Final Project Guide: Data Analysis

:information_source: Project Overview :bar_chart: Difficulty Level: Intermediate to Advanced
⏱️ Estimated Time: 3 weeks (L21-L23)
:dart: Skills Practiced:

• Data cleaning and preprocessing
• Exploratory data analysis (EDA)
• Data visualization with Python
• Statistical analysis and interpretation
• Professional report writing
• Technical presentation skills

:emoji:️ Project Roadmap

Week 21: Dataset Selection & Cleaning
├── Choose dataset aligned with theme
├── Set up Google Colab environment
├── Implement data cleaning procedures
└── Begin exploratory analysis

Week 22: Analysis & Report Writing
├── Perform in-depth analysis
├── Create visualizations
├── Write comprehensive report
└── Prepare supporting evidence

Week 23: Presentation & Submission
├── Create presentation slides
├── Practice delivery
├── Present findings (live/recorded)
└── Submit all deliverables

Overview

This guide outlines the requirements, timeline, and evaluation criteria for the final data analysis project. The project involves selecting a dataset, performing data analysis, and presenting findings through code, documentation, and presentation.

Project Timeline

:clipboard: Project Components

This project consists of three essential components:

One. :computer: Code Implementation

• Platform: Google Colab Notebook
• Requirements:
  • Data cleaning operations
  • Data analysis techniques
  • Data manipulation
  • Data visualization

:warning: Critical Setup Make sure to save your Google Colab notebook regularly! Use File -> Save a copy in Drive to prevent losing your work. Enable automatic saving by going to Tools -> Settings -> Auto-save.

2. :page_facing_up: Analysis Report

• Format: Google Docs (or equivalent document format)
• Requirements:
  • Cover page
  • Overview/Introduction
    • Dataset background explanation
    • Clear project objectives
  • Results section (minimum 2 distinct results)
    • Each result must include at least 2 figures:
      • At least 1 graph/chart
      • At least 1 table
    • Observations about each result
    • Reasoning and interpretation
    • Supporting evidence from credible sources
  • Conclusion
    • Summary of key findings
    • Recommended actions based on results
    • Potential improvements and future work
  • References

3. :emoji: Presentation

• Duration: 5-10 minutes
• Format Options:
  • Live presentation
  • Video recording (uploaded to Google Drive)
• Requirements:
  • Professional slides that include:
    • Title/Introduction
    • Project overview
    • Results with visualizations
    • Conclusion and recommendations
  • Clear explanation of:
    • Project background and objectives
    • Analysis results and significance
    • Conclusions and potential applications

:hammer_and_wrench: Technical Implementation Guide

Data Preparation Workflow

1. Dataset Selection
   • Choose a CSV dataset relevant to your selected theme
   • Ensure sufficient complexity and data points

:bulb: Dataset Sources Popular platforms for finding datasets:

• Kaggle: Wide variety of real-world datasets
• Data.gov: Government open data
• UCI Machine Learning Repository: Academic datasets
• Google Dataset Search: Search across multiple sources

2. Data Cleaning

   • Check for and handle duplicate entries
   • Identify and address missing values
   • Remove or transform irrelevant columns
   • Standardize data formats and values

3. Data Manipulation

   • Apply appropriate techniques:
     • Sorting and filtering
     • Grouping related data
     • Joining datasets when applicable
     • Transforming variables as needed

4. Data Visualization

   • Create meaningful visualizations with:
     • Clear axes (x-axis, y-axis)
     • Descriptive title
     • Appropriate labels
     • Legend when using multiple data series
     • Consistent formatting and style

Example Visualizations

Your visualizations should effectively communicate your findings. Key components include:

• Title: Descriptive and specific
• Axes: Properly labeled with units
• Legend: When multiple data series are displayed
• Data Labels: When appropriate for clarity
• Color Scheme: Logical and accessible

:dart: Project Themes

Select one of these broader themes and develop a specific research question:

One. :bulb: Technology Applications

• AI chatbots and their adoption in 2023
• Communication app usage patterns
• IoT device popularity and market trends

2. :video_game: Gaming and Entertainment

• Highest-rated games (2020-2023)
• Online gaming participation metrics
• E-sports growth and demographics

3. :emoji: Sustainable Development Goals (SDGs)

• Choose from the 17 UN SDGs for analysis
• Focus on measurable metrics and trends

4. :bar_chart: Alternative Topics

• Social media's impact on mental health
• Technology in education
• COVID-19 economic impact analysis
• STEM education performance factors

:memo: Theme Selection Tip Choose a theme that genuinely interests you! Your enthusiasm will show in your analysis and presentation. Consider topics related to your hobbies, career interests, or current events.

