision and science to your organization’s analysis of its endless flow of data.
These packets of words, code and data have become the lingua franca of the world of data science. Static PDFs, full of unchanged analysis and content, can still be respectable because they create a permanent record, but working data scientists like to open the lid and deal with the mechanism below. Jupyter notebooks let readers do more than learn.
The original versions of the notebooks were created by Python users who wanted to take some of the flexibility of Mathematica. Today, the standard Jupyter Notebook supports more than 40 programming languages and it is common to find R, Julia or even Java or C in them.
The code of the notebook itself is open source, which makes it just the beginning of a number of exciting larger projects for curating data, maintaining coursework or simply sharing ideas. Universities run part of the classes with notebooks. Data scientists use them to exchange ideas and deliver ideas. JupyterHub offers a container-centric authentication server to handle the task of deploying your entire data science genius to an audience, so you don’t have to install or maintain software on your desktop or worry about scaling computing servers.
Laboratory spaces for notebooks
Jupyter laptops don’t just run. They need a home database where the data is stored and the analysis is calculated. Several companies offer this support now, sometimes as a promotional tool and sometimes for a nominal fee. Some of the most famous include Google Colabon Github Code spacesazure Machine learning laboratory, JupyterLabs, Binder, CoCalc, and Datalorebut it’s often not too difficult to set up your own server under your lab table.
Although the core of each of these services is similar, there are differences that may be important. Most support Python in some way, but then local preferences matter. of Microsoft Azure Notebooks, for example, it will also support F #, a language developed by Microsoft. Google’s Colab supports Swift, which is also supported for machine learning projects with TensorFlow. In addition, there are many differences between the menus and other minor features offered by each of these lab spaces for laptops.
The R language was developed by statisticians and data scientists to be optimized for loading working data sets and then applying all the best algorithms for data analysis. Some like to start R directly from the command line, but many like to leave it RStudio cope with many of the obligations. This is an integrated development environment (IDE) for mathematical calculations.
The kernel is an open source desktop that allows you to research data, deal with code, and then generate the most complex graphs that R can assemble. It tracks your calculation history so you can rewind or repeat the same commands, and offers some debugging support when the code doesn’t work. If you need Python, it will also work in RStudio.
RStudio also added Characteristic to support teams that want to collaborate on a shared data set. This means version management, roles, security, synchronization, and more.
Sweave and Knitr
Data scientists who write their articles in LaTeX will enjoy the complexity of Sweave and Knitr, two packages designed to integrate the power of R or Python data processing with the elegance of TeX formatting. The goal is to create a pipeline that turns data into a written report full of charts, tables and graphs.
The pipeline is designed to be dynamic and fluid, but ultimately to create a permanent record. As the data is cleaned, organized and analyzed, the charts and tables are adjusted. When the result is complete, the data and text are collected in one package, which combines the raw input and the final text.
Integrated development environments
Thomas Edison once said that genius is 1% inspiration and 99% sweating. It is often felt that 99% of data science simply cleans data and prepares it for analysis. Integrated Development Environments (IDEs) are a good foundation because they support mass programming languages such as C #, as well as some of the more data-focused languages such as R. Eclipse users, for example, can clear their Java code after this to turn to R for analysis with rJava.
Python developers rely on Pycharm to integrate their Python tools and organize Python-based data analysis. Visual studio juggles plain code with Jupyter Notebooks and specialized data science options.
As the workload in data science increases, some companies are building low-code, no-code IDEs that are tuned for much of this data work. Tools like RapidMiner, Orangeand JASP are just some of the examples of excellent tools optimized for data analysis. They rely on visual editors and in many cases it is possible to do everything just by dragging icons. If that’s not enough, a little custom code may be all you need.
Many data scientists today specialize in specific areas such as marketing or supply chain optimization and their tools follow. Some of the best tools are closely focused on specific areas and are optimized for specific problems faced by anyone who studies them.
For example, merchants have dozens of good options that are now often referred to as customer data platforms. They integrate with storefronts, advertising portals and messaging applications to create a consistent (and often relentless) flow of information to customers. Built-in back-end analysis provides key statistics that retailers expect to assess the effectiveness of their campaigns.
There are now hundreds of good domain-specific options that work at all levels. Voyantfor example, it analyzes text to measure legibility and find correlations between passages. AWS Forecast is optimized for predicting the future of the business using time series data. Azure Video analyzer applies AI techniques to find answers in video streams.
The rise of cloud computing options is a find for data scientists. You don’t need to maintain your own hardware just to do analysis from time to time. Cloud providers will rent you a machine every minute just when you need it. This can be a great solution if you need a huge amount of RAM in just one day. However, projects with a constant need for long-term analysis may find it cheaper to simply purchase their own hardware.
Recently, more specialized options for parallel computing have emerged. Data scientists sometimes use graphics processing modules (GPUs), which were once designed for video games. Google makes specialized Tensor processor (TPU) to accelerate machine learning. Nvidia calls some of its chips “Data processing units”Or DPU. Some start-ups, such as d-matrix, design specialized hardware for artificial intelligence. A laptop may be good for some work, but large projects with complex calculations now have much faster options.
The tools are not very good without the raw data. Some companies try to offer selected data collections. Some want to sell their cloud services (AWS, GCP, azure, IBM). Others see it as a form of return (OpenStreetMap). Some are U.S. government agencies that see data sharing as part of their work (Federal repository). Others are smaller, such as cities that want to help residents and businesses succeed (new York, Baltimore, Miamior Orlando). Some just want to charge for the service. All of them can save you the trouble of finding and cleaning the data yourself.