Bài thuyết trình môn: Kỹ thuật phần mềm

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Bài thuyết trình môn: Kỹ thuật phần mềm

Bài thuyết trình môn: Kỹ thuật phần mềm

107 54 lượt tải Tải xuống
ASSIGNMENT 2 FRONT SHEET
Qualification
BTEC Level 5 HND Diploma in Computing
Unit number and title
Unit 14: Business Intelligence
Submission date
31/10/2023
Date Received 1st submission
31/10/2023
Re-submission Date
Date Received 2nd submission
Student Name
Nguyen Xuan Trung
Student ID
GCH211026
Class
GCH1103
Assessor name
Nguyen Duc Giang
Student declaration
I certify that the assignment submission is entirely my own work and I fully understand the consequences of plagiarism. I understand that
making a false declaration is a form of malpractice.
Student’s signature
Trung
Grading grid
P4
P5
M3
D3
D4
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Summative Feedback: Resubmission Feedback:
Grade:
Assessor Signature:
Date:
IV Signature:
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BUSINESS
BUSINESS
INTELLIGENCE
INTELLIGENCE
Group 3
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TEAM MEMBER
TEAM MEMBER
NGUYEN XUAN TRUNG
BUI HAI NAM
NGUYEN NAM KHANH
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CONTRIBUTION
CONTRIBUTION
NGUYEN XUAN TRUNG BUI HAI NAM
BI tools
Explain dataset
Specific example for P5
BI techniques
Specific example for P5
NGUYEN NAM KHANH
General about BI
Specific example for P5.
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01.
01.
GENERAL ABOUT BI
GENERAL ABOUT BI
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Definition
Definition
Business Intelligence is a set of methodologies, process, architectures, and
technologies that transform raw data into meaningful and useful information used
to enable more effective strategic, tactical, and operational insights and decision-
making
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THE IMPORTANCE OF BI
THE IMPORTANCE OF BI
Improved decision-making:
Increased efficiency and productivity:
Increased competitive advantage:
Rely on data to make strategies and decisions
with better results.
Automate tasks, Optimize processes, cut costs.
Predict market trends, customer needs, and
competitor strategies.
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Real-life examples
Real-life examples
Steam tracks how long users play each game
Steam Uses BI to Track and analyze user behavior
Steam Uses BI to Track and analyze user behavior
Steam tracks what games users have purchased
Steam tracks the games that users have added to
their wishlist.
Steam tracks how users interact with each other, such as
by adding friends, sending messages, and playing games
together.
Steam tracks how users behave within games, such as
what achievements they unlock, what items they collect,
and what challenges they complete.
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Real-life examples
Real-life examples
Improve the user experience: Identify user preferences and
to tailor the platform accordingly
As a result of using BI to track and analyze user
As a result of using BI to track and analyze user
behavior, Steam is able to:
behavior, Steam is able to:
Improve the quality of its games: Steam shares
data on user behavior with game developers
Prevent fraud: Steam uses BI to detect and prevent fraudulent
activity, such as account theft and credit card fraud
Steam Sale Recommendations: Steam uses BI to
recommend games to users during sales
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02.
02.
BI TECHNIQUES
BI TECHNIQUES
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Collection Technique
Collection Technique
Data Collection: Gathering data from various sources, such
as databases, spreadsheets, external APIs, or sensors.
Data Cleansing: Cleaning and preparing data to remove
errors, duplicates, and inconsistencies.
Data Integration: Combining data from multiple sources to
create a unified dataset.
Data Transformation: Converting data into a suitable format
for analysis.
Data Labeling: Assigning tags or labels to data for
classification and categorization.
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Analysis Technique
Analysis Technique
Reporting: Creating summary reports that provide insights
into data trends and key performance indicators (KPIs).
Queries: Running queries on databases to extract specific
information or patterns from data.
Dashboards: Building interactive dashboards that visualize
data in real-time and enable users to explore data visually.
Data Visualization: Using charts, graphs, and infographics to
represent data for easy understanding.
Ad Hoc Analysis: On-the-fly analysis to answer specific
questions or explore data without predefined reports.
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Analytic Technique
Analytic Technique
Descriptive Analytics: Examining historical data to
understand past performance and trends.
Diagnostic Analytics: Investigating data to determine the
causes of specific events or trends.