:bar_chart: Evaluation Criteria

Projects will be evaluated on a scale of 1-5 for each of these criteria:

:computer: Code Quality (25%)

• Implementation of required techniques
• Code organization and clarity
• Independent problem-solving ability
• Technical sophistication

:page_facing_up: Analysis Report (25%)

• Clarity of objectives and methodology
• Depth of analysis and insights
• Quality of data presentation
• Logical flow and organization
• Supporting evidence and references

:dart: Results & Objectives (20%)

• Alignment between objectives and results
• Relevance and practical significance
• Depth of insights derived from data
• Quality of supporting evidence

:art: Presentation Quality (15%)

• Slide design and organization
• Clarity of visual elements
• Appropriate level of detail
• Professional formatting

:emoji:️ Presentation Delivery (15%)

• Clear communication of key points
• Logical flow of presentation
• Demonstrated understanding of material
• Ability to explain technical concepts clearly
• Time management

:warning: Grading Note Each criterion is equally important! Don't focus solely on code at the expense of your report or presentation. A balanced approach leads to the best final grade.

:bulb: Project Implementation Tips

For Code Development:

• Begin by exploring the dataset structure before cleaning
• Document each step of your data cleaning process
• Create reusable functions for common operations
• Include comments explaining your reasoning

For Report Writing:

• Start with a clear statement of your research question
• Provide context for why this analysis matters
• Be specific about methodology and limitations
• Connect your findings to practical applications

For Presentation:

• Focus on key insights rather than technical details
• Practice your presentation to ensure timing
• Prepare for potential questions
• Highlight the most interesting or surprising findings

:bug: Debugging Tips & Common Errors

Common Data Cleaning Issues:

1. "FileNotFoundError" when loading CSV

   • Solution: Upload file to Colab first using the files panel
   • Alternative: Use direct URL if dataset is online

2. Missing values causing errors

   • Solution: Use df.fillna() or df.dropna() appropriately
   • Check with df.isnull().sum() first

3. Data type mismatches

   • Solution: Convert columns using pd.to_numeric() or pd.to_datetime()
   • Always check dtypes with df.dtypes

Visualization Problems:

1. Overlapping labels

   • Solution: Rotate labels with plt.xticks(rotation=45)
   • Or use plt.tight_layout()

2. Missing legend or labels

   • Solution: Always add plt.xlabel(), plt.ylabel(), and plt.title()
   • Use plt.legend() for multiple series

:bulb: Pro Debugging Tip When stuck, use print() statements liberally to understand your data at each step. Check shapes with df.shape and preview with df.head().

:package: Submission Requirements

Final submission should include:

1. Google Colab notebook(s) with all code
2. Complete report document
3. Video recording
4. Any supplementary materials referenced

:white_check_mark: Milestone Checkpoints

Week 21 Checkpoint:

• Dataset selected and downloaded
• Google Colab notebook created
• Data successfully loaded
• Initial cleaning completed
• Basic statistics calculated

Week 22 Checkpoint:

• At least 2 meaningful analyses completed
• All required visualizations created
• Report draft completed
• External sources researched
• Conclusions formulated

Week 23 Checkpoint:

• Presentation slides finalized
• Practice run completed
• All files organized for submission
• Video recorded (if applicable)
• Final review done

:emoji: Project Submission

Please double-check your project before submitting it. Ensure you have included the Python code file, Report, and video explanation as required.

