Partial book profile
Data Science Bookcamp
Leonard Apeltsin
An aspiring data scientist learns essential skills in probability, statistics, and machine learning by completing five hands-on Python projects, transforming their coding ability into a high-paying career.
The full book profile is not available yet.
What it’s about
Data Science Bookcamp is a practical, project-based guide for Python coders aspiring to enter the data science field. Rejecting dense mathematical theory, the book champions a code-first approach, teaching fundamental concepts of probability, statistics, and machine learning through intuitive, real-world examples. Readers are guided through five comprehensive case studies—from analyzing card games to detecting social circles on Facebook—mastering key libraries like NumPy, Pandas, and Scikit-Learn along the way. This hands-on 'bookcamp' methodology is designed to build not just technical skills but also the crucial open-ended problem-solving abilities that employers demand, culminating in a robust project portfolio that will help you land your first high-paying data science job.
The through-line
- Who it’s for
- A Python coder who wants to transition into a high-paying data science career but feels intimidated by the complex math and lacks a clear path to gain the practical, project-based experience employers seek.
- The problem
- Lacking a portfolio of real-world data science projects and the specific skills in statistics, machine learning, and key Python libraries needed to get hired. Feeling overwhelmed by the vastness of the data science field, uncertain about where to start, and intimidated by the perceived need for a heavy math background.
- The plan
- Tackle five distinct, real-world case studies, starting with fundamental concepts and progressively building to more advanced techniques.
- Learn the necessary theory and Python library skills (NumPy, Pandas, Scikit-Learn, etc.) in the context of each project.
- Attempt to solve each case study independently to build true problem-solving muscle before comparing your solution to the one provided.
- The payoff
- Possessing a strong portfolio of five impressive data science projects. · Gaining the confidence and competence to solve open-ended, real-world problems. · Successfully landing a high-paying job in the data science field.
See our guide
Related profiles we’ve built
- A Practical Guide To Conjoint Analysis →
- Analytics at Work: Smarter Decisions, Better Results →
- Big Data: A Revolution That Will Transform How We Live, Work, and Think →
- Big Data_ A Very Short Introduction (Very Short Introductions) →
- Business Adventures →
- Business Intelligence Guidebook: From Data Integration to Analytics →
- Data Mining for Business Analytics: Concepts, Techniques, and Applications →
- Data Warehouse and Data Mining →
Featured in these guides
- Practice Business Intelligence & Data Science →Cited source
Co-cited books
- Alteryx Designer The Definitive Guide (Fifth Early Release) · Joshua Burkhow
- Analytics at work smarter decisions, better results · Davenport, Thomas H., 1954-, Harris etc.
- Operations Research for Social Good A Practitioners Introduction Using SAS® and Python
- Business Intelligence and Big Data Business Intelligence and Big Data Drivers of Organizational Success
- Data Analytics for Business Lessons for Sales, Marketing, and Strategy
- Predictive Business Analytics
- Cultural Analytics
- Competing on analytics the new science of winning · Davenport, Thomas H., 1954- author, Harris etc.