Because there are so many areas and data points that a data scientist must deal with on real-world projects, you need practice and experience to become an expert in Data Science. Each new project brings new learning outcomes that improve your data experience. Although a technical degree can help you get started in data science, you must continue to develop and maintain your data skills by working on many real-world projects. Furthermore, working on data science projects will boost your confidence and knowledge and showcase projects on your resume.
TechClass Data Science Capstone Online Course will give you a taste of what data scientists go through in real life when working on data. By completing this capstone project, you will get an opportunity to apply the knowledge and skills that you have gained throughout the data science program. It will test your skills in data visualization, organization, analysis, wrangling and preprocessing, model training, evaluation, and inference.
Table of contents
Chapter 1: Intro to Course
- 1.1. Welcome!
- 1.2. About TechClass Data Science Department
- 1.3. Our Approach in This Course
- 1.4. Your Expectations, Goals, and Knowledge
- 1.5. Abbreviations
- 1.6. Copyright Notice
Chapter 2: IBM Employee Attrition Prediction
- 2.1. About the Project
- 2.2. Dataset
- 2.3. Project
Chapter 3: Tiny ImageNet Image Classification
- 3.1. About the Project
- 3.2. Dataset
- 3.3. Project
Chapter 4: Netflix Hidden Gem Score Prediction
- 4.1. About the Project
- 4.2. Dataset
- 4.3. Project
Chapter 5: Finishing the Course
- 5.1. What We Have Learned
- 5.2. Where to Go Next?
- 5.3. Your Opinion Matters
- 5.4. Congrats! You did it!
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Very useful course containing real world dataset.
Real-world data sets and breaking a data science problem into small components in this course helped me, in addition to strengthening the skills I learned for data science, to move a data science project from zero to 100.
Well-designed projects. Determining what to do step by step helped me understand how to think in the face of a data science project and break it down into smaller parts.
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