Java

Data science tools are becoming increasingly popular as the demand for data scientists increases. However, with so many different tools, knowing which ones to learn can be challenging

In this blog post, we will discuss the top 7 data science tools that you must learn. These tools will help you analyze and understand data better, which is essential for any data scientist.

So, without further ado, let’s get started!

List of 7 data science tools 

There are many tools a data scientist must learn, but these are the top 7:

• Python

• R Programming

• SQL

• Java

• Apache Spark

• Tensorflow
• Git

And now, let me share about each of them in greater detail!

1. Python

Python is a popular programming language that is widely used in data science. It is easy to learn and has many libraries that can be used to analyze data, machine learning, and deep learning.

It has many features that make it attractive for data science: An intuitive syntax, rich libraries, and an active community.

Python is also one of the most popular languages on GitHub, a platform where developers share their code.

Therefore, if you want to learn data science, you must learn Python!

There are several ways you can learn Python:

• Take an online course: There are many online courses that you can take to learn Python. I recommend taking several introductory courses to familiarize yourself with the basic concepts.

PRO TIP: Join our 5-day instructor-led Python for Data Science training to enhance your deep learning skills.

• Read a book: You can also pick up a guidebook to learning data science. They’re usually highly condensed with all the information you need to get started with Python programming.
• Join a Boot Camp: Boot camps are intense, immersive programs that will teach you Python in a short amount of time.

Whichever way you learn Python, make sure you make an effort to master the language. It will be one of the essential tools for your data science career.

2. R Programming

R is another popular programming language that is highly used among statisticians and data scientists. They typically use R for statistical analysis, data visualization, and machine learning.

R has many features that make it attractive for data science:

• A wide range of packages
• An active community
• Great tools for data visualization (ggplot2)

These features make it perfect for scientific research!

In my experience with using R as a healthcare data analyst and data scientist, I enjoyed using packages like ggplot2 and tidyverse to work on healthcare and biological data too!

If you’re going to learn data science with a strong focus on statistics, then you need to learn R.

To learn R, consider working on a data mining project or taking a certificate in data analytics.

3. SQL

SQL (Structured Query Language) is a database query language used to store, manipulate, and retrieve data from data sources. It is an essential tool for data scientists because it allows them to work with databases.

SQL has many features that make it attractive for data science: it is easy to learn, can be used to query large databases, and is widely used in industry.

If you want to learn data science involving big data sets, then you need to learn SQL. SQL is also commonly used among data analysts if that’s a career you’re also considering exploring.

There are several ways you can learn SQL:

• Take an online course: There are plenty of SQL courses online. I’d pick one or two of them to start with
• Work on a simple SQL project
• Watch YouTube tutorials
• Do SQL coding questions

4. Java

Java is another programming language to learn as a data scientist. Java can be used for data processing, analysis, and NLP (Natural Language Processing).

Java has many features that make it attractive for data science: it is easy to learn, can be used to develop scalable applications, and has a wide range of frameworks commonly used in data science. Some popular frameworks include Hadoop and Kafka.

There are several ways you can learn Java:

• Work on a project
• Practice using programming exercises

5. Apache Spark

Apache Spark is a powerful big data processing tool that is used for data analysis, machine learning, and streaming. It is an open-source project that was originally developed at UC Berkeley’s AMPLab.

Apache Spark is known for its uses in large-scale data analytics, where data scientists can run machine learning on single-node clusters and machines.

Spark has many features made for data science:

• It can process large datasets quickly
• It supports multiple programming languages
• It has high scalability
• It has a wide range of libraries

If you want to learn big data science, then Apache Spark is a must-learn. Consider taking an online course or watching a webinar on big data to get started.

6. Tensorflow

TensorFlow is a powerful toolkit for machine learning developed by Google. It allows you to build and train complex models quickly.

