Arvel  Parker

Arvel Parker

1592985840

Building Analysis Pipelines with Kaggle

Kaggle is one of the most popular places to get started with data science and machine learning. Most in the data science world have used or at least heard of it. Kaggle is well-known as a site that hosts machine learning competitions and while that is a big part of the platform, it can do much more.

This year with the COVID-19 Open Research Dataset (CORD-19), I had the chance to use the platform more consistently. Honestly, Jupyter notebooks and GUI-based development hasn’t been my preferred approach (Vim is often good enough for me). But over the last few months, I’ve been impressed with the capabilities of the platform. This article gives an overview of Kaggle Notebooks, the Kaggle API and demonstrates a way to build automated analysis pipelines.


Notebooks

Kaggle Notebooks is a cloud-hosted Jupyter notebook environment. Notebooks can be built in Python or R. Notebooks execute within Docker containers and we can think of them as a bundle of logic. Notebooks can contain all the logic for a data analysis project or they can be chained together to build modular components. Notebooks can be publicly shared or kept private.

Notebooks have access to multiple CPU cores and a healthy amount of RAM. Additionally, GPUs and TPUs can be added, which can accelerate the training of deep learning models. The resources available are extremely impressive for a free service. Spinning up a comparable host on one of the big cloud providers is a sizeable cost.

Notebooks read data using a couple different methods. The main way is through datasets. Anyone with an account can upload data and create their own datasets. There also are a large number of publicly available datasets already on Kaggle. As with notebooks, datasets can be publicly shared or private. Notebooks can have one to many datasets as inputs. Additionally, the output of other notebooks can be used as input, allowing a chain of notebooks to be constructed.

#python #kaggle #machine-learning #data-science #software-development

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Building Analysis Pipelines with Kaggle
Ian  Robinson

Ian Robinson

1623856080

Streamline Your Data Analysis With Automated Business Analysis

Have you ever visited a restaurant or movie theatre, only to be asked to participate in a survey? What about providing your email address in exchange for coupons? Do you ever wonder why you get ads for something you just searched for online? It all comes down to data collection and analysis. Indeed, everywhere you look today, there’s some form of data to be collected and analyzed. As you navigate running your business, you’ll need to create a data analytics plan for yourself. Data helps you solve problems , find new customers, and re-assess your marketing strategies. Automated business analysis tools provide key insights into your data. Below are a few of the many valuable benefits of using such a system for your organization’s data analysis needs.

Workflow integration and AI capability

Pinpoint unexpected data changes

Understand customer behavior

Enhance marketing and ROI

#big data #latest news #data analysis #streamline your data analysis #automated business analysis #streamline your data analysis with automated business analysis

Tyrique  Littel

Tyrique Littel

1604008800

Static Code Analysis: What It Is? How to Use It?

Static code analysis refers to the technique of approximating the runtime behavior of a program. In other words, it is the process of predicting the output of a program without actually executing it.

Lately, however, the term “Static Code Analysis” is more commonly used to refer to one of the applications of this technique rather than the technique itself — program comprehension — understanding the program and detecting issues in it (anything from syntax errors to type mismatches, performance hogs likely bugs, security loopholes, etc.). This is the usage we’d be referring to throughout this post.

“The refinement of techniques for the prompt discovery of error serves as well as any other as a hallmark of what we mean by science.”

  • J. Robert Oppenheimer

Outline

We cover a lot of ground in this post. The aim is to build an understanding of static code analysis and to equip you with the basic theory, and the right tools so that you can write analyzers on your own.

We start our journey with laying down the essential parts of the pipeline which a compiler follows to understand what a piece of code does. We learn where to tap points in this pipeline to plug in our analyzers and extract meaningful information. In the latter half, we get our feet wet, and write four such static analyzers, completely from scratch, in Python.

Note that although the ideas here are discussed in light of Python, static code analyzers across all programming languages are carved out along similar lines. We chose Python because of the availability of an easy to use ast module, and wide adoption of the language itself.

How does it all work?

Before a computer can finally “understand” and execute a piece of code, it goes through a series of complicated transformations:

static analysis workflow

As you can see in the diagram (go ahead, zoom it!), the static analyzers feed on the output of these stages. To be able to better understand the static analysis techniques, let’s look at each of these steps in some more detail:

Scanning

The first thing that a compiler does when trying to understand a piece of code is to break it down into smaller chunks, also known as tokens. Tokens are akin to what words are in a language.

A token might consist of either a single character, like (, or literals (like integers, strings, e.g., 7Bob, etc.), or reserved keywords of that language (e.g, def in Python). Characters which do not contribute towards the semantics of a program, like trailing whitespace, comments, etc. are often discarded by the scanner.

