Team Project pt.2

instructional_team/generate_slides.sh

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 # CONFIGURATION
 folder_md="./markdown_slides"
-folder_output="../slides/" # This will be used for both PDF and HTML
+folder_output="../slides" # This will be used for both PDF and HTML
 
 # Clear the screen for the splash screen
 clear

instructional_team/markdown_slides/04_project_showcase.md

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+---
+marp: true
+theme: dsi-certificates-theme
+_class: invert
+paginate: true
+---
+
+# Introduction to Data Science Portfolios
+
+```
+$ echo "Data Sciences Institute"
+```
+
+
+<!-- Why a data science portfolio is crucial for your career. Overview of portfolio components. -->
+
+<!-- Speaker notes: A portfolio is essential for showcasing your real-world skills and experiences, making you stand out to potential employers. It provides concrete examples of your ability to solve problems and generate insights from data, which is crucial in the competitive field of data science. By effectively presenting your projects, you demonstrate not just technical proficiency but also your capability to communicate complex ideas clearly. -->
+
+---
+
+# Module Overview
+
+The content in this module is a response to the observations made during Team Project 1. There is no specific structure to the slides, as they present a collection of important points based on the feedback and common issues identified.
+
+---
+
+
+# What is a Good Portfolio?
+
+A good portfolio showcases your best work and highlights skills that are highly valued by employers in data science and machine learning. It demonstrates your ability to handle real-world tasks, making you an attractive candidate for employment.
+
+**Personalize Your Portfolio:**
+- **Remove generic content:** For instance, delete the default README we provided. It's designed to describe the module, not your unique project.
+- **Highlight your unique contributions and skills:** This personalization shows employers that you're not just completing assignments but are engaged and innovating on your projects.
+
+---
+
+
+# Types of Projects to Include in Your Portfolio
+
+Including a diverse range of projects in your portfolio can significantly enhance your appeal to potential employers. Consider including these key project types to demonstrate your breadth and depth in data science:
+
+1. **Data Cleaning Project:** Show your ability to prepare data for analysis.
+2. **Data Storytelling and Visualization Project:** Highlight your skills in interpreting and presenting data in compelling ways.
+3. **ML Modeling:** Demonstrate your proficiency in building and tuning models.
+4. **Group Project:** Showcase your teamwork and collaboration skills. (already doing this! 🥳)
+
+For more, read "[How to Create a Project Portfolio for Data Science Job Applications](https://www.dataquest.io/blog/career-guide-data-science-projects-portfolio/)"
+
+
+---
+
+# Selecting Projects That Showcase Your Skills
+
+Choosing the right projects for your portfolio is crucial. Each project should:
+
+- **Solve Real Problems**: Use actual datasets to address genuine issues in your field.
+- **Demonstrate Industry Relevance**: Select projects that are pertinent to your specific area, such as marketing analytics for marketers or predictive maintenance for engineers.
+- **Provide Actionable Insights**: Focus on projects that deliver clear, practical outcomes that demonstrate your ability to impact real-world scenarios.
+
+These criteria ensure that your projects not only highlight your technical skills but also your understanding of and adaptability to industry-specific challenges, making you a valuable candidate to potential employers.
+
+---
+
+# Showcase Projects That Use Diverse Data Types
+
+Enhance your projects by effectively using a mix of structured, unstructured, and time series data:
+
+- **Structured Data**: Employ database data for clear, quantifiable insights.
+- **Unstructured Data**: Add depth with text, images, or videos.
+- **Time Series Data**: Utilize data in sequential order for trend analysis and forecasting.
+
+Select data types strategically to align with your project's objectives. Ensure each type contributes to a clear and coherent narrative, avoiding unnecessary complexity to maintain focus and utility.
+
+---
+
+# Demonstrating Technical and Non-Technical Skills
+
+- **Demonstrate Coding Proficiency**: Include projects that showcase your ability to write clean, efficient, and well-documented code.