Predictive Analytics: Using statistical and machine learning
models to forecast future outcomes.
Prescriptive Analytics: Recommending actions or strategies
based on analytical insights.
Regression Analysis: A statistical technique for modeling
and analyzing relationships between variables.
Machine Learning: Employing algorithms to identify
patterns, make predictions, and automate decision-
making.
Time Series Analysis: Analyzing data points collected over
time to identify trends and seasonality.
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03.
03.
BI TOOLS
BI TOOLS
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Programming tools
Programming tools
Visual Studio Code,
commonly known as VS
Code, is a nimble, cost-free,
and extensively adaptable
code editor created by
Microsoft. It offers a broad
spectrum of programming
language support, a wealth
of extensions, and an array of
features to facilitate efficient
coding, debugging, and
collaborative work on
software development
endeavors.
Python is a legible, high-level
programming language
appreciated by both novices
and professionals. Employed
in various domains such as
web development, scientific
research, data analysis,
artificial intelligence, and
automation, Python benefits
from a robust community
and a wealth of libraries.
Pandas is a freely available
Python library employed in
the handling and
examination of data. It
supplies data structures and
capabilities for the effective
manipulation of organized
data, like tables and time
series, rendering it a favored
instrument among data
scientists and analysts.
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Data visualizations tools
Data visualizations tools
CSV is an abbreviation for "Comma-
Separated Values."
It's a text-based file format commonly
utilized for transferring data between
distinct applications.
Users frequently export intricate data from
one application into a CSV file, which is
then transmitted to another application for
data import purposes.
Tableau stands out as a premier data
visualization application, offering
interactive visual representations.
It is a robust asset in the field of business
intelligence, seamlessly linking to data
sources, crafting dashboards, and
revealing insights without the need for
intricate coding.
Its ability to unveil patterns and trends
empowers informed decision-making.
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04.
04.
EXPLAIN DATASET
EXPLAIN DATASET
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Explanation
Explanation
This dataset pertains to the Steam platform and comprises 18
columns, each providing information on various aspects of
video games, including:
Game Information:
name
release_date
platforms
required_age
categories
genres
steamspy_tags
achievements
owners
Rating and price:
positive_ratings
negative_ratings
average_playtime
median_playtime
Third-Party Involvement:
Developer
Publisher
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05.
05.
PRE-PROCESS STEPS
PRE-PROCESS STEPS
ON DATASETS
ON DATASETS
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The code on the left shows
my csv file filtering process:
- First, I will read the file by
creating the variable df and
saving the read data into it.
- Then, I create the
filtered_df variable
representing the filtered
data according to the
criteria below.
- Next I will retrieve the first
700 data from the
filtered_df variable and
save it into a new csv file
called filtereddata.csv
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06.
06.
DASHBOARDS
DASHBOARDS
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DASHBOARD 1 - OVERVIEW OF STEAM
DASHBOARD 1 - OVERVIEW OF STEAM
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CHART 1.1
CHART 1.1
A bar chart is a graphical
representation of data using
vertical bars, where each
bar's height represents a
specific value or category.
It's a visual tool for
comparing and displaying
data, making it easy to
understand trends and
differences between
categories or data points.
Chart 1.1 shows the 8 games
with the most play time in
terms of their prices.
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CHART 1.2
CHART 1.2
A bar chart is a graphical
representation of data using
vertical bars, where each
bar's height represents a
specific value or category.
It's a visual tool for
comparing and displaying
data, making it easy to
understand trends and
differences between
categories or data points.
Chart 1.2 shows the 5 most
worth playing games,
determined through the
following criteria: most
playing time, price from 500
to 1000 dollars
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CHART 1.3
CHART 1.3
A pie chart is a circular
graphical representation
that displays data in the
form of a circle divided into
slices. Each slice's size is
proportional to the quantity
or percentage it represents
in the whole data set,
making it a visual tool for
illustrating the distribution of
different categories or
values within a dataset.
Chart 1.3 shows the games
with the most achievements
in 2013
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CHART 1.4
CHART 1.4
A line chart is a visual
representation of data
using connected lines to
show trends and changes
in values over a continuous
or sequential period. It
typically has a time-based
or ordered category on the
x-axis and data values on
the y-axis. This chart is
useful for highlighting
patterns and relationships
in data.
Chart 1.4 shows the number
of games downloaded in
2013, from January to
October.
