Bài thuyết trình môn: Kỹ thuật phần mềm

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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:
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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
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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.
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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.
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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
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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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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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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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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
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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
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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
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- 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.
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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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