It is advanced and easy to learn
Python’s position in the tech scenario is growing stronger every day, and it has been around for a while now. Python has evolved over the years, and it is constantly supported by a passionate and growing community of developers. It is at an advanced stage which guarantees reliability and stability. It has come a long way and has a long way to go, and one can rest assured that one’s project won’t be based on a language that is on its way to becoming obsolete. Python is equipped with a gentle learning curve making it possible for developers to master it within a short span of time.
Python is minimal and simple
At the core of Python’s philosophy are two major things – it is minimal and hence simple. These core aspects of Python are derived from many different features like for example, white spaces in Python, signify code blocks. Developers need not worry about adding keywords or curly brackets. Python can be used to code a blockchain without having the need to write a lot of code.
Python is popular
Over the past few years, Python is becoming increasingly popular which makes it an excellent choice for a Blockchain-based project. According to this year’s TIOBE index, Python is ranked third amongst all programming languages, and according to the index, its popularity is only growing steadily. In practical terms, this means that one will have a comparatively easier time building one’s project as there are many developers who specialize in Python including professionals with a scientific or academic background. Pythons’ popularity also means that a team has access to its ever-growing community which shares useful knowledge and builds libraries. Most online courses in blockchain offer a Fintech course which strongly suggests the use of Python over other programming languages.
Python can be run compiled or uncompiled
Unlike other programming languages like C, Python is a scripted language that requires no compilation to be readable by machines, which makes it easier for developers. For example, if someone runs an application and notices a bug – if one is using a compiled language to fix it, one has to stop the application, return to the source code, fix the bug, recompile the code and restart the application. In Python, all one has to do is fix the bug and then reload the application. It is that simple. One does not have to recompile the code, making massive headway in building blockchains.
Python offers free packages for Blockchain
A big plus of using Python in a blockchain project is that it gives developers a collection of free packages to assist them to write code more efficiently. Here is a page with a complete list of these libraries.
Python for Blockchain
Blockchain has some very specific requirements when it comes to code and language. When one chooses a programming language for a Blockchain project, one has to be very sure that the language is secure, well functioning and scalable. An advanced, reliable language is a must to make the blockchains as safe as possible, and Python can be of great help.
Blockchain implementation
With Python, a simple Blockchain can be created in less than 50 lines of code. First, one needs to define what the block will look like. Each block in the blockchain is stored with an index and a timestamp. The key is blockchain integrity, so it should be ensured with a cryptographic hash of the index of the block, timestamp, data and a genesis block at the start.
Python is recommended for blockchain if one is trying to address a case of Internet of Things. One can easily perform many tasks with a single command with Python which makes the work of building blocks with the necessary information and linking them a much easier one to do.
Day: May 2, 2019
How do you build a career in Machine Learning after completing the ML Foundation Course?
ML/Machine Learning has a promising future. Chatbots, smartphones and most AI platforms essentially use ML. For example, Alexa from Amazon, Google, Facebook, and almost all large platforms point to a growing industry and an all-time high ML jobs demand. Very obviously the need for professionals in ML, AI, and Deep Learning outstrips the demand.
Programmers, graduates in Computer Applications, and even graduates in mathematics, Social Science or Economics can learn and become ML professionals by doing a certified foundation course in Data Analytics/ Data Science course.
The ML professionals essential skill set include
· Computer programming and CS Fundamentals.
· Programming languages like R, Python and some more.
· ML libraries and algorithms.
· Statistics and Probability.
· Software design and systems engineering.
Simple ways to get started with Machine Learning:
A. Read ML books and do a machine learning course with a reputed company like Imarticus which can provide you with reinforcement and certification of your practical skills. Data is the beginning and all about applying your machine learning training, programming knowledge, computer science techniques and statistics to data. R and Python are the most commonly preferred languages. While Python scores in leveraging libraries that are analytics-friendly, practical algorithms, the application development and end-to-end integration using sci-learn and Tensorflow APIs, R is preferred for advanced capabilities in data and statistical inferences analysis.
