Python, Tensorflow, Excel and more - Data professionals reveal their top tools

Data professionals are constantly on the lookout for the best tools to simplify their data science tasks - be it data acquisition, machine learning, or visualizing the results of the analysis. With so much on their plate already, having robust, efficient tools in the arsenal helps them a lot in reducing the procedural complexities. Not just that, the time taken to do these tasks is considerably reduced as well.

But what tools do data professionals rely on to make their lives easier? Thanks to the Skill-up 2018 survey that we recently conducted, we have some interesting observations to share with you!

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Key Takeaways

• Python is the most widely used programming language by data professionals
• Python finds a wide adoption across all spectrums of data science - including data analysis, machine learning, deep learning and data visualization
• Excel continues to be favored by the data professionals because of its effectiveness and simplicity
• R is slowly falling behind Python in the race to Data Science supremacy

Now, let’s look at these observations, in more depth.

Python continues its ascension as the top dog

Python’s rise in popularity as well as adoption over the last 3 years has been quite staggering, to say the least. Python’s ease of use, powerful analytical and machine learning capabilities as well as its applications outside of data science make it quite a popular language in the tech community. It thus comes as no surprise that it stood out from the others and was the undisputed choice of language for the data pros.

R, on the other hand, seems to be finding it difficult to play catch-up to Python, with less than half the number of votes - despite being the tool of choice for many statisticians and researchers. Is the paradigm shift well and truly on? Is Python edging R out for good?

Source: Packt Skill-Up Survey 2018

It is interesting to see SQL as the number 2, but considering the number of people working with databases these days it doesn’t come as a surprise. Also, JavaScript is preferred more than Java, indicating the rising need for web-based dashboards for effective Business Intelligence.

Data professionals still love Excel, but Python libraries are taking over

Microsoft Excel has traditionally been a highly popular tool for data analysis, especially when dealing with data with hundreds and thousands of records. Excel’s perfect setting for data manipulation and charting continues to be the reason why people still use it for basic-level data analysis, as indicated by our survey. Almost 53% of the respondents prefer having Excel in their analysis toolkit for their day to day tasks.

Top libraries, tools and frameworks used by data professionals (Source: Packt Skill-Up Survey 2018)

The survey also indicated Python’s rising dominance in the data science domain, with 8 out of the 10 most-used tools for data analysis being Python-based. Python’s offerings for data wrangling, scientific computing, machine learning and deep learning make its libraries the obvious choice for data professionals.

Here’s a quick look at  15 useful Python libraries to make the above-mentioned data science tasks easier.

Tensorflow and PyTorch are in demand

AI’s popularity is soaring with every passing day as it finds applications across all types of industries and business domains. In our survey, we found machine learning and deep learning to be two of the most valuable skills to have for any data scientist, as can be seen from the word cloud below:



Word cloud for the most valued skills by data professionals (Source: Packt Skill-Up Survey)

Python’s two popular deep learning frameworks - Tensorflow and PyTorch have thus gained a lot of attention and adoption in the recent times. Along with Keras - another Python library - these two libraries are the most used frameworks used by data scientists and ML developers for building efficient machine learning and deep learning models.

Which language/libraries do you use for your everyday Data Science tasks? Do you agree with your peers’ choice of tools? Feel free to let us know!

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