:warning: Pre-Submission Checklist

• Code runs without errors
• Report follows all formatting requirements
• Presentation is within 5-10 minute limit
• All files are properly named
• Links are working (especially for video)

:link: Submit your project here

:bar_chart: Project Guideline: Dataset Selection & Data Cleaning

:information_source: Week 21 Focus This section provides specific guidance for Week 21 activities of the final project, focusing on dataset selection, data cleaning, and initial analysis setup. These foundational steps will prepare you for the analysis and reporting phases in subsequent weeks.

:dart: Week 21 Objectives

By the end of Week 21, you should have:

1. Selected an appropriate dataset for your project
2. Created a Google Colab notebook for your analysis
3. Implemented data cleaning procedures
4. Begun initial exploratory data analysis

Required Tasks

One. Dataset Selection

• Choose a CSV dataset that aligns with one of the project themes:

  • Technology Applications (AI chatbots, communication apps, IoT devices)
  • Gaming and Entertainment (game ratings, online gaming, e-sports)
  • Sustainable Development Goals (any of the 17 UN SDGs)
  • Alternative Topics (social media effects, educational technology, etc.)

• Dataset requirements:

  • Must be in CSV format
  • Should contain sufficient data points for meaningful analysis
  • Should have multiple variables to explore relationships
  • Must be relevant to your chosen theme/topic

• Recommended sources:

  • Kaggle datasets
  • Data.gov and other open data portals
  • Industry/academic repositories
  • Public datasets from research institutions

2. Google Colab Notebook Setup

Create a new Google Colab notebook with the following sections:

# Final Project: [Your Project Title]
## Dataset: [Dataset Name and Source]
## Objective: [Brief description of your analysis goals]

## 1. Data Import and Initial Exploration
[Your code for importing and exploring the data]

## 2. Data Cleaning
[Your code for cleaning operations]

## 3. Preliminary Data Analysis
[Your code for initial exploratory analysis]

:warning: Colab Setup Tip Start your notebook with these essential imports:

python

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

3. Data Cleaning Implementation

Your data cleaning section should include operations to:

• Check for duplicates

  • Identify how many duplicate rows exist in your dataset
  • Decide whether to remove duplicates based on your analysis needs
  • Implement appropriate duplicate removal if necessary

• Identify missing values

  • Calculate and display the number of missing values per column
  • Determine the best approach for handling missing values:
    • Removal (if rows/columns have too many missing values)
    • Imputation (filling with mean, median, mode, or predicted values)
    • Keeping as missing (if appropriate for your analysis)
  • Implement your chosen approach

• Assess column relevance

  • Review all columns to determine their relevance to your objectives
  • Identify columns with little or no analytical value
  • Remove unnecessary columns that won't contribute to your analysis
  • Document your reasoning for removing any columns

• Standardize data formats

  • Ensure text data is properly formatted (e.g., consistent case, no extra spaces)
  • Verify date/time data is in a consistent format
  • Check that numeric data has appropriate decimal precision
  • Convert categorical variables to the appropriate format for analysis

4. Initial Data Exploration

After cleaning, perform initial exploratory analysis:

• Basic statistics

  • Calculate descriptive statistics for numeric columns
  • Note key insights about central tendency and variation

• Frequency analysis

  • Determine the distribution of categorical variables
  • Identify the most/least common categories
  • Look for imbalanced categories that might affect analysis

• Simple visualizations

  • Create appropriate visualizations to understand data distribution
  • Explore potential relationships between variables
  • Document initial observations about patterns in the data

:white_check_mark: Data Cleaning Checklist

Before moving to Week 22, ensure you have:

• Removed or handled duplicate entries
• Addressed missing values appropriately
• Removed irrelevant columns
• Standardized data formats
• Corrected any errors or inconsistencies
• Documented your cleaning process with comments
• Verified data types are appropriate
• Confirmed cleaned data is ready for analysis

:memo: Quality Check After cleaning, your dataset should have:

• No unexpected null values
• Consistent data types across columns
• Logical value ranges (no negative ages, etc.)
• Clear column names that describe the data

Technical Tips

1. Data loading considerations:

   • Understand how to upload files to Google Colab
   • Know how to read CSV files using pandas
   • Be familiar with options for handling different CSV formats (delimiters, headers, etc.)