+- **Showcase Communication Skills**: Feature projects where you’ve effectively communicated complex data-driven insights to non-technical stakeholders.
+
+Your ability to document code clearly and communicate effectively can drastically reduce onboarding times for new team members and help non-technical stakeholders make informed decisions, thereby enhancing overall project success and team efficiency.
+
+---
+
+
+# Crafting a Comprehensive Main README File
+
+- **Purpose & Overview:** Introduce the project with essential details and a concise description.
+- **Goals & Objectives:** Articulate what the project aims to achieve.
+- **Techniques & Technologies:** Highlight the tools and methods used.
+- **Key Findings & Instructions:** Summarize outcomes and provide setup instructions.
+- **Visuals & Credits:** Enhance with visuals; acknowledge contributors.
+
+<!-- 
+Focus on the essence of what makes a README effective: clarity and conciseness. A well-organized README provides a snapshot of the project, helping others quickly understand its value and how to engage with it. Ensure that each element is presented in a way that contributes to an overall understanding of the project.
+-->
+
+---
+
+
+# Better Documentation - README
+
+While **you can have multiple README files** in your repository, adhere to the rule of maintaining just one README file per folder to keep each section clear and organized. This structure not only aids in navigation but also showcases your ability to manage and present complex information effectively, increasing your appeal to potential employers.
+
+
+---
+
+# Better Documentation - Comments
+
+**Effective commenting enhances code readability and maintainability, crucial for collaborative environments.**
+
+A lot of participants had code that wasn't properly commented. The idea here is that if someone with no tech background reads your code, they should get the essence of what you're doing and how the code flows. This practice not only aids in understanding but also facilitates smoother transitions and updates within team projects.
+
+📰 [Best practices for writing code comments](https://stackoverflow.blog/2021/12/23/best-practices-for-writing-code-comments/)
+
+---
+## Example
+
+❌
+```
+function calculateTotal(price, quantity) {
+    return price * quantity;
+}
+
+let totalPrice = calculateTotal(25, 5);
+console.log(totalPrice); // Output: 125
+```
+
+✅
+```
+// Calculates the total cost by multiplying the price per item with the quantity
+function calculateTotal(price, quantity) {
+    return price * quantity;
+}
+
+// Example usage: Calculates the total price for 5 items at $25 each by multiplying the price
+// per item ($25) with the quantity (5), and stores the result in the totalPrice variable.
+let totalPrice = calculateTotal(25, 5);
+console.log(totalPrice); // Output: 125
+```
+
+---
+
+
+# Better Documentation - STAR Method
+
+- The STAR method is typically used for answering interview questions, applying this structure to your project overviews can be highly effective. 
+- By organizing your projects using the Situation, Task, Action, Result format, your portfolio essentially speaks for you, conducting a virtual interview with potential employers even in your absence. 
+- This approach ensures that employers can clearly understand the value and impact of your work, even without direct interaction.
+
+---
+
+# Effective GitHub Repository Organization
+
+- Ensure your GitHub repository is neatly organized; avoid unused or empty folders and ensure each folder has a clear purpose.
+- Use READMEs in key folders (like `src`, `iac`, `backend`, `frontend`, etc.) to detail their contents and purpose, aiding clarity for complex sections.
+- Avoid READMEs in simple folders (like `images`, `docs`, etc.), unless there's specific information that needs to be explained.
+
+---
+
+# Effective Presentation of Findings
+
+- Focus on crafting your project presentations to be clear and to the point. Utilize storytelling techniques to make your data findings compelling and employ visualizations to simplify and clarify complex information. 
+
+- Your ability to communicate insights effectively is **crucial** — well-presented findings can make your work stand out to potential employers and demonstrate your capability to aid decision-makers like managers or CEOs in understanding critical data quickly. This approach not only showcases your skills but significantly enhances your employability.
+
+---
+
+# Questions?