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DASHBOARD 2 - SELLING GAME
DASHBOARD 2 - SELLING GAME
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CHART 2.1
CHART 2.1
A pie chart is a formal
circular chart that divides
data into percentage parts
of the total. It is often used
to represent the distribution
of categorical data visually.
Pie charts are suitable for
comparing percentages
between elements or
categories, but caution
should be exercised when
there are too many
elements or proportions
close together.
Chart 2.1 shows the games
with high prices in 2015
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CHART 2.2
CHART 2.2
A pie chart is a formal
circular chart that divides
data into percentage parts
of the total. It is often used
to represent the distribution
of categorical data visually.
Pie charts are suitable for
comparing percentages
between elements or
categories, but caution
should be exercised when
there are too many
elements or proportions
close together.
Chart 2.2 shows the games
with low prices in 2011
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CHART 2.3
CHART 2.3
Chart 2.3 shows
games with high
average play
times recently
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CHART 2.4
CHART 2.4
A bar chart is a statistical
chart that uses vertical
columns to represent data.
Each column represents the
value of a variable or group
of variables in the data.
Column charts are often
used to compare values
between elements or over
time. It is a powerful tool for
presenting data in an
intuitive and easy-to-
understand manner.
Chart 2.4 shows games with
popular game categories
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DASHBOARD 3 - CUSTOMER FEEDBACK
DASHBOARD 3 - CUSTOMER FEEDBACK
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CHART 3.1
CHART 3.1
The chart shows the top 700
most highly rated games on
Steam
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CHART 3.2
CHART 3.2
The chart shows the top lowest-
rated games on Steam, based on
the number of negative ratings
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CHART 3.3
CHART 3.3
The chart shows the
number of games and the
total positive reviews of the
games in a year
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CHART 3.4
CHART 3.4
The chart shows the quality
ratio of games based on the
total number of positive
reviews for ranking
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07.
07.
SPECIFIC EXAMPLE ABOUT BI
SPECIFIC EXAMPLE ABOUT BI
TOOLS TO MAKE EFFECTIVE
TOOLS TO MAKE EFFECTIVE
DECISION MAKING
DECISION MAKING
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MARKETING ENHANCEMENT
MARKETING ENHANCEMENT
- Python and tableau can help Steam promote
the company, as well as existing products.
- Python will analyze user trends when
purchasing from steam and tableau will play
the role of visualizing that data.
- This parallel approach will make marketing
easier.
- Python makes it easy to make marketing
effective by analyzing data about the most
played games recently. Or bring famous
games from previous years to promote.
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SELLING GAME
SELLING GAME
- Python is used to collect data, and Tableau is
used to visualize the data.
- The data pertains to selling games to
customers and may include customer data,
game data, sales data, user behavior data,
feedback and reviews, market trends, and
marketing campaign data.
Improving the Process:
- After collecting and visualizing the data, the
process of selling games to customers can be
improved through:
+ Data analysis to identify trends and
preferences.
+ Customer segmentation for tailored
marketing.
+ Optimization of pricing.
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RECEIVE CUSTOMER FEEDBACK
RECEIVE CUSTOMER FEEDBACK
- Steam leverages the collaborative power of
Python and Tableau to collect data and visualize it
to enable better business processes
- Python will collect data such as game name,
genre, number of positive and negative reviews
from users. While Tableau converts data into
charts, helping to display differences between
collected data.
- Results: based on user reviews to improve
customer service quality, improve user experience,
develop and launch products based on favorite
player genres.
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08.
08.
HOW BI TOOLS CAN USE FOR
HOW BI TOOLS CAN USE FOR
EXTEND TARGET AUDIENCE
EXTEND TARGET AUDIENCE
AND MAKE THEM MORE
AND MAKE THEM MORE
COMPETITIVE WITHIN THE
COMPETITIVE WITHIN THE
MARKET
MARKET
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Data-Driven Insights:
BI tools like Tableau, Power BI, and others allow companies to collect, analyze, and visualize large
volumes of data. This data can include customer demographics, preferences, and behavior,
helping businesses gain insights into their target audience.
Audience Segmentation:
By utilizing BI tools to segment the audience, companies can create highly targeted marketing
campaigns. Understanding the unique characteristics and preferences of different customer
segments enables tailored messaging and product offerings.