2. Verification practices:

   • Always check your dataset before and after each cleaning operation
   • Compare row and column counts to ensure you're not losing data unexpectedly
   • Review a sample of the data to confirm cleaning operations worked as expected

3. Backup strategy:

   • Create a copy of your original data before making changes
   • Work with copies of your data frame when performing cleaning operations
   • This allows you to revert to previous stages if needed

4. Documentation importance:

   • Add markdown cells explaining your reasoning for each cleaning decision
   • Note any assumptions you're making about the data
   • Record any patterns or anomalies you discover during cleaning

Next Steps for Week 22

After completing Week 21 tasks, you will be prepared to:

• Perform in-depth data analysis
• Create meaningful visualizations
• Begin drafting your analysis report
• Identify key insights from your data

Keep your cleaned dataset and initial analysis ready for these next steps.

:memo: Project Guide: Report Making

:information_source: Week 22 Focus This section provides specific guidance for Week 22 activities of the final project, focusing on creating a comprehensive analysis report based on the data analysis performed in Week 21. Your report will document your findings, observations, and conclusions in a clear, structured format.

:dart: Week 22 Objectives

By the end of Week 22, you should have:

1. Created a Google Doc for your analysis report
2. Completed all required sections of the report
3. Included at least 2 distinct results with supporting tables and graphs
4. Ensured all visual elements are properly labeled and explained
5. Finalized the report with appropriate formatting and citations

Report Structure Requirements

Your report must include the following sections:

One. Cover Page

The cover page should include:

• Title of your project
• "Analysis Report" subtitle
• Your name (or team members' names)
• Group/team identifier (if applicable)
• Class information

2. Overview

The overview section should include:

• Background: Explain how you obtained your dataset

  • Describe the source of your data (e.g., "The mobile apps data is from Kaggle.com")
  • Provide context about the dataset (size, time period covered, limitations)
  • Explain any preprocessing steps you performed before analysis

• Objectives: State clearly what you aimed to discover from the data

  • Frame objectives as specific questions or hypotheses
  • Explain why these objectives are relevant or interesting
  • Example: "To study which app categories have the highest number of downloads"

3. Results

You must include at least 2 distinct results in your report. For each result:

• Tables and Graphs:

  • Include at least 1 table and 1 graph for each result
  • Ensure all tables and graphs have clear titles
  • Label axes, legends, and data points appropriately
  • Use consistent formatting and color schemes

• Observation:

  • Describe what the data shows in your tables and graphs
  • Point out patterns, trends, outliers, or notable findings
  • Use specific numbers and values from your analysis
  • Keep observations factual and evidence-based

• Reason:

  • Provide your interpretation of why these patterns exist
  • Explain potential causes for the trends observed
  • Connect your findings to your initial objectives
  • This section should reflect your own analytical thinking

• Support:

  • Reference external sources that support your reasoning
  • Include information from academic papers, industry reports, or credible articles
  • Explain how these external sources relate to your findings
  • All supporting references must be cited in your references section

4. Conclusion

The conclusion should synthesize your findings and include:

• Summary:

  • Briefly recap the main results of your analysis
  • Relate findings back to your original objectives
  • Highlight the most significant discoveries

• Suggestions:

  • Propose actions that could be taken based on your results
  • Explain who might benefit from these suggestions
  • Connect suggestions directly to your findings

• Improvements:

  • Discuss how your analysis could be enhanced in the future
  • Identify limitations in your current approach
  • Suggest additional data or methods that could strengthen the analysis

5. References

• List all external sources you referenced in your report
• Include full URLs for online sources
• Use a consistent citation format
• Ensure all supporting evidence in your "Support" sections is properly cited

Report Writing Guidelines

Content Quality

1. Clarity: Explain concepts in simple, understandable language

   • Avoid jargon unless necessary and defined
   • Use concrete examples to illustrate abstract points

2. Thoroughness: Provide complete explanations

   • Don't leave logical gaps in your reasoning
   • Include sufficient detail for readers to follow your analysis

3. Evidence-based: Support claims with data

   • Reference specific numbers from your analysis
   • Don't make unsupported generalizations