instructional_team/markdown_slides/05_project_planning_and_impact.md

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+---
+marp: true
+theme: dsi-certificates-theme
+_class: invert
+paginate: true
+---
+
+# Effective Project Planning and Time Management
+
+```
+$ echo "Data Sciences Institute"
+```
+
+<!-- Importance of planning in data science projects, overview of planning phases, and key considerations. -->
+
+<!-- Speaker notes: Emphasize the necessity of structured planning for successful project outcomes. -->
+
+---
+
+# Module Overview
+
+This is a continuation of our reaction to what we observed in Team Project 1. The slides present key points and recommendations rather than following a specific order. Each slide addresses different aspects that need improvement, and they do not necessarily build on each other.
+
+---
+
+# Strategic Planning for Short and Long-Term Goals
+
+Setting realistic time frames for your project stages is crucial. Use S.M.A.R.T goals to structure your planning effectively:
+
+- **Specific**: Define clear details about what needs to be achieved, who's involved, and where it will occur.
+- **Measurable**: Establish concrete criteria for measuring progress and success.
+- **Achievable**: Ensure the goals are realistic, considering available resources and constraints.
+- **Relevant**: Align goals with broader business objectives to ensure they are meaningful.
+- **Time-bound**: Set definitive deadlines to maintain focus and urgency.
+
+<!-- Speaker notes: Discuss the importance of using the SMART framework to guide project planning. Emphasize how each element contributes to setting effective goals that are not only clear and actionable but also aligned with strategic business outcomes. -->
+
+---
+
+### Example: Customer Retention Model
+
+**Scenario**: Develop a predictive model to improve customer retention for a subscription-based service.
+
+- **Specific**: Machine learning model to predict customer churn using six months of user activity and demographic data.
+- **Measurable**: Target 80% accuracy on a validation set.
+- **Achievable**: Use Python’s Scikit-Learn and a marketing department dataset.
+- **Relevant**: Aligns with the company’s goal of reducing churn and improving retention.
+- **Time-bound**: Initial model version in three months, with testing and deployment in the next quarter.
+
+---
+
+### Example: Dataset Preparation
+
+**Scenario**: Prepare a dataset for urgent customer feedback analysis.
+
+- **Specific**: Clean feedback data from the latest product launch, removing duplicates and handling missing values.
+- **Measurable**: No missing values or duplicates, verified by data quality checks.
+- **Achievable**: Use Python’s Pandas library for data manipulation.
+- **Relevant**: Crucial for immediate analysis to inform the marketing team’s next steps.
+- **Time-bound**: Complete in two days to start analysis on schedule.
+---
+
+# Leveraging Project Management Methodologies
+
+Explore methodologies like Scrum, Kanban, Waterfall, and Agile on your own. For now, let's focus on an essential concept: **spikes**.
+
+**Spikes** are short, time-boxed research or exploration tasks designed to answer specific questions or solve particular problems before starting the main project work.
+
+### Benefits of Spikes:
+
+- **Clarify Requirements**: Quickly gather necessary information.
+- **Solve Problems Early**: Address technical uncertainties before main development.
+- **Prevent Delays**: Avoid excessive brainstorming that can stall progress.
+
+<!-- Speaker notes: Instructor Phil noted that during Team Project 1, students often stalled for two days due to extensive brainstorming on data handling. We're discussing spikes today because they prevent such delays by providing a structured approach to gather information and clarify project scopes efficiently. It's about time-boxing the analysis and thinking time to maximize productivity. -->
+
+---
+
+
+# Demonstrating the Business Impact of Your Projects
+
+Understanding and articulating the business value of your data science and machine learning projects is crucial for two main reasons:
+
+### Benefits:
+
+- **Educational**: Apply new technologies and methodologies from both data science and machine learning.
+- **Employability**: Showcase your ability to solve real-world problems, making you attractive to potential employers.
+
+Highlight your skills through projects by using relevant industry examples and metrics, such as operational improvements, technical innovations, or strategic insights.
+
