• After the initial pain, and maybe a bit of a slow start, your thesis will advance ten times faster and you will be capable to achieve more than you can imagine.
• What you will be learning will not be lost. Many companies use a similar environment (if not the same).
• Many of these resources are free and you will be able to use them for other projects after the end of this academic endeavor.

In other words, you have nothing to lose. All you have to do is to endure a bit of the initial annoyance that is required for a proper warm up of your programming environment. However, the reward is much greater than the initial investment.

In this article, I list only a very few resources and only the ones that are free. In the internet you can find many more.

Programming skills: no matter how much you know, there is always room for knowing more. Most libraries, both public and university, have free subscriptions to courses. For example, the library in my university gives free access to DataCamp to all the students that request it.

If you are not familiar with the platform, it contains a set of courses at all levels and many of them have code examples. As they cover a large range of cases, some of them will be relevant for your thesis. All you have to do is to dig into the list and familiarize with the code that is most promising for the work that you plan to do with your supervisor.

Get a student account in GitHub, it is a free repository that you can use for storing your code. Calming student status will grant you a pro version for free as well as many discounts for products of other companies. In addition, you will also not loose these perks after the graduation – some of them could be useful for you in the future.

Get an account in Kaggle, again, it is a free service that has courses and datasets. They also offer a web based programming environment with the idea that if you will find yourself in need of additional computation, you will use their services. As a general rule, I would recommend to have a local working environment instead of a cloud based like the one of Kaggle. However, a cloud based environment may be useful under certain conditions.

Set up Anaconda (or Eclipse) in your laptop. An Integrated Development Environment is extremely useful when you have to do some professional programming. Anaconda is free, open source and perfect for Python. Eclipse is its counterpart for Java. In the article below you can read about some of the benefits of an IDE. Once you will start using one you will never go back:

This article is a mini-tutorial on how to set up the environment for Anaconda:

Finally, in the article below, you can familiarize with the debug functionality. It is one of the most useful features of an IDE and can really save you a tremendous amount of time:

I would strongly encourage you in enduring the pain of getting familiar with the tools and the resources that I shared with you in this article. In the long run your future self will be extremely thankful.

A working environment is a very personal thing and I can imagine that every supervisor has their own. Consequently, I would encourage you to discuss the details during your first meeting. At the same time, I can imagine that these tools will be, in a way or an other, useful for your future work. In addition, the fact that you will have your working environment ready will allow you to advance conversations that are more specific to your thesis and you will result in a better use of the (limited) time that you will share with your supervisor.

#free #Resources that will #SHARP your #programming #environnement. #Java #pythonprogramming #pythoncode #DataScience #DataAnalytics #data
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By the way, here you can fine my profiles in GitHub Kaggle and DataCamp:

• https://github.com/Nym77
• https://kaggle.com/francescolelli
• https://www.datacamp.com/profile/flelli
• https://www.freecodecamp.org/francesco-lelli

Are you really committed in improving your programming skills?

Free Code Camp is an open source free course that will guide you in a project based journey towards advanced programming skills. It contains also a session on how to pass a programming oriented job interview.

Introduction to Computer Science from Harvard This is a free edX course from Harvard. You can also get a certificate but this will cost you something. Nevertheless the entire course is free.

Something else:

This article (Free Resources That Will Warm Up Your Programming Environment) is part of the miniseries on how to do a good thesis, you can see the full list of post at the following link:

This article is also part of a miniseries on how to become a better programmer. You can see the full list of posts at the following link:

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Debug refers to the process of identifying and removing errors from computer hardware or software.

Definition of Debug

This is not the only benefit of using an IDE for developing your code and this article presents the full list of feature. However for the purpose of this post we will try to understand how to do a debug in Anaconda. Practically speaking, your working environment (Spyder in this case) can keep track of the value of each single variable that you are using as well as execute the code that you wrote line by line. In this way you can jump into the portion of code that you need to investigate (debug) and see how all the critical aspects evolves. If it sound too theoretical and abstract to you I think that an example will be beneficial. In the video below you how debug in Anacoda works in practice:

This second video discuss a practical problem. In particular shows how to use the debugger for understanding why a portion of code is simultaneously execute the “then part” and the “else part” of an if-then-else. This is clearly impossible and therefore there is an error in the code.