4. Relevance: Stay focused on your objectives

   • Every section should contribute to answering your research questions
   • Avoid tangential information

Visual Elements

1. Tables:

   • Keep tables clean and readable
   • Include clear headers for all columns
   • Format numbers consistently (decimal places, units)
   • Include a title that describes the content

2. Graphs:

   • Choose appropriate graph types for your data
   • Label all axes with units where applicable
   • Include a legend if multiple data series are present
   • Use colors thoughtfully for clarity

3. Formatting:

   • Use consistent fonts and text sizes
   • Apply appropriate spacing between sections
   • Use headings and subheadings to organize content
   • Include page numbers for longer reports

:warning: Common Pitfalls to Avoid

1. Missing connections: Failing to relate your results to your objectives
2. Insufficient analysis: Just describing data without interpretation
3. Unsupported claims: Making statements without evidence
4. Poor visual presentation: Cluttered or mislabeled graphs and tables
5. Inconsistent formatting: Varying styles throughout the document
6. Lack of citations: Not acknowledging external sources

:warning: Critical Report Mistakes The most common reason for low report scores:

• Writing observations without explaining WHY patterns exist
• Forgetting to cite external sources in the support section
• Using low-quality visualizations without proper labels

Report Template

A template has been provided for your convenience. It includes:

• Correctly formatted cover page
• Section headers for all required components
• Placeholders for results with sample image placements
• Space for conclusion and references

You can access the template here: Report Template

Using the Template

1. Make a copy of the template:

   • Open the template link
   • Go to File > Make a copy
   • Name your document appropriately (e.g., "Final Project Analysis Report - [Your Name/Group]")

2. Template structure:

   • The template includes the following pre-formatted sections:
     rust

     Title and Analysis Report
     Name:
     Group:
     Class:
     
     Overview
     [Space for your overview paragraph]
     
     Results
     1. Result number 1
     [Placeholders for table and graph images]
     
     2. Result number 2
     [Placeholders for table and graph images]
     
     Conclusion
     [Space for your conclusion paragraph]
     
     Reference
     [Numbered list for your references]
     

3. Inserting your visualizations:

   • Replace the image placeholders with your actual tables and graphs
   • In Google Docs, use Insert > Image to add your visualizations
   • Make sure to position and resize images appropriately

4. Formatting guide:

   • Maintain consistent font styles (the template uses default Google Docs fonts)
   • Use the built-in heading styles for section titles
   • Keep reasonable margins and spacing between elements
   • Number your results sections as shown in the template

:white_check_mark: Week 22 Checklist

Before moving to Week 23, ensure you have:

• Created a Google Doc using the provided template
• Completed the cover page with all required information
• Written a comprehensive overview with background and objectives
• Included at least 2 distinct results with tables and graphs
• Provided observations, reasoning, and supporting evidence for each result
• Written a conclusion with summary, suggestions, and improvements
• Listed all references with proper citations
• Checked for spelling, grammar, and formatting consistency
• Verified that all visual elements are properly labeled
• Ensured the report meets the evaluation criteria from the project guide

:bulb: Final Report Review Ask a friend or family member to read your report. If they can understand your findings without seeing your code, you've written a good report!

Next Steps for Week 23

After completing the analysis report in Week 22, you will be prepared to:

• Create a presentation based on your report
• Rehearse your presentation delivery
• Finalize all project deliverables

Keep your completed report accessible as you will reference it when creating your presentation slides.

:emoji: Project Guide: Presentation

:information_source: Week 23 Focus This section provides specific guidance for Week 23 activities of the final project, focusing on delivering an effective presentation of your data analysis findings. This is the culmination of your project work from Weeks 21-22 and your opportunity to showcase your analysis, insights, and communication skills.