Some ways TensorFlow is useful for data science:

• Provides a platform for data automation
• Model monitoring
• Model training

Many data scientists use TensorFlow with Python to develop machine learning models. TensorFlow helps them to build complex models quickly and easily.

If you’re interested to learn TensorFlow, do consider these ways:

• Read the official documentation
• Complete online courses
• Attend a TensorFlow meetup

However, to learn and practice your Tensorflow skills, you’ll need to pick up decent deep learning hardware to support the running of your algorithms.

7. Git

Git is a version control system used to track code changes. It is an essential tool for data scientists because it allows them to work on projects collaboratively and keep track of their work.

Git is useful in data science for:

• Tracking changes in code
• Allowing collaboration on coding projects
• Keeping track of work

If you’re planning to enter data science, Git is a must-know tool! Since you’ll be coding a lot in Python/R/Java, you’ll want to master Git to work with your team well in a collaborative coding environment.

Git is also an essential part of using GitHub, a code repository platform used by many data scientists.

To learn Git, I’d recommend just watching simple tutorials on YouTube.

Final thoughts

And these are the top seven data science tools that you must learn!

The most important thing is to get started and keep upskilling yourself! There is no one-size-fits-all solution in data science, so find the tools that work best for you and your team and start learning.

I hope this blog post has been helpful in your journey to becoming a data scientist. Happy learning!

Both Java and Python have their pros and cons when it comes to Android app development. Find out which one is the best for you.

Few things can be so divisive among developers as their choice of programming languages. Developers will promote one over the other, often touting their chosen language’s purity, speed, elegance, efficiency, power, portability, compatibility, or any number of other features.

Android app developers are no exception, with many developers divided between using Java or Python to develop their applications. Let’s look at these two languages and see which is best for Android app developers.

Java

Originally released in 1995, Java is one of the cornerstone languages of modern programming and continues to be one of the most popular programming languages in the world. Java was designed on the “write once, run anywhere” principle, as compiled Java apps are designed to run on a Java virtual machine (JVM). Any computer, device, or platform with a JVM installed should be able to run a Java app without it being altered or recompiled. In addition, Java is a true object-oriented programming language, with many modern features.

Because of these features, Google used Java as the core basis of Android when it began development. As a result, to this day, Java remains the primary way to create true “native” Android apps. Apps written in Java tend to have the fastest performance, tightest integration, and easier access to underlying features and APIs.

Despite these advantages, Java is not an easy language for many developers to pick up, especially those coming from a career in web development.

Python

First released in 1991, Python predates Java by a few years, yet continues to be a force to be reckoned with in the development world. Unlike Java—or other languages such as C, Objective-C, or Swift—Python is an interpreted language, rather than a compiled one. In other words, rather than compiling the completed code into machine-language instructions, Python code is executed by a Python interpreter on the fly.

Python has long had a reputation for being a simple, elegant language. Whereas other languages emphasize many ways to accomplish a goal, Python’s philosophy is that there should be a single, superior way to do it. This, in turn, gives Python a much easier learning curve for new developers.

Because of its interpreted nature, Android does not natively support Python apps. However, there are several frameworks available that allow Python apps to be interpreted and run on Android, even giving them a native look and feel. Despite this, because it is not the native development environment, Python apps do not always have the same level of system access as their Java counterparts. In addition, as a general rule, Python apps tend to have slower performance, although this is increasingly mitigated by faster hardware.

Despite these disadvantages, Python appeals to many developers who are already proficient with it or are coming from a web development background. Because Python is an interpreted language, this gives Python apps an even greater degree of portability than Java, especially since some platforms—such as macOS—no longer install a JVM by default.

Java or Python – which language to choose?

The fact of the matter is, that both Java and Python have pros and cons. Java is the native language of Android and enjoys the associated benefits. Python is an easier language to learn and work with, and is more portable, but gives up some performance compared to Java.