Clearly you could be able to spot this problem without the need of a debugger. However, this tools, once you start getting familiar with its potential, makes your job easy. Let me add that once you will know how to use it you will never go back to an environment that do not offer you this functionality!

These videos cover the most useful functionalities and with them you will be able to cover 80-90% of your practical needs. For the full list of functionalities I would recommend you to visit the official documentation of the Spyder at the following link:

• Debug in Anaconda using the Spyder: full documentation

How to Debug in Anaconda and #Spyder : Save Time and Energy! #Python #programming
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In the case you do not know what is Anaconda, well is an IDE ( Here you can see what is an IDE and why you should use one) for Python specialized in big data oriented developments.

Installing the software is pretty much straight forward. Just

1: go to the download section of the official Anaconda website:

• https://www.anaconda.com/download/

2: select the proper distribution for your PC/Operative system

3: download and install it.

You will probably have different libraries that you would like to import into your working environment. In order to do that you will use a command line interface. Anaconda has a shell interface called “Anaconda Prompt” just locate the executable and run it.

Once the shell is open use the following commands for installing the packages

conda install <yourPackageName>

Where <yourPackageName> is the name of the library that you are planning to import in the program that you will write.

For example, in the case of Tensor Flow these are the commands:

conda install tensorflow
conda install keras
conda install theano

Now that the libraries are successfully installed you can use them directly in the code. The IDE is called Spyder. You will find the executable in the same folder of the Anaconda Prompt. Just run it!

If you are in need of an example of program you can refer to the second video of this introduction to object oriented programming.

If you successfully followed the instructions you now know how to install anaconda and import libraries. This should kick start your adventure into programming in Python like professionals do.

Additional resource in the web:

• Documentation from Anaconda and how to manage packages
• Install tensor flow in Anaconda

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This maybe be sufficient for deliver one or two assignments but is far from been what is used in “real life” for delivering successful projects. Imagine that the project that need to be delivered to your client is about 100.000 lines of code organized in 5.000 files and you are working at it with 5 colleagues that have all different experiences and background. Do you think that a text editor will be sufficient for handling this complexity?

The answer is: it is simply impossible. This is the reason why disciplines like software engineering exist and offer some of the most payed jobs to its practitioners. You probably start learning to program writing procedures, or sequence of actions that the computer should perform for you. However, “real life projects” use Object Oriented Programming (OOP) as basic framework for ensuring that the code can be read and maintained by large groups of developers.

You may be not interested in learning the intricate details of software development. However, some of the tools that a software engineer use may simplify your life and facilitate the development of the “few lines of code” that you have do deliver. This is the case of what is called Integrated Development Environment or IDE in short. You can consider this as a collection of tools that are used for facilitating the development of code.

Normally each language has his own IDE. For example, if you are a JAVA developer you will probably use Eclipse. If you are a Python developer interest in data science related project you will probably consider Anaconda.

Your university may not actively promote the use of an IDE for several reasons. Some of the includes:

• The automation fostered by this approach will bring you “far from the metal” and will not let you appreciate how things works.
• The learning curve is too slow. In other words, you will spend more time learning the details of an IDE instead of learning the topics of the course.
• Text based programming will help you developing a good set of habits.

However, independently of the reasons for not using an IDE my suggestion is to try to familiarize with one. If not for your classes, you may want to consider using it for your thesis. You can consider the initial time that you will spend learning the tool(s) as an investment that will pay off during your thesis.

If you like this idea you can find in these articles you can find a few tips and suggestions on how to set up your IDE for particular projects:

• IDE for Python in data science related projects
• IDE of JAVA for Distributed Applications and Service Oriented Architecture

In case you are not familiar with OOP, you may find this article useful for familiarize with the concepts:

• The basic of Object Oriented Programming

The use of a proper Integrated Development Environment ( #IDE ) will help you in becoming a #programmer that is familiar with Object Oriented #programming
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