The benefit of getting familiar with Data Science Concepts

There are several benefits of getting familiar Data Science concepts. Here, I will list the most obvious:

• You will have another methodology in your portfolio of tools for conducting research.
• The certifications that you will obtain will be part of your CV and will be able to differentiate your profile.
• If you plan a a programming intensive and prototype driven approach for conducting your thesis (or research in general) you will be able to focus on the most interesting aspect from day one. This will prevent the unfortunate situation where you will be spending your time in catching up with your programming skills instead of really conducting research.

Courses Related to Data Science In DataCamp

This is a Python 101 course to perform Data Science:

• https://app.datacamp.com/learn/courses/introduction-to-data-science-in-python

This course is a bit more advanced compared to the previous one and focuses on Python specifically for data science:

• https://app.datacamp.com/learn/courses/intro-to-python-for-data-science

Here you learn to write your own custom functions, complete with multiple parameters and multiple return values, along with default arguments and variable-length arguments:

• https://app.datacamp.com/learn/courses/python-data-science-toolbox-part-1

This one complements the previous one and lets you practice doing a case study:

• https://app.datacamp.com/learn/courses/python-data-science-toolbox-part-2

With this you will learn how to improve the efficiency and readability of your code:

• https://app.datacamp.com/learn/courses/writing-efficient-python-code

And this one provides you with techniques for data cleaning and data (pre)elaboration:

• https://app.datacamp.com/learn/courses/cleaning-data-in-python

Finally, the following 2 courses will let you familiarize yourself with Tensorflow

• https://www.datacamp.com/tutorial/tensorflow-tutorial
• https://app.datacamp.com/learn/courses/introduction-to-tensorflow-in-python

If you like the idea of learning Data Science, consider sharing and spreading the voice:

A collection of #courses in DataCamp for getting familiar with #DataScience concepts using #Python . They are free if you are a #student. #DataScientist #pythonprogramming
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Bonus Course (in OOP):

This course is not strictly related to Data Science skills. However, the more the complexity of your code will grow the more you will need to organize it properly. If you are not familiar with OOP at all, this post is a good introduction to Object Oriented Programming.

• https://app.datacamp.com/learn/courses/object-oriented-programming-in-python

The post Using DataCamp for getting familiar with Data Science concepts appeared first on Francesco Lelli.

• 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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Let’s start from the beginning: We can define computer programming as the process of developing and implementing various sets of instructions to enable a computer to do a certain task.

Programming is a language and is subject, like any other traditional language, to changes over time. Moreover, there is not a single language but several. Each one of them is specialized for a particular set of tasks.

In addition, as programmers, the more we learn about how to program, the more we evolve the languages of programming.

This video gives you an overview on how the languages we use for programming evolved during the years. In addition, you can also see an estimation of the adoption of the language itself.

As you can see, there are several languages and some of them, like Latin, are considered dead.

Programming is a combination of languages, a set of data structures, and algorithms. The video below give you an overview of how they evolved over time:

If we try to generalize and categorize the art of programming, we can group the style in one of the 3 following categories that are intrinsically different:

• Functional programming
• Structured/Procedural programming
• Object oriented programming

In case you are wondering what they mean, we can borrow some definitions from Wikipedia:

“ Functional programming (FP) is a programming paradigm — a style of building the structure and elements of computer programs — that treats computation as the evaluation of mathematical functions and avoids changing-state and mutable data.” — Wikipedia

“ Object-oriented programming (OOP) is a programming paradigm based on the concept of “objects”, which may contain data, in the form of fields, often known as attributes; and code, in the form of procedures, often known as methods.” — Wikipedia

“ Procedural programming is a programming paradigm, derived from structured programming, based upon the concept of the procedure call. Procedures, also known as routines, subroutines, or functions, simply contain a series of computational steps to be carried out.” — Wikipedia

If we fast forward to the present we can see that Java+JavaScript+Python are the languages that is used by the majority of the programmers. Are they procedural programming? Are they OOP? Or maybe they are Functional?

Well… let’s try to answer: Java is the flagship of Object Oriented Programming. JavaScript is a simplified version of Java that has the majority of the properties of OOP. This last claim is actually a simplification of a very long discussion that I will eventually summarize in a different article. However, we can claim that is “debatably true”. As for Python, it is a native Object Oriented Programming language; however, it is fairly normal to start using it as a scripting/procedural language, because is easy to approach programming in this way.

The landscape is far from been binary and this video summarize the most relevant languages at the time of writing in 2022:

In case you are curios of knowing more about programming languages and how they evolved you may watch the following video for further details:

I hope that you now have an idea of the past and the present. In this way you will be able to get an idea of the future of programming languages.

Past, Present and future of #programming. Understanding the past could help you in building a better #Software . #softwaredevelopment #SoftwareEngineer #OOP
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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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