Performance Monitoring:
BI tools provide real-time and historical data on marketing campaign performance. This allows
businesses to track which strategies are effective in reaching their target audience and adjust
their approach accordingly.
Competitive Analysis:
BI tools can be used to gather data on competitors, their marketing strategies, and their audience.
Analyzing this information can help businesses identify gaps in the market or develop strategies
to differentiate themselves from competitors.
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09.
09.
THE LEGAL ISSUES INVOLVED
THE LEGAL ISSUES INVOLVED
IN EXPLOITING USER DATA
IN EXPLOITING USER DATA
FOR BUSINESS
FOR BUSINESS
INTELLIGENCE.
INTELLIGENCE.
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Data Security:
Companies must take reasonable measures to safeguard user data. Data
breaches can lead to legal liabilities and damage to the company's
reputation.
Data Minimization:
Companies should only collect data that is necessary for their business
purposes. Collecting excessive data that is not relevant can lead to legal
issues.
Data Retention Policies:
Having clear policies on how long user data is retained and when it will be
deleted is crucial. Over-retention of data can be a violation of data privacy
laws.
Sensitive Data Handling:
Special care should be taken when collecting and handling sensitive user
data, such as medical information or financial data. Laws often have
stricter requirements for this type of data.
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THANKYOU
By : Ingoude Company
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Preview text:

lOMoARcPSD|35919223
ASSIGNMENT 2 FRONT SHEET Qualification
BTEC Level 5 HND Diploma in Computing Unit number and title
Unit 14: Business Intelligence Submission date 31/10/2023
Date Received 1st submission 31/10/2023 Re-submission Date
Date Received 2nd submission Student Name Nguyen Xuan Trung Student ID GCH211026 Class GCH1103 Assessor name Nguyen Duc Giang Student declaration
I certify that the assignment submission is entirely my own work and I fully understand the consequences of plagiarism. I understand that
making a false declaration is a form of malpractice. Student’s signature Trung Grading grid P3 P4 P5 P6 M3 M4 D3 D4
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Summative Feedback: Resubmission Feedback: Grade: Assessor Signature: Date: IV Signature:
Downloaded by V?n Lê Ti?n (vanlehcp1152001@gmail.com) lOMoARcPSD|35919223 BUSINESS INTELLIGENCE Group 3
Downloaded by V?n Lê Ti?n (vanlehcp1152001@gmail.com) lOMoARcPSD|35919223 TEA EAM MEM EMBER NGUYEN XUAN TRUNG BUI HAI NAM NGUYEN NAM KHANH
Downloaded by V?n Lê Ti?n (vanlehcp1152001@gmail.com) lOMoARcPSD|35919223 CONTRIBUTION NGUYEN XUAN TRUNG BUI HAI NAM NGUYEN NAM KHANH BI tools BI techniques General about BI Explain dataset Specific example for P5 Specific example for P5. Specific example for P5
Downloaded by V?n Lê Ti?n (vanlehcp1152001@gmail.com) lOMoARcPSD|35919223 01. GENERAL ABOUT BI
Downloaded by V?n Lê Ti?n (vanlehcp1152001@gmail.com) lOMoARcPSD|35919223 Definition
Business Intel igence is a set of methodologies, process, architectures, and
technologies that transform raw data into meaningful and useful information used
to enable more effective strategic, tactical, and operational insights and decision- making
Downloaded by V?n Lê Ti?n (vanlehcp1152001@gmail.com) lOMoARcPSD|35919223 THE IMPORTANCE OF BI Improved decision-making:
Rely on data to make strategies and decisions with better results.
Increased efficiency and productivity:
Automate tasks, Optimize processes, cut costs.
Increased competitive advantage:
Predict market trends, customer needs, and competitor strategies.
Downloaded by V?n Lê Ti?n (vanlehcp1152001@gmail.com) lOMoARcPSD|35919223 Real-life examples
Steam Uses BI to Track and analyze user b r behavior
Steam tracks how long users play each game
Steam tracks what games users have purchased
Steam tracks the games that users have added to their wishlist.
Steam tracks how users interact with each other, such as
by adding friends, sending messages, and playing games together.
Steam tracks how users behave within games, such as
what achievements they unlock, what items they collect,
and what challenges they complete.
