Certainly! Jupyter Notebook paired with Python provides a convenient and efficient method for code execution, allowing you to assess outputs incrementally. This is particularly beneficial when utilizing libraries like Matplotlib for initial 2D visualizations and subsequently transitioning to Seaborn. As your needs evolve to encompass 3D visualizations, these libraries remain useful. Additionally, exploring R can be advantageous for proficient data analysis.

I also used a business data visualisation Tableau had an easier interface and didn't need to code alot.

Yes but use replit

Dear Alžbeta Simon

Surly, using Python and Jupyter Notebook can help you to obtain data visualization techniques using Matplotlib and Seaborn libraries so these libraries could able to draw different graphs.

Tableau is another software with this aim although it has very user friendly GUI but it has some bugs specially with versions after 2019 so do not advise you to use it.

For Data analysis. I think excel, power BI or table can be okay. Then you will gradually grow into python

Microsoft PowerBI, ERD, RDBMS (Access, mySQL)

JASP is a free tool.

Try WEKA too

Maybe phyton... That mt recomend

python exploratory data analysis using Notebook is best way to do this.

I am with Jupyter notebook /python.

Its free and have good community support.

They have special packages for two or multidimensional data and interesting colormaps.

For data analysis and creating charts, most people use spreadsheets, but I assume spreadsheets are not an option for you, because of the amount of data you need to process. Otherwise, that's totally fine. Another option, besides Python, could be Octave or R. Some colleagues like a language called Julia.

My favorite tool for data analysis and visualization is Matlab. It has many toolboxes for data analysis and also integration with several programming languages and databases including Python.

For plotting graphs definitely it is matplotlib.

And sometimes you need to plot different kinds of graphs on different scenarios

Software for Data Analysis and Chart Creation:

• Python: Python is a highly recommended language for data analysis. Libraries like Pandas, NumPy, and Matplotlib/Seaborn make it a powerful choice. Jupyter Notebook provides an interactive environment for data exploration and analysis.
• R: R is another popular language for data analysis, especially in statistics and data visualization. It has a strong ecosystem of packages like ggplot2 and dplyr.
• Excel: Excel is widely used for basic data analysis and chart creation. It's user-friendly and suitable for small-scale tasks.

Experiences with Python, Pandas, and Jupyter Notebook:

• Python: Python is a versatile language used in various domains, including data science. Learning Python is a valuable skill not only for data analysis but also for web development, automation, and more.
• Pandas: Pandas is an essential library for data manipulation and analysis in Python. It provides data structures like DataFrames that simplify working with structured data. It's highly regarded in the data science community.
• Jupyter Notebook: Jupyter Notebook is a fantastic tool for interactive data analysis and sharing results. It allows you to combine code, visualizations, and explanatory text in a single document.

Recommendation:

Learning Python, Pandas, and Jupyter Notebook is an excellent choice for anyone interested in data analysis and data science. Here's why:

So, yes, I would highly recommend learning Python, Pandas, and Jupyter Notebook if you are interested in data analysis and data science. It's a valuable investment in your career, and you'll find it worth the effort.