The demand for data scientists, Machine learning and Full Stack Developer is continues to growth, and more and more software engineers are working with software companies are switching there

So what skills needed to switch the role to Data Scientists, Machine learning and Full Stack Development, we will discuss all in sequence.

As a Software Professional with a clear understanding of data science concepts and how data scientists work, you will be positioned to collaborate with data scientists while expanding your own expertise in this growing discipline you can be a Data Scientist.

Data Scientist:

If you want to become a data scientist yourself, this is the perfect time to do it. Online courses, such as those from Coursera, EdX, and Udacity, have good data science tracks. Some of them are quite advanced and taught by well-known data scientists.

You can also find boot camps that take people with a computer science, mathematics, or physics background and teach them how to solve a data-centric business problem from scratch. Kaggle offers data-science competitions sponsored by companies such as Facebook and Wix, which may recruit participants who score well.

Recruiting a data scientists, baseline is a computer science degree, or at least significant experience in software development, because data scientists are expected to write production-level code that is part of the product.

Here are some links which can help you in start learning Data Science,

1. 8 ways to improve your Data Science skills

2. Getting started in data science

3. Tools in the Bigdata ecosystem

4. Recent Research Topics in Data Science

Machine Learning:

Machine learning is a fascinating field, So being IT professional some of you wants to switch your job role to Machine Learning, But machine learning is quite a wide field. I would suggest get more specific to field of machine learning .

Here is a list of some fields,

• speech recognition / natural language processing

• image / video processing / computer vision

• medical systems

• fraud detection

• search engines

• Human-computer interfaces etc.

Many machine learning jobs will require a graduate degree (MS, or PhD) in Computer Science. The strong mathematical aspect to data and statistics also required.

Software engineers that want to make the transition can do so with some preparation. If you are well-versed in a particular language, consider taking the time to learn the frameworks and libraries that accompany that language. Many of these libraries are open source, making it even easier for you to access and learn. Once you get the basics
down, head over to GitHub to put your skills to the test.

Here are some links which can help you in getting this transition possible.
1. Resources to learn AI and Machine learning

2.Programming languages for Machine learning

Full Stack Development:

Full-Stack Web Development, according to the Stack Overflow Developer Survey in 2016 as well as 2017, is the most popular developer occupation today. It’s no wonder then that there are dozens of online and in-person programs that will help people become Full-Stack Developers and then even assist these new developers land high-paying programming jobs.

Some popular online programs can be found on Lynda, Udacity, Coursera, Thinkful, General Assembly, and so much more. Aside from these online programs, there are also in-person coding bootcamps that are teaching people the skills required to become Full Stack developers.

People working in IT industry are generally comfortable with coding, working with databases and using frameworks. After spending a few years as a developer, you would know at least some languages like Java,, Javascript, C++, C, HTML, Python, PHP, and you would have worked with several databases including SQL, MongoDb, Oracle
etc.All these skills help you to become Full Stack Developer.

For full stack development, you need to understand

• Hosting systems (the computer; the OS; and supporting services like DNS, SSH, email, and Apache)

• Application stack (web server like Apache or IIS; relational database like Oracle, MySQL, and PostgreSQL; and dynamic server-side web languages like Python, PHP, NodeJS, and Ruby)

• Web applications (model view controller framework like Agavi, Django, and Turbine; object relational modeling like Propel, SQL Alchemy, and Torque; and models, views,application logic, and front-end development including audio, video, HTML, CSS, and JavaScript).

All the Best !!!

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