Downloaded by V?n Lê Ti?n (vanlehcp1152001@gmail.com) lOMoARcPSD|35919223 Real-life examples
As a result of using BI to track and analyze user behavior, Steam is able to:
Improve the user experience: Identify user preferences and
to tailor the platform accordingly
Improve the quality of its games: Steam shares
data on user behavior with game developers
Prevent fraud: Steam uses BI to detect and prevent fraudulent
activity, such as account theft and credit card fraud
Steam Sale Recommendations: Steam uses BI to
recommend games to users during sales
Downloaded by V?n Lê Ti?n (vanlehcp1152001@gmail.com) lOMoARcPSD|35919223 02. BI T I TECHNIQUES ES
Downloaded by V?n Lê Ti?n (vanlehcp1152001@gmail.com) lOMoARcPSD|35919223 Collection Technique
Data Collection: Gathering data from various sources, such
as databases, spreadsheets, external APIs, or sensors.
Data Cleansing: Cleaning and preparing data to remove
errors, duplicates, and inconsistencies.
Data Integration: Combining data from multiple sources to create a unified dataset.
Data Transformation: Converting data into a suitable format for analysis.
Data Labeling: Assigning tags or labels to data for
classification and categorization.
Downloaded by V?n Lê Ti?n (vanlehcp1152001@gmail.com) lOMoARcPSD|35919223 Analysis Technique
Reporting: Creating summary reports that provide insights
into data trends and key performance indicators (KPIs).
Queries: Running queries on databases to extract specific
information or patterns from data.
Dashboards: Building interactive dashboards that visualize
data in real-time and enable users to explore data visually.
Data Visualization: Using charts, graphs, and infographics to
represent data for easy understanding.
Ad Hoc Analysis: On-the-fly analysis to answer specific
questions or explore data without predefined reports.
Downloaded by V?n Lê Ti?n (vanlehcp1152001@gmail.com) lOMoARcPSD|35919223 Analytic Technique
Descriptive Analytics: Examining historical data to
understand past performance and trends.
Diagnostic Analytics: Investigating data to determine the
causes of specific events or trends.
Predictive Analytics: Using statistical and machine learning
models to forecast future outcomes.
Prescriptive Analytics: Recommending actions or strategies based on analytical insights.
Regression Analysis: A statistical technique for modeling
and analyzing relationships between variables.
Machine Learning: Employing algorithms to identify
patterns, make predictions, and automate decision- making.
Time Series Analysis: Analyzing data points collected over
time to identify trends and seasonality.
Downloaded by V?n Lê Ti?n (vanlehcp1152001@gmail.com) lOMoARcPSD|35919223 03. BI T I TOOLS
Downloaded by V?n Lê Ti?n (vanlehcp1152001@gmail.com) lOMoARcPSD|35919223 Programming tools Visual Studio Code, commonly known as VS
Python is a legible, high-level Code, is a nimble, cost-free, programming language and extensively adaptable appreciated by both novices Pandas is a freely available code editor created by and professionals. Employed Python library employed in Microsoft. It offers a broad in various domains such as the handling and spectrum of programming web development, scientific examination of data. It language support, a wealth research, data analysis, supplies data structures and of extensions, and an array of artificial intel igence, and capabilities for the effective
features to facilitate efficient automation, Python benefits manipulation of organized coding, debugging, and from a robust community data, like tables and time col aborative work on and a wealth of libraries. series, rendering it a favored software development instrument among data endeavors. scientists and analysts.
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Data visualizations tools
CSV is an abbreviation for "Comma-
Tableau stands out as a premier data Separated Values."
visualization application, offering
interactive visual representations.
It's a text-based file format commonly
utilized for transferring data between
It is a robust asset in the field of business distinct applications.
intel igence, seamlessly linking to data
sources, crafting dashboards, and
Users frequently export intricate data from
revealing insights without the need for
one application into a CSV file, which is intricate coding.
then transmitted to another application for data import purposes.
Its ability to unveil patterns and trends
empowers informed decision-making.
