INFO 103 – Project description
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School
Drexel University *
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Course
103
Subject
Information Systems
Date
Dec 6, 2023
Type
Pages
7
Uploaded by Jomiiii
INFO 103 – Project description
Group member names:
Group member IDs: bz337, oo68
Instructions: Groups may have a maximum of
four
and a minimum of
two
people. Turn in one
document for your whole group.
1.
(5 pts)
Your project should fit into one of three categories. Please select one, below.
a.
Data product or project development.
Study the development of an
existing or hypothetical data product or project, focusing on value and
marketability.
b.
Data collection and curation.
Study an existing or hypothetical data source,
focusing on its challenges in curation, storage, and collection.
c.
Data technology and processing.
Study an existing or hypothetical
processing methodology or big data technology, focusing on problems and
applications.
2.
(15 pts) Who’s on this project’s team?
This team consists of two members: Jojo Oshinowo- Mustapaha and Bryce Zenon. The
skills and knowledge needed to efficiently carry out this project would mostly be
research based skills and general knowledge from this course, Introduction to Data
Science. We both have research skills in which we would both use to gather
information on the topic we are working on. There would also be knowledge applied
from what we have both learnt in this course, for example, the pre-processing methods
and API.
Bryce has worked on Google Analytics previously so he has some knowledge and
experience with this topic. This is very beneficial as he would be able to apply his
previous knowledge and skills to the project. I have some experience when it comes to
gaining insights on customer trends through and how these could be visualized through
Google analytics, as I have worked with a marketing and promotion team before.
The following roles were chosen that are apart of the data science world and are
required for Google Analytics to function properly:
Database Administrator
Necessary for data modeling and design, understanding databases that hold user
information. In this case, this would also be identifying and understanding the database
that holds the user's /website visitors information which is the page tags that have the
Javascript inserted in them.
Programmer
This person is literally required to program the software that will handle the data.
Data Scientist
This person is necessary to determine how to handle the data via predictive modeling,
machine learning (machine learning is one of the new features added to Google
Analytics: Google Analytic 4) , etc.
3.
(25 pts) Identify your specific topic of study, and provide a summary, below.
Google Analytics is a web analytics service offered by google that tracks and reports
website traffic. It became part of the Google Marketing Platform in 2015 and is available
for free to anyone with a Google account. Google analytics helps track website
performance and gather visitor insights. This helps businesses identify the sources of
user traffic, user trends and engagement, and to overall see the success of their
marketing strategies. Google analytics acquires its data from website visitors by using
page tags which function as a web bug or a web beacon. They do this by inserting a
JavaScript page tag into the code of each page. JavaScript is known as one of the core
technology programming languages of the World Wide Web. The tag therefore runs in
the web browser of the website visitors, collecting data and sending it to one of
Google’s data collection servers. This allows Google Analytics to create personalizable
reports in order to visualize and keep track of data. This data could be the number of
users, average session duration, page views and more.
Because Google Analytics relies on cookies, their system cannot collect data from
visitors who disable them. Google Analytics includes features that enable data
collection, data analysis, data monitoring, data visualization, data reporting and data
integration with other applications. Features include: Data filtering (manipulation and
funnel analysis), Data visualization and monitoring tool (including dashboards,
scoreboards and motion charts that display changes in data over time), Data collection
application program interfaces (APIs), predictive analytics, email-based sharing, and
Integration with other products like Google Ads, Google Data Studio, Salesforce
Marketing Cloud, Google AdSense, Google Optimize 360, Google Search Ads 360, and
Google Ad Manager. Google Analytics also allows its users to track up to 200 different
metrics to measure website performance. This includes bounce rate, page views pers
session, users, and more. Google Analytics consists of both metrics and dimensions
which are critical for proper interpretation of reports, this would all be further discussed
in the presentation. The type of data provided by Google can be categorized as user
acquisition data or user behavior data.
Google Analytics 4 is the most recent interiation of the service that was released in
October 2020. It includes new features such as machine learning and artificial
intelligence (AI) tools, deeper integrations with Google Ads, customer-centric reporting
designed around lifecycle data, additional codeless tracking features that provide data
with less latency, and enhanced data control features.
4.
(25 pts) Support your topic with reference materials and briefly summarize what each
piece of reference material tells you about your topic.
What is Google Analytics and how does it work?
https://www.techtarget.com/searchbusinessanalytics/definition/Google-Analytics
This website provides a broad overview of what Google Analytics is and how it works. It
goes into details of how it uses JavaScript to track user data via cookies as well as
provides an informative video that visually and audibly gives information on the product.
It then goes into detail about more features such as their metrics and the usability of its
features. This source focuses on the database and programming languages used in
processing data.
Introducing Google Analytics 4 (GA4)
https://support.google.com/analytics/answer/10089681
This is a wiki style post explaining the new Google Analytics 4 and how to prepare your
website to switch to it. It also details new features that separates Analytics 4 from the
Universal Analytics model that has been used up to this point. There are several links to
jumping between information for the legacy and new models as well as support
questions for both models.