:dart: Week 23 Objectives

By the end of Week 23, you should have:

1. Finalized your presentation slides
2. Delivered your presentation (live or recorded)
3. Demonstrated your understanding of the data analysis process
4. Effectively communicated your findings and their significance

Presentation Requirements

Format Options

You have two presentation format options:

One. Live Presentation

• Deliver your presentation live during class time
• Suitable for groups with all members present
• Ideal for those with stable internet connections
• Allows for immediate Q&A and feedback

2. Video Presentation

• Record your presentation in advance
• Suitable for groups whose members cannot all be present
• Good option for those with unstable internet connections
• Must be uploaded to Google Drive before the deadline

Duration and Participation

• Time Limit: 5-10 minutes
• Participation: All members must present a portion of the material
• Sequence: Presentation order will be determined randomly using Picker Wheel
  • The sequence will be posted on the Telebort Dashboard
  • Be prepared to present when your turn comes

Presentation Slide Requirements

Your presentation must include the following sections:

One. Title Slide

• Project title
• Your name (or team members' names)
• Class information

2. Overview Section

• Introduction to your project topic
• Background information on your dataset
• Clear statement of your objectives

3. Results Section

• Present at least 2 distinct results from your analysis
• Each result should include:
  • At least one graph and one table
  • Clear explanation of what the data shows
  • Your interpretation of the findings
  • Supporting evidence from credible sources

4. Conclusion Section

• Summary of key findings
• Suggestions for actions based on your results
• Potential improvements or next steps

:art: Effective Slide Design

:white_check_mark: Do's

1. Use concise, point-form text

   • Break long sentences into bullet points
   • Example: "The category with the most apps: Game (301)"
   • Instead of lengthy paragraphs

2. Choose comfortable background colors

   • Soft, neutral colors (light beige, pale blue, soft green)
   • Consistent color scheme throughout
   • High contrast between text and background

3. Use appropriate fonts and styles

   • Readable fonts (Arial, Calibri, Georgia)
   • Consistent font usage throughout
   • Adequate font size (min. 24pt for body text, 32pt for headings)

4. Include relevant photos and animations

   • Visual elements that support your message
   • Clean, professional graphics
   • Appropriate charts and diagrams

:x: Don'ts

1. Avoid lengthy sentences on slides

   • Don't put full paragraphs on slides
   • Don't include excessive detail in text form

2. Avoid distracting backgrounds

   • No bright, jarring colors
   • No busy patterns
   • No excessive graphics

3. Avoid fancy or hard-to-read fonts

   • No decorative or script fonts
   • No mixing too many different fonts
   • No font sizes that are too small

4. Avoid distracting images

   • No irrelevant pictures
   • No excessive animations
   • No cluttered visuals

:bulb: Slide Design Rule Follow the 6-6-6 rule: No more than 6 bullet points per slide, 6 words per bullet, and spend about 6 seconds explaining each point.

:emoji:️ Presentation Delivery Guidelines

:white_check_mark: Do's

1. Be confident

   • Stand (or sit) straight
   • Speak clearly and at an appropriate volume
   • Make eye contact with the audience or camera

2. Explain in your own words

   • Don't read directly from the slides
   • Show your understanding of the material
   • Use natural, conversational language

3. Show your passion

   • Demonstrate enthusiasm for your topic
   • Vary your tone to emphasize key points
   • Share why your findings matter

4. Ensure all team members participate

   • Divide speaking parts equally
   • Support each other during transitions
   • Acknowledge team contributions

:x: Don'ts

1. Don't appear too nervous

   • Practice beforehand to build confidence
   • Remember: everyone is supportive
   • Take deep breaths if feeling anxious

2. Don't read slides verbatim

   • Use slides as prompts, not a script
   • Add value beyond what's written on slides
   • Look at the audience, not just the screen

3. Don't be casual or unprepared

   • Treat the presentation professionally
   • Practice your timing
   • Anticipate potential questions

4. Don't let only one person dominate

   • Ensure balanced participation
   • Practice smooth transitions between speakers
   • Support team members if they struggle

:memo: Presentation Confidence Booster Remember: You're the expert on your analysis! You've spent weeks working on this project. Your audience wants to learn from you, not judge you.