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Downloaded by V?n Lê Ti?n (vanlehcp1152001@gmail.com) lOMoARcPSD|35919223 Explanation
This dataset pertains to the Steam platform and comprises 18
columns, each providing information on various aspects of video games, including: Game Information: Rating and price:
Third-Party Involvement: name positive_ratings Developer release_date negative_ratings Publisher platforms average_playtime required_age median_playtime categories genres steamspy_tags achievements owners
Downloaded by V?n Lê Ti?n (vanlehcp1152001@gmail.com) lOMoARcPSD|35919223 05. PRE-PROCES ESS S STEP EPS ON DATASETS
Downloaded by V?n Lê Ti?n (vanlehcp1152001@gmail.com) lOMoARcPSD|35919223 The code on the left shows my csv file filtering process:
- First, I will read the file by creating the variable df and saving the read data into it. - Then, I create the filtered_df variable representing the filtered data according to the criteria below.
- Next I will retrieve the first 700 data from the filtered_df variable and save it into a new csv file called filtereddata.csv
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DASHBOARD 1 - OVERVIEW OF STEAM
Downloaded by V?n Lê Ti?n (vanlehcp1152001@gmail.com) lOMoARcPSD|35919223 CHART 1.1
A bar chart is a graphical representation of data using vertical bars, where each bar's height represents a specific value or category. It's a visual tool for comparing and displaying data, making it easy to understand trends and differences between categories or data points.
Chart 1.1 shows the 8 games with the most play time in terms of their prices.
Downloaded by V?n Lê Ti?n (vanlehcp1152001@gmail.com) lOMoARcPSD|35919223 CHART 1.2
A bar chart is a graphical representation of data using vertical bars, where each bar's height represents a specific value or category. It's a visual tool for comparing and displaying data, making it easy to understand trends and differences between categories or data points.
Chart 1.2 shows the 5 most worth playing games, determined through the following criteria: most playing time, price from 500 to 1000 dollars
Downloaded by V?n Lê Ti?n (vanlehcp1152001@gmail.com) lOMoARcPSD|35919223 CHART 1.3
A pie chart is a circular graphical representation that displays data in the form of a circle divided into slices. Each slice's size is proportional to the quantity or percentage it represents in the whole data set, making it a visual tool for
illustrating the distribution of different categories or values within a dataset.
Chart 1.3 shows the games with the most achievements in 2013
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A line chart is a visual representation of data using connected lines to show trends and changes in values over a continuous or sequential period. It typically has a time-based or ordered category on the x-axis and data values on the y-axis. This chart is useful for highlighting patterns and relationships in data.
Chart 1.4 shows the number of games downloaded in 2013, from January to October.
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DASHBOARD 2 - SlOMoARcPSD|35919223ELLING GAME
Downloaded by V?n Lê Ti?n (vanlehcp1152001@gmail.com) lOMoARcPSD|35919223 CHART 2.1 A pie chart is a formal circular chart that divides data into percentage parts of the total. It is often used to represent the distribution of categorical data visually. Pie charts are suitable for comparing percentages between elements or categories, but caution should be exercised when there are too many elements or proportions close together. Chart 2.1 shows the games with high prices in 2015
Downloaded by V?n Lê Ti?n (vanlehcp1152001@gmail.com) lOMoARcPSD|35919223 CHART 2.2 A pie chart is a formal circular chart that divides data into percentage parts of the total. It is often used to represent the distribution of categorical data visually. Pie charts are suitable for comparing percentages between elements or categories, but caution should be exercised when there are too many elements or proportions close together. Chart 2.2 shows the games with low prices in 2011
Downloaded by V?n Lê Ti?n (vanlehcp1152001@gmail.com) lOMoARcPSD|35919223 CHART 2.3 Chart 2.3 shows games with high average play times recently
Downloaded by V?n Lê Ti?n (vanlehcp1152001@gmail.com) lOMoARcPSD|35919223 CHART 2.4 A bar chart is a statistical chart that uses vertical columns to represent data. Each column represents the value of a variable or group of variables in the data. Column charts are often used to compare values between elements or over
time. It is a powerful tool for presenting data in an intuitive and easy-to- understand manner. Chart 2.4 shows games with popular game categories
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DASHBOARD 3 - CUSTOMER FEEDBACK
Downloaded by V?n Lê Ti?n (vanlehcp1152001@gmail.com) lOMoARcPSD|35919223 CHART 3.1 The chart shows the top 700 most highly rated games on Steam
Downloaded by V?n Lê Ti?n (vanlehcp1152001@gmail.com) lOMoARcPSD|35919223 CHART 3.2
The chart shows the top lowest- rated games on Steam, based on the number of negative ratings
Downloaded by V?n Lê Ti?n (vanlehcp1152001@gmail.com) lOMoARcPSD|35919223 CHART 3.3 The chart shows the number of games and the total positive reviews of the games in a year
Downloaded by V?n Lê Ti?n (vanlehcp1152001@gmail.com) lOMoARcPSD|35919223 CHART 3.4 The chart shows the quality ratio of games based on the total number of positive reviews for ranking
Downloaded by V?n Lê Ti?n (vanlehcp1152001@gmail.com) lOMoARcPSD|35919223 07. SPECIF IFIC E X EXAMPLE A E ABOUT B BI TOOLS TO M MAKE E E EFF FFEC ECTIV IVE DECIS ISIO ION MAKING
Downloaded by V?n Lê Ti?n (vanlehcp1152001@gmail.com) lOMoARcPSD|35919223 MARKETING ENHANCEMENT
- Python and tableau can help Steam promote
the company, as wel as existing products.