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The Ultimate Guide to Google Analytics
https://blog.hubspot.com/marketing/google-analytics
This is an incredibly in depth and comprehensive guide for the user on how to use
Google Analytics. It goes into details on how to set up an analytics account to step by
step instructions on what each tool is and how to use it. The blog also boasts several
images to coincide with the instructions to ensure no confusion when trying to interpret
the reports and materials presented.
The top 7 Google Analytics reports that every data scientist needs to know
https://data36.com/google-analytics-reports-top/
This source basically provides an overview of the history of Google analytics and how it
has evolved overtime. It goes more in depth into the characteristics of the service and
how it functions.
How Google Analytics Can Help You Increase Leads and Sales
https://www.silverdisc.co.uk/blog/2020/09/11/how-google-analytics-can-help-you-increas
e-leads-and-sales
This article is written from the business perspective of using Google Analytics. It shows
how its powerful capabilities can be used to boost a businesses' leads and sales such
as knowing which users are leaving your website (or viewing), which products are more
popular and more.
Why you should stop using Google Analytics on your website
https://plausible.io/blog/remove-google-analytics
This blog serves as an alternative viewpoint for using Google Analytics. Despite it's
incredibly powerful software, Analytics is still created by Google which is a big tech
company. This blog presents arguments for using a different software because of the
amount of data that is collected from analytics and how it can compromise user security.
The blog highlights the flaws of analytics such as having a high script load for some
websites as well as its inaccuracy due to using cookies.
5.
(30 pts) Identify how your topic intersects with the different areas of the data science
life cycle.
Data acquisition:
Google Analytics falls directly in the data acquisition category of the data science
lifestyle. Because of its use of cookies on web pages, it directly acquires information
from the user to use for its analytical processes. Its acquisition isn't incredibly thorough
as it's a common form of web scraping, however what it does with the data it acquires
greatly assists businesses using its software with developing strategies for handling
users and potential commerce.
Data preparation:
As mentioned before, the entire purpose of Google Analytics is to take the data it
receives from its users and prepare it into a readable output that can be used by
companies. The ability to filter, manipulate, display and integrate the data among many
other various functions makes Google Analytics rather proficient at preparing data as
described by the data science lifestyle.
Hypothesis and modeling:
Google Analytics doesn't necessarily perform the hypothesis action but it can provide
models of the data requested and even provide predictive analytics. The user basically
uses the analytics software to then formulate or test hypotheses they've developed
through various business activities. The modeling is shown through the plethora of
display options available for the data that Analytics has compiled based on the user's
specifications.
Evaluation & interpretation:
This is where Google Analytics thrives. Although it doesn't necessarily make decisions
for the user on what to do with the data, it does provide an extensive list of
comprehensive ways to display the data that makes it easy to evaluate. Through
showing average user visits, users, sessions, page visits all able to be customized and
filtered based on location, browser type, age group and more, Google Analytics
prepares information that's easily readable. This then makes it able to be interpreted by
the business owner in a way that can benefit their business whether it's determining
what users are choosing what product the most or whether a page is worth having on
the website at all.
Deployment:
Analytics doesn't directly deploy a product or sample of a product. It does have
predictive analysis tools, however. Predictive analytics are used by taking current and
historical data and using it to predict future behaviors based on trends. Users can use
the predictive analytics to test a hypothesis or simply interpret the data to support or
refute the hypothesis.
Operations:
Google Analytics has a fantastic user dashboard that makes daily operations using its
software easy. One of the pros of the software is its beginner friendly capabilities which
harvests a well developed API to give the user power on a simple display. With multiple
available reports the user can draw up as well as anomaly detection, there are many
avenues with which users can maintain their website's marketing strategy by using
Google Analytics. The software does require some sort of user interaction, however, it
does not act autonomously all the time.
Optimization:
Google is a powerful technology company that has paved the way for many businesses,
small and large, to use its different software to suit their business needs. Google
Analytics is constantly being updated, as we mentioned with Analytics 4's recent
release, and therefore optimization is one of the highest criterias for success from the
Analytics team. The purpose of the product is to streamline business marketing and
analytical skills therefore many of the features mentioned before such as anomaly
detection and user reports are key tools to optimizing the business experience. Its
integration with many of Google's other products such as adsense means it will be at
the forefront of the online advertisement sphere which Google has maintained for some
time.
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Presentation Outline:
Requirements:
-
Motivation for topic
-
Related work/data/products
-
Technical details of how it works
-
How the technical details fit into the data science lifestyle
-
Individuals and skills required for topic to work
-
Outcomes and impacts: who/how/what will the topic impact
-
References for all material
I say we fill out information for each requirement and then place them on 1-2 slides
depending on the information. Then each take a slide and record the sections then I'll
compile them together and submit it then we'll be done!
Here's a
link
to the Google Slides, we can fill it out as we go.]
Okay great thanks for making this easier. Which parts would you be doing so i can work
on my parts ?