B. Hone your ML skills with ML Courses which provide ML fundamentals and basic algorithms, statistical pattern recognition and data mining. Your knowledge of statistics should include Bayesian probability, inferential and descriptive statistics for which you will find free courses by Udacity.
C. Applying your learning to building algorithms like perception and control for robotics, building smart robots, anti-spam, and web-search text understanding, medical informatics, computer vision, database mining, and audio based applications.
D. Attend hackathons (Kaggle, TechGig, Hackerearth, etc) which give you support, exposure and mentorship in ML practical ideas.
E. Build your portfolio with
- A project where you collect the data yourself
- A project where you deal with data cleaning, missing data, etc
F. Master areas that you like to work in like Neural Networks, AI, and ML as applied to image segmentation, speech recognition, object recognition and VR.
The Job Scope:
ML can be the most satisfying choice of careers today which include algorithm development and research used for adaptive systems, building predictive methods for product demand and suggestions, and exploring extractable patterns in Big Data. Companies recruit for positions like
- ML Analyst
- ML Engineer
- Data Scientist NLP
- Lead- Data Sciences
- ML Scientist.
Expected payouts:
According to a Gartner report, 2.3 million ML jobs in AI are expected by 2020. Entering the ML field now, according to Digital Vidya, is a great option because the ML payouts for the new entrants vary from Rs 699,807- 891,326. With good expertise in algorithms and data analysis the range of reported salaries could be from Rs 9 lakh to Rs 1.8 crore pa.
How much time does it take to become a Full Stack Developer?
How much time does it take to become a Full Stack Developer?
All of us have this doubt about every learning journey we are about to take. Learning, especially something complex as web development is a major investment of energy, time and opportunity cost. So, it is natural to be curious about how long it will take to reach your goals. However, despite this being a common query, it is not easy to put a solid number on how long it will take you to become a full-stack developer. This article attempts to give you an idea about the full stack learning procedure and how much time it could take.
What is actually full stack?
We know that traditionally, the developers were categorized into two.
- Front-end Developers – Who handle the user interface and user experience architecture of an application.
- Back-end Developers – Who designs and handles the interactions between the server and the database.
Later, with the evolution in customer requirements and associated technologies, a new breed of developers was born. They are full stack developers. Unlike their predecessors, full stack developers are not specialists of a single end. They can handle both back-end and front-end operations. Obviously, to handle all this work, you should have proficiency in a couple of technologies as a full stack developer.
To be a Full Stack Developer
A full stack developer can be called as a jack of everything but master of none. You have to cover a relatively long list of skills to become one finally. In short, you should be able to:
- Carry out core programming
- Build the front-end and handle user interactions with the application
- Design the business logic and application’s interactions with the database.
- Handle the data of your application.
It should also be noted that if you are trying to learn enough full stack development for a rudimentary application, you might succeed in one or two months. You can join a full stack developer course and readily learn it. However, it takes more work to become a professional full stack developer. The individual tools, frameworks, and libraries will keep changing all the time, but the fundamentals never change, and your learning should be concentrated towards those basics.
Mastering the Fundamentals
Mastering the fundamentals of full stack is a long process and takes time. Often you will find it difficult to focus on the process and jump ahead when you think they know something. It is quite natural and should be taken care of. In the very beginning, while you are exploring each skill, you find your confidence rising. But, that is only the first stage of the process. Soon after that, you will realize the difficulties associated with it your doubts will start to increase. In this period the progress will be very slow and hard to measure. In the next stage, you will really start to learn things and make progress in the process.
What I am implying here is that you can’t actually predict with certainty how long it will take you to become a full stack developer. This gets harder if you want to become really good at it. A good full stack developer tutorial can take you really ahead in the process, but being a professional takes practical experience. So, a good suggestion is to worry less about the time and focus on the process. You will most probably end up exceeding your original goals in a shorter period.