What will you learn in this Applied Data Science Capstone Course
Apply the complete data science methodology to a real-world project, encompassing data collection, wrangling, exploration, modeling, and evaluation.
Utilize Python libraries such as Pandas, Matplotlib, Seaborn, and Scikit-learn for data analysis and machine learning tasks.
Access and extract data using APIs and web scraping techniques with tools like BeautifulSoup.
Develop and compare classification models, including Support Vector Machines, Decision Trees, and K-Nearest Neighbors.
Create interactive visualizations and dashboards using libraries like Folium and Plotly Dash.
Program Overview
1. Introduction and Data Collection
⏳ 1 week
Understand the project’s context and objectives. Learn about different data sources, including APIs and web scraping, to gather relevant data.
2. Data Wrangling and Exploration
⏳ 1 week
Clean and preprocess the collected data. Perform exploratory data analysis to uncover patterns and insights using statistical methods and visualizations.
3. Data Visualization and Dashboarding
⏳ 1 week
Create informative visualizations to communicate findings effectively. Develop interactive dashboards to present data insights dynamically.
4. Machine Learning and Model Evaluation
⏳ 1 week
Build and train classification models to predict outcomes. Evaluate model performance using appropriate metrics and refine models for better accuracy.
5. Final Report and Presentation
⏳ 1 week
Compile the entire project into a comprehensive report. Present findings, methodologies, and conclusions in a format suitable for stakeholders.
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Job Outlook
Equips learners with practical experience in handling real-world data science projects, enhancing employability in roles such as Data Scientist, Data Analyst, and Machine Learning Engineer.
Applicable across various industries, including technology, finance, healthcare, and aerospace, where data-driven decision-making is crucial.
Demonstrates proficiency in end-to-end data science workflows, a valuable asset for career advancement.
Explore More Learning Paths
Enhance your data science expertise with these related courses and resources. These learning paths will help you master tools, methodologies, and executive-level data science applications.
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Tools for Data Science
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Explore advanced concepts and leadership-focused applications in data science for business impact.
Related Reading
What Is Data Management
Discover best practices for organizing, storing, and maintaining data to drive effective analytics and decision-making.
Specification: Applied Data Science Capstone Course
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FAQs
- Requires prior knowledge of Python programming.
- Familiarity with machine learning and data analysis concepts is recommended.
- Prior exposure to libraries like pandas, scikit-learn, and matplotlib helps.
- Beginners may struggle without foundational coursework.
- Hands-on labs reinforce applied skills for those with prior knowledge.
- Apply data science methodology to a full project lifecycle.
- Work with APIs, web scraping, and multiple data sources.
- Build and evaluate classification models (SVM, Decision Trees, KNN).
- Create interactive dashboards using Plotly Dash and Folium.
- Develop a final report suitable for stakeholders and portfolio presentation.
- Self-paced learning allows flexible scheduling.
- Estimated total duration is about 5 weeks for project completion.
- Hands-on exercises and modeling can be done incrementally.
- Lifetime access allows reviewing content at any time.
- Suitable for professionals seeking practical portfolio projects.
- Prepares for roles like Data Scientist, Data Analyst, and Machine Learning Engineer.
- Skills applicable in technology, finance, healthcare, and aerospace.
- Enhances employability with practical, end-to-end project experience.
- Demonstrates ability to apply theoretical knowledge to real-world problems.
- Supports advancement in data-driven and analytics-focused careers.
- Certificate of completion provided upon finishing the capstone.
- Shareable on LinkedIn and professional networks.
- Demonstrates applied data science skills with real-world projects.
- Enhances credibility for job applications in analytics and machine learning.
- Valuable for portfolio building and career advancement.
