The 18 Best Online Resources for Data Science in 2024

data science resources

Getting enrolled in an online data science certification program is the most convenient way to learn data science today. As data scientists are among the most in-demand professionals today, it has become a considerable career option for many youngsters. To make a successful career in the field, only theoretical knowledge is not enough. You should also be aware of the latest tools and technologies being used in data science. The digital world is vast and you can find the answers and solutions to almost everything through a web search. If you aspire to become a data scientist, you can learn all the necessary skills through online certification programs. Moreover, plenty of data science resources are available online that make your tasks easier as a data scientist. Here is a list of the 18 best online resources and tools for Data Science that you should be aware of:

1. IBM DataCap

IBM Datacap is a powerful tool that data scientists use to acquire documents, gather information, and transfer documents to other business processes at the backend. It helps organizations in improving flexibility, automation, and accuracy in their business processes. The tool uses ML (machine learning), text analytics, and NLP (Natural Language Processing) techniques to automate the process of identification and extraction of useful information from unstructured documents.

2. Amazon RedShift

Amazon RedShift is a fully managed cloud data warehouse service that allows data scientists to extract meaningful business insights and customer information from data. Being one of the most powerful data science software, it enables organizations to scale from a few hundred GBs of data to much more. Using the nodes called Amazon ResShift clusters, the tools allow you to upload data sets to the warehouses and then perform analysis on the same.

3. ChatGPT

ChatGPT is one of the best platforms to learn the basics of data science and get assistance with analysis tasks. It is an AI-driven chatbot having answers to almost every question. You can ask questions about data science techniques and get detailed answers for the same. You can even ask follow-up questions and ask ChatGPT to explain more if you are not satisfied with the chatbot’s answers. Data scientists can even use it to create data visualizations and to get code for building ML models.

4. Microsoft Azure

MS Azure is one of the most popular cloud services that is used to create, manage, and deploy applications on a large global scale. It combines a variety of services, such as AI, ML, data analytics, hybrid integration, data storage, etc, to build a data warehouse. Having key features like DR (Disaster Recovery), this software competes with the top platforms, such as AWS (Amazon Web Services), IBM, and GCP (Google Cloud Platform).

5. MySQL

MySQL is an open-source RDBMS (Relational Database Management System) that uses SQL programming language to administer databases and for data queries. Data Scientists can use MySQL databases to implement data warehousing solutions as these databases provide them with convenient methods to analyze data. They can clean, store, and visualize data directly from the database.

6. KNIME Analytics Platform

KNIME is another open-source platform for data scientists, which makes data workflows accessible to every user without any programming requirements. It allows you to build visual workflows easily with its drag-and-drop user interface. It supports a wide range of data, including PDF, CSV, XLS, time series data, unstructured data, and many more. You can implement different methods like correlation analysis, statistical analysis, etc, to modify and shape your data for analysis.

7. Google BigQuery

BigQuery is a serverless data warehousing tool that can be used for data analysis and processing. As there is no infrastructure to manage, it allows data scientists to completely focus on driving meaningful information from data. With its in-memory BI engine, the tool also provides fast dashboards and reporting. It allows data scientists to share data insights with other team members in the form of datasets, queries, spreadsheets, and reports.

8. MS Power BI

Power BI is a widely used analytics service offered by Microsoft that data scientists can use to create visualizations in the cloud. You can produce eye-catching data visualizations and share them with other team members with any device. One of its key features is the AI assistance offered by this software which helps non-technical users to prepare structured/unstructured data and produce visualizations.

9. Google Fusion Tables

A Google web service for data management, fusion tables, is useful in gathering, visualizing, and sharing data tables that others can access and download. The tool offers various types of visualization options, including pie charts, bar graphs, histograms, geographical maps, line plots, scatter plots, etc. Users can produce creative visuals and export them as CSV files. Its key features include instant maps, combined tables, online hosting, etc.

10. Jupyter Notebook

Jupyter Notebook is an open-source web application that allows data scientists and other professionals to collaborate and work together on specific data science projects. With this notebook tool, you can write, edit, or share code, text, and even images with others. The notebooks can be used in interactive sessions between data science teams. The roots of this software lie in Python as it initially was a part of the IPython interactive toolkit. Also Read: Skills required for a megabucks career in data science

11. Matplotlib

Matpltlib is also an open-source Python library that allows users to create interactive, static, or animated data visualizations. This tool majorly focuses on creating 2D visualizations but also offers an additional toolkit for producing 3D visuals. It can be quite challenging to master the large code base of Matplotlib, but it has been organized in such a way that most visuals can be produced using high-level commands.

12. NumPy

NumPy stands for Numerical Python, which is also an open-source Python library majorly used in scientific computing, machine learning, and data science applications. It supports random number generation that is useful in sampling, linear algebra, and various other operations. The tool is widely known for its speed, which is a result of optimized C code. Moreover, NumPy is also useful for building various other Python libraries.

13. Pandas

Pandas, again a Python library, is used by data scientists for the analysis and manipulation of data. It is built on top of NumPy and features two types of data structures, including the Data Frame and the Series One dimensional array. Both these structures support data from ndarrays of NumPy and several other inputs. Pandas is considered a powerful tool for data scientists due to its exploratory data analysis functions, built-in data visualization capabilities, and various other features.

14. PyTorch

PyTorch is an open-source Python library that is useful in training neural network-based deep learning models. It is designed to be faster and more flexible than the original library Torch. Some most significant functions and techniques of this library include a module for building neural networks, an automatic differentiation package, and a TorchServe tool that can be used to deploy PyTorch models.

15. SAS

SAS is an integrated statistical software that data scientists can use for advanced analytics, business intelligence, predictive analytics, data management, etc. It allows users to cleanse and prepare data for analysis using different statistical and data science techniques. The tool was initially developed for statistical analysis, but its functions expanded over time and it eventually became one of the most popular software used by data science professionals.

16. TensorFlow

Designed by Google, TensorFlow is a popular machine learning platform that is used to implement deep learning neural networks. It takes tensors as inputs and then flows this data through various computational operations using a graph structure. It uses Python as its main programming language and also includes Keras high-level API to build and train models.

17. Scikit Learn

Scikit Learn, an open-source ML library for Python, is built on NumPy and SciPy scientific computing libraries. The tool also involves Malplotlib for plotting graphs or charts for the data. It supports supervised as well as unsupervised machine learning. Scikit Learn majorly focuses on numeric data that is stored in SciPy sparse matrices and NumPy arrays.

18. Weka

Weka is an open-source workbench that can be used for a number of applications, including clustering, regression, classification, data mining, etc. It also includes various data visualization and preprocessing tools. The tool allows users to integrate with Python, R, Spark, and several libraries like Scikit Learn.

Learn Data Science at Edvancer

  Edvancer is one of the best platforms you should consider for online data science certifications. Being one of the leading career-oriented learning platforms, Edvancer offers four courses in data science as listed below: With these courses, you get a complete understanding of what the data science field is all about, how it works, and what are its real-world applications. These programs also allow you to gain practical knowledge by working on real industry projects and assignments. Students can also choose one of the two learning styles offered by Edvancer, including self-paced learning and live online classes, as per their comfort.


1. Which online website is best for data science? Ans. The best online platform to learn data science is Edvancer, which aims to provide high-quality career-oriented education to youngsters. 2. Is it better to do a data science course online or offline? Ans. Though online as well as offline courses can prepare you for a data science job, online courses have so many advantages over offline ones. With an online data science course, you get more flexibility to learn at your convenience and these are also more cost-effective.Share this on

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