MTA Turnstile Traffic Analysis to Optimize Street Engagements

In the Project 1 of Metis Data Science Bootcamp (Singapore Batch 5), we are tasked on exploratory data analysis (EDA) of MTA turnstile data to advise a fictitious non-profit organization, WomenTechWomenYes (WTWY) on the optimal placement of street teams (at entrances to NYC subway stations) for social engagements. WTWY wants to invite interested individuals to its annual gala to raise awareness and increase participation for women in tech, and the street teams’ agenda is to collect as many emails as possible and give out free tickets to the gala. In my analysis, I have made the following assumptions:

Assumptions

  • WTWY is constrained by time and manpower resources, hence insights from my analysis should identify top stations by traffic, as well as the peak periods in those stations.
  • Individuals who are interested in tech have a higher probability to be encountered in city center with a denser cluster of tech corporate offices.
  • The WTWY gala is imminent, and a week of MTA turnstile data is analyzed as an sample for the weeks leading to the gala.

#data-visualization #new-york-city #data-science #metis

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MTA Turnstile Traffic Analysis to Optimize Street Engagements
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

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

MTA Turnstile Traffic Analysis to Optimize Street Engagements

In the Project 1 of Metis Data Science Bootcamp (Singapore Batch 5), we are tasked on exploratory data analysis (EDA) of MTA turnstile data to advise a fictitious non-profit organization, WomenTechWomenYes (WTWY) on the optimal placement of street teams (at entrances to NYC subway stations) for social engagements. WTWY wants to invite interested individuals to its annual gala to raise awareness and increase participation for women in tech, and the street teams’ agenda is to collect as many emails as possible and give out free tickets to the gala. In my analysis, I have made the following assumptions:

Assumptions

  • WTWY is constrained by time and manpower resources, hence insights from my analysis should identify top stations by traffic, as well as the peak periods in those stations.
  • Individuals who are interested in tech have a higher probability to be encountered in city center with a denser cluster of tech corporate offices.
  • The WTWY gala is imminent, and a week of MTA turnstile data is analyzed as an sample for the weeks leading to the gala.

#data-visualization #new-york-city #data-science #metis

Ray  Patel

Ray Patel

1623292080

Getting started with Time Series using Pandas

An introductory guide on getting started with the Time Series Analysis in Python

Time series analysis is the backbone for many companies since most businesses work by analyzing their past data to predict their future decisions. Analyzing such data can be tricky but Python, as a programming language, can help to deal with such data. Python has both inbuilt tools and external libraries, making the whole analysis process both seamless and easy. Python’s Panda s library is frequently used to import, manage, and analyze datasets in various formats. However, in this article, we’ll use it to analyze stock prices and perform some basic time-series operations.

#data-analysis #time-series-analysis #exploratory-data-analysis #stock-market-analysis #financial-analysis #getting started with time series using pandas

Ruth  Nabimanya

Ruth Nabimanya

1625027400

Projection Queries: A Way to Optimize Data Traffic

JPA provides several solutions for projection queries and DTO mapping. It is up to us to choose the most appropriate solution for each of our use cases.

In our modern, highly concurrent world, enterprise application developers have to deal with new challenges like huge data volumes, diversity of clients, and permanently changing business requirements. Now, it is a usual case when a microservice application has to serve various clients, and some of them are other microservices. These factors imply higher requirements for controlling data traffic. We cannot afford to send any excessive data and we need to respond to each request with data well-tailored for this particular client.

One option of customizing data traffic is the usage of projection queries; that is, queries that return a projection of domain objects. Almost all enterprise applications use some kind of ORM technology, and JPA is a standard way for its implementation. So, let’s see how we can implement projection queries based on JPA 2.2 specification.

Suppose we are to implement a collection management online application. The domain system is the following.

#tutorial #big data #optimization #querying #projection queries #projection queries: a way to optimize data traffic