+<!-- Speaker notes: Emphasize aligning data science and machine learning projects with business goals. Highlight that demonstrating business impact is about tackling complex problems and delivering meaningful insights. This supports your professional development and attractiveness to employers. Provide examples of techniques and frameworks used to showcase hands-on experience and technical proficiency. -->
+
+---
+
+# Examples Across Sectors (Part 1)
+
+### Healthcare Example
+"Improved patient outcomes by developing a predictive model using Python and scikit-learn. Data was preprocessed using pandas to handle missing values and standardize features. Logistic regression was selected through cross-validation due to its interpretability and performance."
+
+### Retail Example
+"Optimized inventory management with SQL and Tableau. Data was cleaned and aggregated using SQL queries to identify sales patterns. Visualizations in Tableau helped to forecast demand, leading to cost savings."
+
+*⏭️ Continued on next slide...*
+
+---
+
+# Examples Across Sectors (Part 2)
+
+### Marketing Example
+Increased customer engagement by performing customer segmentation using k-means clustering with Python. Feature engineering was conducted to create meaningful segments based on purchase history and demographics. The optimal number of clusters was determined using the elbow method, which helps identify the point where adding more clusters does not significantly improve the model.
+
+---
+
+# How to Effectively Communicate Business Impact
+
+Showcase your project’s business value by applying specialized skills:
+
+- **📊 Data Science Stream**: Apply the techniques learned in your sampling and visualization modules to deliver impactful data insights.
+- **⚙️ Machine Learning Stream**: Demonstrate model efficiency and algorithm optimization with detailed performance graphs and clear pipeline visualizations.
+
+Ensure your presentations translate complex data into clear, actionable business insights.
+
+<!-- Speaker notes: Encourage Data Science stream participants to apply their sampling and visualization techniques effectively. Machine Learning stream participants should focus on illustrating model efficiency and optimization. Emphasize that both groups need to communicate how their projects provide tangible business value to potential employers. -->
+
+---
+
+# Making Complex Concepts Accessible
+
+Communicating complex data science concepts clearly is essential for audiences without technical backgrounds, **including recruiters and HR teams.**
+
+Strategies to Enhance Understandability:
+- **Use Analogies and Metaphors**: Bridge understanding gaps with relatable comparisons.
+- **Incremental Explanation**: Simplify concepts step-by-step.
+- **Visual Aids**: Employ diagrams and infographics for clarity.
+
+Clear communication ensures your projects are comprehensible to all, enhancing your professional appeal and broadening project impact.
+
+<!-- Speaker notes: Emphasize the need to make data science projects accessible to non-experts, including those in HR and recruitment. Discuss using analogies, incremental explanations, and visual aids to clarify complex ideas. This approach not only makes your work more impactful but also supports your candidacy during job evaluations. -->
+
+---
+
+# Next Steps Post-Certification
+
+As you transition from the certificate program, continue to build and showcase your skills.
+
+### Strategies for Continuous Growth:
+
+- **Active Portfolio**: Aim for 5-6 substantial projects to demonstrate a broad range of skills. Focus on quality over quantity.
+- **Explore and Learn**: Continue exploring new areas within data science and machine learning to keep your knowledge current and dynamic.
+- **Peer Collaboration**: Participate in group projects with fellow participants to enhance your skills and widen your professional network.
+
+Actively applying for jobs should proceed in tandem with these activities.
+
+<!-- Speaker notes: Emphasize that maintaining a portfolio with 5-6 diverse projects is ideal for demonstrating a commitment to the field and depth of skills. Clarify that the focus should be on the quality of projects rather than simply meeting a numerical target. Encourage participants to continuously apply for jobs, using their portfolios to strengthen their applications and showcase their ongoing engagement and expertise. Remind them that even after the modules end, they still have access to the Slack community (more information will come out later). This platform can be a valuable resource for finding project partners, asking questions, and getting feedback on their work. Additionally, advise them to present completed projects on LinkedIn. This not only timestamps their active engagement in relevant projects during job searches but also enhances their visibility and personal branding on a professional network. -->
+
+---
+
+# Questions and Answers