Saturday, December 3, 2022

Top data science tools for Python learners

If you are pursuing data science with Python courses, you require a solid foundation in the technologies and techniques. In the recent times, we have seen how data scientists have completely relied on top grade enterprise software platforms to get their job done with accuracy and better productivity. In this article, we will touch base with some of the most highly ranked data science software platforms for Python learners.

Microsoft Excel

Excel skills are a must have to crack the difficulty levels in Python courses. Most beginners are trained in Excel with Python libraries. Not only is Excel an effective tool for data science and analysis, but it is also able to handle advanced AI and machine learning applications that are useful in modern business domains such as employee data management, marketing research, sales forecasting, financial data analysis, and data reporting. 

Pro tip: To master data science with Python courses, you should learn as many Python libraries and other supporting programming languages within Excel systems. It takes less than 6 months to completely get a wrap around data science, Python, and Excel skills.


If you are looking for an extremely analyst friendly platform for your data science projects, Python experts recommend RapidMiner. RapidMiner maximizes the value of your data management systems by automating a bulk of workflow operations. Even with Python coding, this works fluidly, as analysts are able to design their own workflow processes using drag and drop features, visual reporting, and Machine learning operations. The advantage of RapidMiner for Python coders is simply hard to ignore as more and more programmers are leveraging this platform using Python Extension Setup for PyCharm, NumPy and others.

Pro tip: If you are looking to build an AI based data driven product that requires optimization of costs and resources, Python with RapidMiner is a remarkable option to other conventional data science tools that are more commonly available for model building, collaboration, AI based app building, and much more. 


Tableau is now owned by Salesforce. Though mostly unrelated to the traditional Python programming applications for a very long time, modern data scientists have begun to adapt to Tableau’s expanded offerings. These offerings allow integration with Python, Panda, SQL, and other kinds of data engineering frameworks required to build an enterprise level data management system. 

If you are looking to build a model for business analytics, tapping the Tableau system with Python libraries is a great approach. This would allow you to see a wider horizon in the analytics world where you could bring in the basics of Tableau data management techniques to automate a part or the full operation in matters of hours, and not days or weeks.

Pro tip:

In the data science with Python course, take a shot at TabPy. The tableau community of software developers and analysts constantly comes up with new Python guidelines for Tableau integration, Python automation, and visualizations. 


Learning to adapt to neural networks and memory-mapping algorithms for advanced AI projects? In data science courses, Python’s open source libraries are very useful. There are many libraries that provide an access to code-free algorithms. One such platform is PyTorch. In the recent years, PyTorch applications have given large scale opportunities to Python learners in the fields of Graph Based executions, AI hardware designing, Embedded analytics, Auto Machine Learning modeling, and so on. 

It is easily integrated into all the above listed data science tools, and Python libraries such as NumPy, SciPy, Cython, and others. It can be used for reverse automation workflows which means you can engineer distributed data sets for advanced GPU and CPU systems for optimized run time and flexibility spread across various types of machine learning and deep learning frameworks.

More options

Apart from the above listed tools and platforms, there is more to look out for. For example, you can always rely on Microsoft Azure’s growing repertoire of data science tools. Microsoft-certified data scientists are among the best paid professionals in the world. Moreover, Python coders often take to Azure backed certifications to excel in this field. You could also opt for Red Hat, Databricks, Alteryz, SAS, RStudio, Matplotlib, and others.

Pro tip: 

Whether you are starting in data science now, or plan to do so in the coming years, getting Python training is a great asset to acquire today. Modern AI models are tapping the skills of Python designers to extend the scope of other AI and data science tools like KNIME, TensorFlow, Keras, and so on.