During the progress of any Data Science project, most data scientists tend to utilize tools and gadgets that would help them reach their goals faster and more efficiently. They use these tools to speed up routine tasks to save their energy and brain-power to find solutions for the current problem they are trying to solve.
Because of this desire to speed up a project’s workflow, there are so many data science tools out there that you can choose from, whichever suits the task at hand. And believe me, when I say this, there are hundreds of tools you can choose to finish your project; at the end of the project, you will discover that you used multiple of these tools to finish one project.
5 Data Science Programming Languages Not Including Python or R
Since any data science project consists of different steps, from gathering and collecting data to clean it, analyzing, and visualizing it, there are tools designed and developed for each of these steps. Tools to automatically collect data for you from all over the web, or tools to visualize your data and help you tell the story hidden within, or tools to help you clean your data and use the most relevant part of it in your analysis.
This article will take a look through the data science tools catalog and talk about 7 of the most used tools by data scientists today. Maybe one of these tools can help you in completing your next project.
№1: IBM Watson Studio
The first tool on our list is the monster tool, IBM Watson Studio. Watson Studio is a collection of tools and APIs designed to accelerate, including Machine Learning and deep learning techniques to your application. IBM Watson Studio has both free and paid plans depending on your needs, the size of your project, and your team.
Watson Studio offers many tutorials on various concepts and APIs available. Most of these tutorials are flexible and can be done from your browser, so no need for any installation. Watson Studio offers tools to prepare, manage and analyze your data. It also offers tons of datasets, models, and tutorials for you to use in your project.
№2: Amazon Redshift
Data Science is all about data, and in most projects, the amount of data you need to handle is quite large. Our next tool on the list, Amazon Redshift, is a cloud service that allows you to scale your projects so they can handle large-scale datasets. Once your data is uploaded to Redshift, you can analyze it and perform queries on it.
There are many benefits of using Amazon RedShift as the home to your data; you can encrypt your data to keep it secure among these benefits. You can easily increase the number of nodes in your dataset, and the tool has no up-front cost. And even when you do use one of the paid plans, the tools offer on-demand pricing to eliminate long-term commitment.
№3: Google BigQuery
The next tool on our list is another data storage and query platform that is BigQuery. Google’s BigQuery is a scalable, serverless data warehouse tool. The tool is designed to allow data scientists a productive and efficient analysis of their data. The tool helps developers uncover patterns and trends by creating dashboards and reports fast and easily.
Developers who use BigQuery do so because of how fast and simple it is to analyze that data efficiently and how seamlessly it is to scale due to the warehouse being serverless. BigQuesry is a paid service, but they promise that the price for the service they provide is unmatched by other services.
№4: Microsoft Azure
Next up is Microsoft Azure. Microsoft Azure is a very well-known cloud service that only grows in both features and user base. This tool offers many options for developers to design, build and deploy applications hassle-free. Microsoft Azure is a collection of tools, some store data, some analyze, and some integrate AI and machine learning techniques.
All tools offered by Microsoft Azure are priced with a pay-as-you-go model that only allows you to pay as you use. Not just that, but you can use the Azure Cost Management tool to optimize the money you spend paying for Microsoft Azure services.
№5: Snowflake
Or last data warehouse on the list is Snowflake. Snowflake is a relational ANSI SQL data warehouse that the developer can use to optimize their communication with the database, from reading items to deleting them and even performing some analytical queries.
Using Snowflake has so many advantages, including eliminating any administration and management demands because there’s no infrastructure in Snowflake to manage. Snowflake also supports all forms of data that you may use in your project with seemingly easy support for scaling and sharing the data.
№6: Alteryx
One of the main steps in any data science project is data analysis. Out next tool is Alteryx, a data analysis tool that allows you to search your data for relevant information and easily find and manage any information held within your data. It allows you to analyze data from multiple sources simultaneously; you can import data from both Excel and Hadoop and analyze them in the same place.
Alteryx has more than 60 built-in tools for all data analytics needs, from regression to clustering and categorization. You can also build your own tools in Alteryx using Python or R. Alteryx also give you the ability to visualize your data by creating reports in commonly used formats like Qlik, Microsoft Power BI, and Tableau.
№7: Qlik
Our last tool on the list is a data visualization tool. Data visualization is an essential part of any data science project; it can make or break your project. The data visualization tool on this list is Qlik. Qlik is a visual analysis tool that allows you to create dashboards and visualization that help tell your data’s story.
Using Qlik, developers can create interactive visualizations using a simple and fast drag and drop interface. Qlik is more than just a data visualization tool; it’s a centralized hub that gives you the ability to unify data from different databases and create a visual analysis of their content. You can also embed Qlik in your application for automated data capture and analysis.
Final Thoughts
When you get into the data science field, you often start by learning the basic concepts and a programming language to use, whether it be Python, R, Julia, or any other languages. But, when you start building real-life applications, you will realize that most of the tasks you must do in any project are so routine and don’t require your full energy.
The main part of the problem – applying models, training, testing, and optimizing them – needs your full focus, and it:s probably the part that will take the longest time to complete. That’s why many tools have been developed to help data scientists efficiently complete their projects without wasting any time or brain-power on routine tasks.
In this article, we discussed 7 of the hundreds of tools out there most used by data scientists to speed up their workflow and help them build their projects efficiently and hassle-free. I hope you can utilize these tools in your next project, and I hope they will help you reach your goal faster.