Python provides the tokenize module in its standard library to let you play around with tokens:

Python

1

import io

2

import tokenize

3

4

code = b"color = input('Enter your favourite color: ')"

5

6

for token in tokenize.tokenize(io.BytesIO(code).readline):

7

    print(token)

Python

1

TokenInfo(type=62 (ENCODING),  string='utf-8')

2

TokenInfo(type=1  (NAME),      string='color')

3

TokenInfo(type=54 (OP),        string='=')

4

TokenInfo(type=1  (NAME),      string='input')

5

TokenInfo(type=54 (OP),        string='(')

6

TokenInfo(type=3  (STRING),    string="'Enter your favourite color: '")

7

TokenInfo(type=54 (OP),        string=')')

8

TokenInfo(type=4  (NEWLINE),   string='')

9

TokenInfo(type=0  (ENDMARKER), string='')

(Note that for the sake of readability, I’ve omitted a few columns from the result above — metadata like starting index, ending index, a copy of the line on which a token occurs, etc.)

#code quality #code review #static analysis #static code analysis #code analysis #static analysis tools #code review tips #static code analyzer #static code analysis tool #static analyzer

Anthony  Dach

Anthony Dach

1623649980

Building An Automated Testing Pipeline with GoCD [Tutorial]

CI/CD enables developers, engineers and DevOps team to create a fast and effective process of packaging the product to market, thereby allowing them to stay ahead of the competition. When Selenium automation testing joins force with an effective CI/CD tool, it does wonders for the product delivery. GoCD is one such open-source Continuous Integration (CI) and Continuous Delivery (CD) tool developed by ThoughtWorks that supports the software development life cycle by enabling automation for the entire process. Right from development –. test –> deployment, GoCD ensures that your delivery cycles are on time, reliable, and efficient.

Ok. I know what you are thinking!

We have Jenkins for CI & CD ! Why a new tool ?

In this GoCD pipeline tutorial, we will deep dive into all the information you would need to set up a GoCD pipeline and in conjunction with the underlying concepts. You will also learn how to perform automation testing using Selenium in GoCD Pipeline through an Online Selenium Grid .

Why GoCD ?

Setting up GoCD

Setting up GoCD

Setting Up the GoCD Pipeline

#selenium testing #ci/cd #building an automated testing pipeline with gocd #gocd pipeline #build-test #gocd

The Best Way to Build a Chatbot in 2021

A useful tool several businesses implement for answering questions that potential customers may have is a chatbot. Many programming languages give web designers several ways on how to make a chatbot for their websites. They are capable of answering basic questions for visitors and offer innovation for businesses.

With the help of programming languages, it is possible to create a chatbot from the ground up to satisfy someone’s needs.

Plan Out the Chatbot’s Purpose

Before building a chatbot, it is ideal for web designers to determine how it will function on a website. Several chatbot duties center around fulfilling customers’ needs and questions or compiling and optimizing data via transactions.

Some benefits of implementing chatbots include:

  • Generating leads for marketing products and services
  • Improve work capacity when employees cannot answer questions or during non-business hours
  • Reducing errors while providing accurate information to customers or visitors
  • Meeting customer demands through instant communication
  • Alerting customers about their online transactions

Some programmers may choose to design a chatbox to function through predefined answers based on the questions customers may input or function by adapting and learning via human input.

#chatbots #latest news #the best way to build a chatbot in 2021 #build #build a chatbot #best way to build a chatbot

Arvel  Parker

Arvel Parker

1592985840

Building Analysis Pipelines with Kaggle

Kaggle is one of the most popular places to get started with data science and machine learning. Most in the data science world have used or at least heard of it. Kaggle is well-known as a site that hosts machine learning competitions and while that is a big part of the platform, it can do much more.

This year with the COVID-19 Open Research Dataset (CORD-19), I had the chance to use the platform more consistently. Honestly, Jupyter notebooks and GUI-based development hasn’t been my preferred approach (Vim is often good enough for me). But over the last few months, I’ve been impressed with the capabilities of the platform. This article gives an overview of Kaggle Notebooks, the Kaggle API and demonstrates a way to build automated analysis pipelines.


Notebooks

Kaggle Notebooks is a cloud-hosted Jupyter notebook environment. Notebooks can be built in Python or R. Notebooks execute within Docker containers and we can think of them as a bundle of logic. Notebooks can contain all the logic for a data analysis project or they can be chained together to build modular components. Notebooks can be publicly shared or kept private.

Notebooks have access to multiple CPU cores and a healthy amount of RAM. Additionally, GPUs and TPUs can be added, which can accelerate the training of deep learning models. The resources available are extremely impressive for a free service. Spinning up a comparable host on one of the big cloud providers is a sizeable cost.

Notebooks read data using a couple different methods. The main way is through datasets. Anyone with an account can upload data and create their own datasets. There also are a large number of publicly available datasets already on Kaggle. As with notebooks, datasets can be publicly shared or private. Notebooks can have one to many datasets as inputs. Additionally, the output of other notebooks can be used as input, allowing a chain of notebooks to be constructed.

#python #kaggle #machine-learning #data-science #software-development