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Evolution of API Economy

API (Application Programming Interface) economy is impelling companies to secure the digital services and assets. Having API that is useful in providing solutions to the consumers is DevOps core goal. API economy is growing for its management platforms acting as an API request and protects the back ends of services from being brought down from too many breaches or queries. API platforms have a wide range available for many tailored solutions that cater to the specific needs.

Read More: 

https://www.apacciooutlook.com/news/evolution-of-api-economy-nwid-6087.html 

I survived by telling myself ‘I’ll kill myself tomorrow but not today.’ I kept putting it off for days and days and days with the hope that the darkness will leave my body one of these days. and some days, it leaves and some days, it stays. I’m still surviving and that’s the sad truth and maybe I’ll kill myself tomorrow but not today.

Juansen Dizon, Magic Mantra (via juansendizon)

Big data analysis is the buzzword in the technology domain has gathered massive traction in recent time. As the customer touch points are increasing from digital communications like social media and emergence of new innovative technologies like IoT, companies now have data sources that give real-time information. 

It is forcing enterprises to rely on it for more profound insights which are helping organizations to grow. Here are five significant data analytics trends that will be the talk of the technology world in 2019 and beyond.

https://goo.gl/XEcJHK

10 Point Checklist on How to start an E-Commerce Business

Checklist for potential entrepreneurs who are planning to launch an e-commerce business: https://goo.gl/viM5gF

The cybercriminals have turned their gaze toward the digital assets of local governments. The increasing frequency of ransomware attacks has spurred them to adopt cyber insurance to mitigate the damage. However, the implementation of basic policies cannot counter the danger posed by cybercriminals. The insurance serves as a ransom rather than a robust countermeasure for cyberattacks, and hence result in encouraging the hackers rather than stopping them.

Technology helps the governments understand cities better, achieve outcomes, provide services more efficiently by assisting the citizen in embracing the future.

Know these to get started with Python.

Web Development has advanced over a period of time

Taking a Mobile First Strategy to Web-development

Changing trends in web design via web development

Java's Ongoing Popularity

The Era of Modern Web Development

Python as a first language: a roadmap to getting started.

I personally prefer Java as a first language. Put non-technically, it is a lot less convenient, so you get a more realistic idea of how computers work. Nonetheless, Python is an amazing language (with convenience as one of it’s values) so it makes sense as a gentle introduction. It’s also a popular language for data science and machine learning, so it’s great to have experience with.

The 3 ingredients you need to get started:

The Python Language Interpreter: when you write some code in a text file and save it as a .py file, the Python interpreter is what turns that code into commands that your computer can then actually preform. This is necessary.

An Integrated Development Environment (IDE): An IDE is like a helpful text editor for programming. Some basic features include auto-complete, typo and mistake catching, and automatic text coloring to make some parts of your code easier to find. This is optional but highly recommended.

Some learning resources: We’re going to need something about programming basics, problem solving in computer science, using an API, learning how to use google and stackexchange, data types, control structures, and then maybe an object-oriented programming intro, and eventually all the neat advanced features of the python language. Then we need to learn how to use Numpy (for scientific computing), Pandas (for easy data storage), and Tensorflow (machine learning!). Add some handy cheat-sheets for python, numpy, pandas, and Tensorflow, and we’re good to go.

Other posts will adress download, installation, and resources.

A curriculum:

Like I said up above, we need to know how to do the following. Save this and make it a checklist.

Learn to use google to answer questions about installing or using python, any packages, or computer science.

This also includes getting to know how to search Stackexchange, the website for coding questions n’ stuff.

How to install python 3 and get set up

How to install an IDE like Eclipse (with PyDev), IDLE, or Notebook++.

Programming basics: how does python work? What does the language look like? How does tabbing work?

Understand basic logic, including AND, OR, XOR, NOR, NAND, XNOR, Implies, and If…Else statements.

Variables: what are they, how do I set one and change it?

Basic math in Python.

Data types: what kinds of variables can I have? How does my computer store data? How do I use those types of data? What are the key commands and operations I know how to do?

Control structures: if, else, elif, for loops, while loops, break, continue

Methods! What are they, how do I make one, what can I do with it?

The open() command, the all() command, other neat built-in methods

<function name>= lambda <your variables>: <single line method>

Problem solving in computer science: now do fizbuzz.

What’s a package?

Importing packages, installing packages you don’t have with PIP

Using an API: how do I find one and how do I read it?

object-oriented programming in Python: what’s a class, how do I make one, how do I reference and instantiate one, methods, class vars, etc

Error handling: how to do exceptions

All the neat advanced features of the python language: iterators, generators, list comprehensions, enumerate, range, assert, with…as, etc.

Read through the Numpy API (for scientific computing), data types, matrices, stats, methods, etc. A short detour through scikit would be helpful.

Read through Matplotlib.pyplot API, plotting, plotting options, histograms, scatterplots, etc.

Pandas (for easy data storage), data frames, series, built-in operations on columns and rows, loading from a CSV, saving as a csv, apply, etc

Tensorflow (machine learning!) For basic stuff, shoot for knowing how to use the estimator package, which is discussed elsewhere on this blog. Also get to know the nitty gritty, including tensors, layers, tensorboard, etc.

Digital transformation is transforming industries beyond recognition. Not only does the digital revolution simplify tasks but also makes them more transparent. Retail banking is one such sector where digitalization has immense potential. Especially as the conventional systems are getting outdated and the older workforce aware of the hardcoded methods is retiring, banks are under high pressure to digitalize their systems.

Although the greatest advantage of the traditional banking system has been the trust factor, consumers are increasingly shifting towards new players such as Google, Amazon, and Apple because of the convenience in transactions offered by them. Customer preference is also putting the retail banking under pressure to digitalize.