Sometimes having an exam scheduled and 2 pages of topics you must master for it, is a good and structured way to go deep

The list can go on and on, but the point is that instead of doing the same task several times (maybe even doing it manually each time), we develop a robust, reusable pipeline. It can be used by ourselves in the future or handed over to our colleagues (obviously, if they know Python). Moreover, Python script is usually transparent and readable if created properly, so it should be easy for our colleagues or supervisors to go through each step of our analysis and get what is going on there. That is a great advantage compared to Excel, as Excel does not allow us to see all these intermediate steps.

Plenty of brilliant off-the-shelf analytical solutions (including libraries for ML/DL) Speaking about ready-to-use solutions, analysts are often used to working with such, compared to developers who are comfortable designing their own. That is, the existence of numerous great Python libraries for data analytics is a great benefit.

Here I am talking not only about the traditional definition of Big Data (3 Vs) but also about any data too big to fit into Excel (over ~1 mln rows). This is something that I also have encountered a lot. The majority of large corporations (not only digitally-advanced ones) have already generated enough data to call it “Big”, and they predictably want to get insights from this data. So when traditional business analysts are unable to do it, these corporations turn to data scientists for answers. I understand that some of these questions may be extremely complicated and indeed require the data specialists’ involvement, but is it always the case? I believe that it’s not. Sometimes the task is as easy as to open a few 5Gb datasets, merge them, make some pivots, and draw the slides/ charts on their base. Of course, it may sound like a problem if you only know how to use Excel, but if you are a Python-user, you would have easily cracked this task and saved some money and time for your organization.

On a closing remark, one of the things I love about Python is its open-source principles. There is a wide community of professionals who openly share their ideas and groundwork, help others, and create some great projects together. This idea was confusing for me at first as for a person from the traditional corporate world, but later I have realized its enormous potential. That is why I am trying to motivate people to learn Python and join this great community.

This for me is one of my favorite ways to keep up my continuous learning and improvement! Really making use of the work environment for knowledge share! Either being a mentor, that really makes you step up your game, or having someone that can guide you in your first steps! This can also work by just finding a colleague that is on the same path as you, and so instead of you and your head find the best resources now there are 2! Also it is so much more fun!

Relatively simple to learn When compared to other programming languages, some might say that Python is quite easy to learn. It has a relatively simple syntax and plenty of great learning resources.

It happened many times in my practice when I simply needed to draw some charts based on such large databases as ticket sales distribution, customer information, SKU data, and so on. It was crucial to show the full picture instead of some sample, so Big Data-processing skills were necessary.

Could these tasks be solved in Excel using linear regression? Well, with some degree of creativity, maybe…but I will not be sure about the quality of such work. More advanced modeling in Python will certainly give us higher flexibility and very likely better results.

This for me is one of my favorite ways to keep up my continuous learning and improvement! Really making use of the work environment for knowledge share! Either being a mentor, that really makes you step up your game, or having someone that can guide you in your first steps! This can also work by just finding a colleague that is on the same path as you, and so instead of you and your head find the best resources now there are 2! Also it is so much more fun!

Huge community support Obviously, Python is very popular. That is why, when you run into some problem, there is a high possibility that someone had run into the same problem before. Therefore, you can always find help or maybe even a ready-to-use solution.

Nobody likes doing routine repeatable tasks. Well, maybe somebody does, but it is definitely not a common trait for analysts as we often prefer to delegate these tasks if possible. This is where Python comes to help because it allows us to automate many processes. Here are some examples I have encountered:

Why particularly Python? To be honest, almost any programming language might be suitable, and I am not going to cover this topic in detail here. But I do believe that Python is the best choice because of several reasons:

If somebody asks you about the most important hard skills for business analysts, which ones would you name? Well, in management consulting, investment banking, and many other analytical jobs it usually is (or at least traditionally was) Excel and PowerPoint. This software is certainly convenient and often efficient for data analysis and visualization, which is the solid reason it is widely accepted as a standard. However, numerous limitations have been becoming more and more clear in recent years. Data volume and complexity grow exponentially, which calls for more sophisticated data processing tools such as programming languages. In this article, I will focus on Python and explain, based on my work experience in management consulting, why I believe it to be an essential skill for business analysts right now and maybe even a critical skill in the future.

http://startup.munich.es/mlt/video-HC-Ambri-Piotta-SC-Bern-v-en-gb-yyw-.php

http://startup.munich.es/mlt/Video-HC-Ambri-Piotta-SC-Bern-v-en-gb-ldx30122020-.php

http://main.ruicasa.com/tgq/video-red-bull-v-dornbirner-v-at-at-1gyu-25.php

http://www.ectp.org/mdt/videos-saski-baskonia-v-alba-berlin-v-de-de-1mtc-4.php

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http://team.vidrio.org/myp/videos-Rapperswil-Jona-Lakers-ZSC-Lions-v-en-gb-vzi-.php

http://team.vidrio.org/myp/videos-Rapperswil-Jona-Lakers-ZSC-Lions-v-en-gb-drk-.php

http://team.vidrio.org/myp/video-Rapperswil-Jona-Lakers-ZSC-Lions-v-en-gb-nom30122020-.php

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a fair test, so we created a very simple microservice with no external dependencies (like a database for example), and with very short code paths (just manipulating strings). We did include metrics and logging, since these seem to be always included in any real microservice. We used small, lightweight frameworks (Helidon for Java and Go-Kit for Go) and we also experimented with pure JAX-RS for Java. We experimented with different versions of Java and different JVMs. We did some basic tuning of the heap size and garbage collector. We warmed up the microservices before test runs.

That’s the most exciting part for me, but the least often used, to be honest. Nevertheless, sometimes it could be quite important for success. The underlying reason is the same as for the previous argument — corporations have data and want insights. Some of these insights could not be uncovered without machine learning, as they are too complex to be manually found by human analysts. Some tasks of this type I have been doing: