Tips, Expertise, Articles and Advice from the Pro's for Your Website or Blog to Succeed
These days, I spend far more time writing and reviewing code in Python than I do in PHP. It's been a refreshing change of pace, and it's fun to learn different patterns in various programming languages.
If you haven't taken the time to look around to see what other languages or frameworks are doing, I'd highly recommend it.
Anaconda is an open source and free distribution of Python and R programming languages used for data science and machine learning related applications. Anaconda helps organizations to develop, manage, and automate AI/ML, regardless of the scale of deployment. It allows organizations to scale from individual data scientists to a collaborative group of data scientists, and from a single server to thousands of nodes for model training and deployment.
In this tutorial, we will be installing and setting up Anaconda Python on an Alibaba Cloud Elastic Compute Service (ECS) instance with Ubuntu 16.04 installed.
Data scientists are among the most sought-after technical talent today. According to Glassdoor, Data Scientist has been the top role in the US for four consecutive years and is increasingly identified as an essential component for business growth. As the demand for data science talent increases and the importance of the role is understood, the need to formalize the profession arises. What exactly is the role of a data scientist and why is professional certification for data scientists growing in importance?
You may also like: 10 Steps to Become a Data Scientist.
Data scientists work with enterprise leaders and key decision-makers to solve problems by preparing, analyzing, and understanding data to deliver insight, predict emerging trends, and provide recommendations to optimize results. The impact these professionals have varies by industry. For example, in healthcare, data scientists are using cognitive computing technologies to help support doctors to deliver personalized and precision medicine.
This week, we welcome Marlene Mhangami (@marlene_zw) as our PyDev of the Week! Marlene is the PyCon Africa (@pyconafrica) chair, the co-founder of @zimbopy, and a director for the Python Software Foundation. Let’s spend some time getting to know her!
You may also like: Confessions of a Woman in IT: The Highs, Lows, and Everything In-Between
Hello, there.
A lot of people want to start programming apps for Android, but they prefer not to use Android Studio and/or Java. Why? Because it's an overkill. "I just wanna create Snake and nothing more!"
It’s easy to learn to build scalable interactive web sites if you choose a simple learning path. I recommend you start with Python and Django instead of JavaScript and Mongo, Express, Angular (or React, Vue) and Node (MEAN) stack.
I've observed the success and failure to approaches that thousands of developers took to access a REST-like API in the RICOH THETA developer community. I saw problems students encountered in workshops and one-on-one training. Novice backend developers finish more projects when they use Python. To get people started quickly, I developed a free video course on YouTube, The Beginner Django Tutorial.
As a programmer, you want to explore new things. Learning a new language gives you a different perspective when solving problems.
So what are the five languages that a Java developer must explore?
This week, we welcome Veronica Hanus (@veronica_hanus) as our PyDev of the Week! Veronica is a regular tech speaker at Python and other tech conferences and meetups. You can see some of her talks and her schedule on her website. She has been active in the Python community for the past few years. Let's take a few moments to get to know her better!
Can you tell us a little about yourself (hobbies, education, etc):
Python is one of the easiest programming languages to learn. The syntax is close to English. Beginners generally encounter only a few surprises, such as forced indentation and the use of self in methods.
At some point, everyone starts reading, copying, and editing other people’s code. That’s where the confusion starts.
VS Code is an open-source, light-weight IDE that is gaining popularity due to its flexibility, ability to configure different programming languages, and cross-platform support. The following tutorial will walk you through how to set up a development and test environment in Python.
Interested in VS Code for Java development? Check out Visual Studio Code for Java: The Ultimate Guide 2019.
We will cover the following topics:
There is so much hype around the data scientist role these days that when a company needs a specialist to get some insights from data, their first inclination is to look for a data scientist. But is that really the best option? Let’s see how the roles of data scientists and data analysts differ and why you may want to hire an analyst before any other role.
You may also like: Five Must Read Books to Become a Successful Data Analyst.
So, what’s the difference between data scientists and data analysts? The definitions of these roles can vary, but it’s usually believed that a data scientist combines three key disciplines — data analysis, statistics, and Machine Learning. Machine learning involves the process of data analysis to learn and generate analytical models that can perform intelligent action on unseen data, with minimal human intervention. With such expectations, it’s clear that three-in-one is better than one-in-one, and data scientists become more desired by companies.
The good news is that Raspberry Pi 4 can run MiNiFi Java Agents, Intel Movidius Neural Compute Stick 2, and AI libraries. You can now use this 4GB of RAM device to run IoT with AI on the edge.
Hi, everyone. I recently got the chance to be interviewed on two podcast/youtube shows. They were a ton of fun and I think we covered some fun topics that were both technically deep and covered entrepreneurship from a tech and open source perspective.
In recent years, I have seen more people learn Python than any other language. Many of them are learning Python to explore Data Science and Machine learning libraries provided by Python. Some people are also learning Python for web development, and there are still many developers who are learning Python for scripting and automating trivial tasks. It doesn't matter why you are learning Python at this moment; it's a great thing in itself that you have decided to learn Python.
Even though I am a Java programmer, and I have spent all my career coding in Java, I value Python very for its versatility.
Considering learning a new Python framework for deep learning? If you already know some TensorFlow and are looking for something with a little more dynamism, you no longer have to switch all the way to PyTorch thanks to some substantial changes coming as part of TensorFlow 2.0. In fact, many of the changes in 2.0 specifically address the alleged shortcomings of TensorFlow.
With eager execution by default, you no longer have to pre-define a static graph, initialize sessions, or worry about tensors falling outside of the proper scope when you get over-zealous in your object-oriented principles. TensorFlow still has about 3 times the user base of PyTorch (judging from the repositories on GitHub referencing each framework), and that means more extensions, more tutorials, and more developers collaboratively exploring the space of all possible code errors on Stack Overflow. You’ll also find that despite the major changes starting with TensorFlow 2.0, the project developers have taken many steps to ensure that backwards compatibility can be maintained.
Let's say that we want to study the time between the end of a marked point and next serve in a tennis game. After gathering our data, the first thing that we can do is to draw a histogram of the variable that we are interested in:
import pandas as pd
import matplotlib.pyplot as plt
url = 'https://raw.githubusercontent.com/fivethirtyeight'
url += '/data/master/tennis-time/serve_times.csv'
event = pd.read_csv(url)
plt.hist(event.seconds_before_next_point, bins=10)
plt.xlabel('Seconds before next serve')
plt.show()
A Python thread is like a process, and may even be a process, depending on the Python thread system. In fact, Python threads are sometimes called lightweight processes because threads occupy much less memory and take less time to create than do processes.
Threads allow applications to perform multiple tasks at once. Multithreading is important in many applications.