At the end of the day, each tool has its place depending on what you are trying to accomplish and what your background is as an Android app developer. If you’re not sure about how to create an Android app that will meet your expectations, you may need to seek out expert advice from people who’ve worked on similar projects before.

Want to learn more about Python? Take a look at these Python tutorials.

GitHub repositories are great for finding new solutions and keeping up with open source projects. Take a look at some of the most popular repositories.

If you didn’t watch the video because you’re in a public setting and don’t have headphones, the video essentially says GitHub is an open source software development community for teams of and individual developers to work on projects. GitHub gives you a platform to copy projects, track changes, and so much more.

What brings me to share the trending GitHub repositories for this month,  and the months to come, are the ideas a community of people has. Like the introduction video says, an idea/implementation for one product can bring up new solutions for another. We should always be aware of products and solutions in the community because they might help us in the future.

Trending GitHub repositories

With that, here are the top trending GitHub repositories users are most excited about for October.

1) iptv-org/ iptv

This project allows you to watch IPTV (Internal Protocol Television) channels from around the world. There are currently a collection of 8,000+ channels available to the public.

2) bloc97/ Anime4K

Anime4K is used as a “real-time upscaling for anime video”. It’s completely open-sourced, and far from perfect. Currently, it claims to be the “fastest at achieving reasonable quality” for your anime 4K upscaling. The project is looking at taking a hybrid approach with machine learning to improve results.

3) axi0mX/ ipwndfu

IPWNDFU is a jail-breaking tool for many iOS devices. “Jail-breaking” is the term used when someone wants to free their device (typically mobile) of restrictions given by the operator or manufacturer. It allows the user to download software previously unauthorized. This project is BETA software so you will need to be careful if you use it.

4) vlang/ v

Version 1.0 of the V Programming Language hasn’t been released yet, but I imagine it will be a popular repository to follow for the next few months. Expected in December 2019, V is a simple language for developing maintainable software. The language can reportedly compile itself in less than 1 second with zero dependencies.

5) geek computer/ Python

The Python project is a list of Python examples created by the project managers to share their experience with the programming language. If you want to advance your skills with Python, take a look at this repository.

6) TheAlgorithms/ Java

TheAlgorithms makes open source resources for learning data structures and algorithms with their implementation in any programming language. Java is their project for implementing all algorithms in Java.

Check out the post I co-authored covering 101 machine learning algorithms.

7) home-assistant/ home-assistant

“Home Assistant is a home automation platform running on Python 3. It can track and control all devices at home and offer a platform for automating control.”

8) robbyrussell/ oh-my-zsh

Zsh is a powerful scripting tool to help make you feel like a 10x developer. In this community of 1300+ contributors, you can have a framework for managing your zsh configuration.

9) mitesh77/ Best-Flutter-UI-Templates

Flutter Dart is an open-source software used to develop applications. In this instance, it’s being used to make available templates to build your app.

10) gatsbyjs/ gatsby

Like Flutter, Gatsby is used to helping developers “build blazing fast websites and apps”. Gatsby allows you to create dynamic pages, pull data from just about any data source, and automates many activities such as lazy-loading, image optimization, and inlining critical styles.

Over the years, the popularity of different programming languages has been increasing. This blog lists down some of the top & most useful programming languages for you to learn.

The use of programming languages ​​remains a popular way of earning money and the main tool for creating modern technologies. Even if you start studying some of these programming languages right now, you can be sure of high wages and rapid career growth.

The more programming languages ​​you know and use, the higher your status will be. For the development of one application/technology, several of them can be used at once. Since the inception of the first computers, more than 8,000 programming languages have been invented. There are basic ones that are used everywhere. It is impossible to single out the best one, each has advantages and disadvantages.

