Hi, data scientists.
I bet you have been looking for methods to boost efficiency. That’s what I am going to share in this post – the best tools for data scientists to get jobs done faster.
Efficiency saves time. In the meantime, it enables us to deal with a greater amount of data. With a larger sample, we can get a bit closer to the truth. In this sense, as long as you can get a hang of it, a tool that works for data scientists works for everyone who deals with data.
In this post, I will introduce a kit of 10 effective tools. They make data collection, data analysis, and data visualization faster and easier.
Automation Tools for Data Collection
Data collection is the very first step in a data science program. And data collection could be extremely time-consuming if you are doing it under these conditions:
- Copying data across pages or screens
- Collecting data from multiple sources
- Collecting data from platforms where batch export is not allowed
- Gathering data that is so raw that needs heavy cleaning
Collecting data manually is wasting human life.
A robot/script can do this perfectly well. So don’t hesitate to outsource it to ‘the professional’ and free yourself from endless clicking and pasting.
BeautifulSoup is a Python library that extracts data from HTML and XML files. It builds a sort of parse tree for the parsed page. In this way, we can select the elements and extract the data we are interested in.
- Advantages: For people with a coding background, it’s a ready-to-use framework and it is simple to learn and understand.
Selenium is a Python library that provides API to access Selenium Web browsers like Chrome, Mozilla, Microsoft Edge.
- Disadvantages: You need to have already good knowledge of Python, HTML, and the XPath syntax.
Octoparse is an amazing web scraping software for web scraping any type of website without any coding knowledge. When you download and launch it, the interface presents some video tutorials to let you see how to exploit this powerful tool.
- Advantages: You don’t need any previous coding knowledge. It has a user-friendly interface and its auto feature recognizes the elements you want to extract automatically.
- Disadvantages: Even if it’s easy to use, at the beginning it needs a little time to understand how to set up the tool and complete the web scraping tasks.
Data Visualization Tools
In a data science project, the second step is to explore data to get insights. In real-world cases, you don’t have previous information about your dataset, and the only way to understand the business problem is by displaying data visualizations.
At first, this task seems boring but it allows you to reach knowledge that you couldn’t obtain by looking only at the raw data. Below I suggest some tools to automate this step, saving time that can be used for other tasks.
1. Power BI
Power BI is a Microsoft collection of different products that work together to build interactive visualizations from any type of source data. There are two principal products, the Power BI Desktop and the Power BI service. The Desktop version is software that needs to be installed on the local PC and can create dashboards and reports, while the Power BI service is the cloud version of Power BI to share reports and collaborate with other users.
- Advantages: No programming language is needed. It allows the creation of two types of workspaces, the personal workspace and the workspaces used to share the dashboards/reports with the colleagues in the cloud version of Power BI. There is also the possibility to share data visualizations using the mobile app. Another advantage is that it provides the Power Query Editor, which allows managing queries between the different data collections, group, and filter data.
- Disadvantages: The Pro or the Premium subscription is needed to share the content across the Power BI service, which includes also the mobile app. The storage capacity for the workplace is limited (10 GB) for free and Pro users. To increase the capacity, a Premium subscription is needed.
Like Power BI, Tableau consists of a collection of products, that provides interactive data visualizations and allows to work with different types of data sources. Principally, it’s composed of two products, Tableau Public and Tableau Desktop. Tableau Public is free and allows the creation of interactive data visualizations for the web, while the Tableau Desktop is only available with a subscription, needs to be installed on the PC, and, consequently, build dashboards and reports on the local computer.
- Advantages: It has an intuitive and nice graphical interface for data visualization purposes. Moreover, the paid version of Tableau has unlimited storage capacity. Like Power BI, it allows sharing the dashboards and the reports on mobile devices.
- Disadvantages: The free version of Tableau has a limit of 15 million rows. So, it’s more adapt for small projects.
Dash is an open-source library, that allows building gorgeous web-based interfaces visible in the browsers. These web apps can be built and developed using programming languages, like Python, R, Julia. The most used and popular language is Python.
- Disadvantages: you need to know at least one of the allowed programming languages to build these applications. So, it takes time to implement the dashboard. Another drawback is that it’s still not able to share the reports in mobile apps for now.
When the data collection and the exploratory analysis have been done, we can finally perform more advanced tasks, like medical image analysis, speech recognition, groups, and recommendation system. Since Python is the most popular programming language used for these tasks, I introduce three tools mostly used to build ML and DL models, while the fourth tool is more focused on identifying the best hyperparameters of the models, that can allow improving their performance.
Scikit-Learn is one of the most popular open-source libraries used to build machine learning models. It’s based on NumPy, SciPy, and matplotlib libraries.
- Advantages: it supports a wide range of models. Most of the available approaches are supervised, applied for regression and classification tasks, like Stochastic Gradient Descent, SVM, Random Forest. It also provides unsupervised techniques for clustering or dimensionality reduction. Moreover, it permits to improve the performance of the models through hyperparameter optimization techniques, like Grid-Search and Random-Search.
- Disadvantages: It doesn’t provide automatic hyperparameter optimization techniques, that demonstrated to define search spaces of the hyperparameters dynamically and efficiently. Another drawback is that parallelism and serialization are hard with this library.
Tensorflow is an open-source and freely available Python library, developed originally by Google Brain Team in 2017. In the beginning, the purpose of this library was to execute a graph for numerical computations, but it’s widely known for designing and training deep learning models.
- Advantages: it provides a high-level API, called Keras, that allows creating of deep learning models. In this way, they can be built with few lines of code, making it user-friendly and is intuitive even for people starting learning data science. These architectures can be built using TPU, which allows performing computations faster than GPU and CPU. Another important feature is that it provides a library used for data visualization of the training process, called TensorBoard. The visualizations are useful to evaluate the performance of the model.
- Disadvantages: To build advanced deep learning architecture, the small amount of code provided by Tensorflow can constitute a drawback since it doesn’t allow to add extra functionalities. Moreover, there are frequent updates every 1–2 months. This aspect can be a problem because it breaks backward compatibility.
Pytorch is an open-source Python library like TensorFlow, but based on the Torch library and developed by Facebook’s AI Research lab. Like Keras, it constitutes one of the widely used libraries used to design deep learning models.
- Advantages: Pytorch is simple to learn for people that have already a good knowledge of Python. It also has a strong GPU support, allowing the models to train fast. Moreover, many pre-trained models can be applied easily to solve problems.
- Disadvantages: It’s not easy to learn for Python beginners and doesn’t provide data visualizations of the training process. To provide plots, it needs an external library, called Visdom, but it’s limited compared to the one provided by TensorFlow.
Optuna is an automatic hyperparameter optimization software framework, which helps to improve the performance of Machine Learning and Deep Learning models. The integration modules are available for all the libraries shown previously, Scikit-Learn, Pytorch, and Tensorflow.
- Advantages: it allows defining search spaces for the hyperparameters dynamically and uses pruning to discard low-quality trials easily. Moreover, the hyperparameter tuning is much faster than the sci-kit learning methods provided for that purpose, like Grid-Search.
- Disadvantages: there aren’t many code examples available since it’s has been developed recently in 2019.
I hope that this guide helped you to discover some tools you didn’t know to solve automation, visualization, and analytics tasks. There are surely other tools I didn’t cover here, but these are the principal tools I used until now. I hope you linked the article.
Thanks for reading!
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