Preparation Tips

1. Practice your presentation multiple times

   • Rehearse individually and as a team
   • Time your presentation to stay within limits
   • Practice with the actual slides you'll use

2. Prepare for technical issues

   • Have a backup of your presentation
   • Test all equipment beforehand
   • Have a contingency plan

3. Anticipate questions

   • Consider what questions might arise
   • Be prepared to elaborate on your findings
   • Know your data well enough to provide additional insights

4. For video presentations

   • Ensure good lighting and clear audio
   • Find a quiet location without background noise
   • Test your recording setup before the final recording

Recommended Slide Resources

For professional-looking slide templates, consider these sources:

• SlidesMania - Free templates for various presentation styles
• SlidesGo - Modern, creative presentation templates
• Canva - Variety, animated presentation templates

:white_check_mark: Week 23 Checklist

Before your presentation day, ensure you have:

• Finalized all slides with appropriate content
• Checked for visual appeal and readability
• Practiced your presentation multiple times
• Confirmed all team members know their parts
• Tested any technology you'll be using
• Prepared any notes or speaking aids
• For video presentations: recorded, reviewed, and uploaded your video
• Checked the presentation sequence on the Telebort Dashboard

:warning: Last-Minute Check Test your technology setup at least 1 hour before presentation time:

• Internet connection stability
• Microphone and camera functionality
• Screen sharing capabilities
• Backup plan if technical issues arise

Final Reminders

• Your presentation is the culmination of weeks of work - be proud of what you've accomplished!
• Focus on clearly communicating your findings and their significance
• Remember that effective presentation skills are valuable beyond this project
• The presentation evaluation focuses on both content quality and delivery style
• Approach this as an opportunity to share your discoveries, not just as an assessment

Good luck with your presentation!

:rocket: Extension Challenges

Ready to go above and beyond? Try these advanced challenges:

:star2: Advanced Analysis Challenges

1. Machine Learning Integration

   • Apply a simple ML model to your dataset
   • Make predictions based on your data
   • Compare different algorithms

2. Interactive Dashboard

   • Create an interactive visualization using Plotly
   • Add filters and controls for data exploration
   • Deploy using Google Colab's sharing features

3. Statistical Testing

   • Perform hypothesis testing on your findings
   • Calculate confidence intervals
   • Validate your conclusions statistically

:art: Presentation Excellence Challenges

1. Data Storytelling

   • Create a narrative arc for your presentation
   • Use analogies to explain complex findings
   • Design custom visualizations

2. Professional Polish

   • Create a one-page executive summary
   • Design an infographic of key findings
   • Record a professional video with editing

3. Real-World Application

   • Contact a relevant organization with your findings
   • Propose actionable recommendations
   • Create an implementation plan

:bulb: Excellence Recognition Students who complete extension challenges may receive special recognition or bonus points. Check with your instructor about opportunities for presenting exceptional work!

:emoji: AI Prompt

Dataset Selection and Cleaning

Code with AI: Try using AI to help with dataset selection and cleaning. Prompts:

• "Suggest appropriate datasets for analyzing [your topic], preferably in CSV format."
• "Help me identify potential data quality issues in this dataset: [paste first 10 rows of your data]."
• "Write code to clean this dataset by handling missing values, removing duplicates, and standardizing formats: [provide data description or sample]."
• "What columns from this dataset are most relevant for analyzing [your specific objective]?"

Report Writing and Data Visualization

Code with AI: Try using AI to assist with report writing and visualization design. Prompts:

• "Create a clear overview section for my analysis report on [topic] that includes background and objectives."
• "Help me interpret this visualization: [describe or share graph]. What key observations can I make?"
• "Suggest ways to present this finding more effectively: [describe your current result and visualization]."
• "Draft a conclusion section summarizing these results and suggesting next actions: [list your key findings]."
• "How can I properly cite this source in my report: [provide source details]?"

Presentation Development and Delivery

Code with AI: Try using AI to help create effective presentation content and improve delivery. Prompts:

• "Convert this paragraph into concise bullet points for my presentation slide: [paste paragraph]."
• "Suggest a clean, professional slide design for presenting data analysis on [your topic]."
• "What are the most important aspects of my analysis to highlight in a 5-minute presentation?"
• "Help me create a script for presenting this slide that sounds natural and engaging: [describe slide content]."
• "How can I effectively transition between these two sections of my presentation: [describe the sections]?"