Below is the list to get you acquainted with the best programming languages ​​and find out which one to start with:

Python 

Python is a simple programming language that is suitable for beginners and will be a relatively easy way to get into a new profession. A clear code, a large library of tools, and a minimum of tricks allow you to quickly get the hang of it, making the language the most popular in education and also helping you to learn data science. Although learning Python for Data Science is not an easy task, there is a lot of training out there that can help one get started.  It is not for nothing called “language with batteries included”, it itself provides methods for solving basic problems. It is easy to integrate with C and C ++ languages.

Python’s performance is inferior to other languages, but because of this, it does not lose its relevance. Scientists all over the world use it for machine learning. Plus, it’s ideal for web services; backend, and sysadmin.

C & C++

The C language appeared in 1975, and its more multifaceted extension C ++, in 1985. They are the progenitors of most programming languages. Every 3 years the C ++ language is updated, and today there is already the 20th ISO standard. Initially, the C language was developed for less powerful computers, was economical, and more tied to the hardware. This binding remains today, which allows you to “squeeze” the maximum out of productivity. Now the language is used both for game development and for machines with a low-power processor.

These complex languages ​​are not the most fun places to learn programming. When studying, you can quickly burn out and say goodbye to the profession. However, it is C ++ that will help to fully probe the “brain” of the computer, which is extremely important for the programmer. This hard start is suitable for those who want to understand the basics. C ++ does not support validation at the time of writing the code, which also complicates the development work.

That is why these specialists are in great demand.

.NET

.NET is a framework from Microsoft that allows you to use the same namespaces, libraries, and APIs for different languages. .NET supports several languages: along with C#, it also includes VB.NET, C ++, F #, as well as various dialects of other languages ​​tied to .NET.

.NET is fairly widespread in the development of in-house software products, but it is still relatively rare in web development, like other software products from Microsoft. Therefore, finding .NET developers for a web project can be quite difficult. The use of .NET usually  “pulls” the purchase of other software from Microsoft. However, if you are looking for a promising direction, this is a great option.

JavaScript

Created in 1995, JavaScript is the dominant frontend language around the world. Its relevance is not lost even for a minute, it can be used to create interactive websites. It is also easy to learn, but today it is not enough for development, and the number and quality of frameworks can become difficult. That is why you should not start with it, because constant retraining for the changing frontend, by virtue of already active programmers.

Thanks to a large number of add-ons, the functionality of JavaScript is limitless. The main disadvantage is that due to the fact that the language is used to encode pop-ups, we often have to deal with malicious content.

Java

Java was also introduced in 1995 but has nothing to do with JavaScript. It is in demand in the backend and occupies a good position in it. Death is predicted for the language every year, but it seems that this will not happen soon. Behind the “boring and verbose” structure, many find the perfect solution to many problems. For example, it is used by banking structures to write mobile applications for large companies.

The main advantage is that the developed application will run on any platform that supports Java due to the weak link system. That is, after the initial creation, there is no need to modify the application specifically for each server.

The disadvantages of the language include additional payment for the licensed version of the Java Development Kit, and it is not suitable for applications in the cloud.

Learning Java first is not worth it, it is the perfect complement to other more fundamental languages.

Swift

Created by Apple in 2014, Swift has grown exponentially in popularity. The creators positioned it as a replacement for Objective-C and the beginning of a “new era” of programming. But so far it is in demand for only iOS applications.

The language is perfectly adapted for both custom and server-side development. The syntax is easy to read and the code runs quickly.

It’s only worth learning if you’re going to develop apps for Apple products.

But even for iOS, it does not always work. Since the language is new, it is used to write applications for at least the seventh generation of iOS. In addition, Swift still has many shortcomings, it is unstable and has a small number of third-party resources to work with.

Conclusion

A number of programmers build their careers with professional knowledge in only one programming language, but they are more proficient in several of them at once, which significantly increases their chances of succeeding in their careers. It is difficult to say which of them should be studied. For example, if you want to work in a large company, it is better to learn C and Java, Python and JavaScript are suitable for participation in web startups, and for iOS mobile applications, it is enough to have knowledge of Swift.