- Python wil analyze user trends when
purchasing from steam and tableau wil play
the role of visualizing that data.
- This paral el approach wil make marketing easier.
- Python makes it easy to make marketing
effective by analyzing data about the most
played games recently. Or bring famous
games from previous years to promote.
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SELLING GAMElOMoARcPSD|35919223
- Python is used to col ect data, and Tableau is used to visualize the data.
- The data pertains to sel ing games to
customers and may include customer data,
game data, sales data, user behavior data,
feedback and reviews, market trends, and marketing campaign data. Improving the Process:
- After col ecting and visualizing the data, the
process of sel ing games to customers can be improved through:
+ Data analysis to identify trends and preferences.
+ Customer segmentation for tailored marketing. + Optimization of pricing.
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RECEIVE CUSTOlOMoARcPSD|35919223MER FEEDBACK
- Steam leverages the col aborative power of
Python and Tableau to col ect data and visualize it
to enable better business processes
- Python wil col ect data such as game name,
genre, number of positive and negative reviews
from users. While Tableau converts data into
charts, helping to display differences between col ected data.
- Results: based on user reviews to improve
customer service quality, improve user experience,
develop and launch products based on favorite player genres.
Downloaded by V?n Lê Ti?n (vanlehcp1152001@gmail.com) lOMoARcPSD|35919223 08. HOW BI T I TOOLS C CAN U USE F E FOR EXTEND TARGET A AUDIE I N ENCE AND MAKE T E THEM EM MORE COMPETITIVE W E WIT ITHIN IN T THE MARKET
Downloaded by V?n Lê Ti?n (vanlehcp1152001@gmail.com) lOMoARcPSD|35919223 Data-Driven Insights:
BI tools like Tableau, Power BI, and others al ow companies to col ect, analyze, and visualize large
volumes of data. This data can include customer demographics, preferences, and behavior,
helping businesses gain insights into their target audience. Audience Segmentation:
By utilizing BI tools to segment the audience, companies can create highly targeted marketing
campaigns. Understanding the unique characteristics and preferences of different customer
segments enables tailored messaging and product offerings. Performance Monitoring:
BI tools provide real-time and historical data on marketing campaign performance. This al ows
businesses to track which strategies are effective in reaching their target audience and adjust their approach accordingly. Competitive Analysis:
BI tools can be used to gather data on competitors, their marketing strategies, and their audience.
Analyzing this information can help businesses identify gaps in the market or develop strategies
to differentiate themselves from competitors.
Downloaded by V?n Lê Ti?n (vanlehcp1152001@gmail.com) lOMoARcPSD|35919223 09. THE LEGAL IS ISSUES ES IN INVOLVED IN E EXPLOIT ITIN ING U USER ER DATA FOR B BUSIN INES ESS INTELLIGENCE. E.
Downloaded by V?n Lê Ti?n (vanlehcp1152001@gmail.com) lOMoARcPSD|35919223 Data Security:
Companies must take reasonable measures to safeguard user data. Data
breaches can lead to legal liabilities and damage to the company's reputation. Data Minimization:
Companies should only collect data that is necessary for their business
purposes. Collecting excessive data that is not relevant can lead to legal issues.
Data Retention Policies:
Having clear policies on how long user data is retained and when it will be
deleted is crucial. Over-retention of data can be a violation of data privacy laws.
Sensitive Data Handling:
Special care should be taken when collecting and handling sensitive user
data, such as medical information or financial data. Laws often have
stricter requirements for this type of data.
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