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Top Salary backup can be some Data Science Courses
- Describe common Python functionality and features used for data science
- Explain distributions, sampling, and t-tests
- Query DataFrame structures for cleaning and processing
- Understand techniques such as lambdas and manipulating CSV files
- Use R to clean, analyze, and visualize data.
- Navigate the entire data science pipeline from data acquisition to publication.
- Use GitHub to manage data science projects.
- Perform regression analysis, least squares, and inference using regression models.
- A Specialization is a series of courses that help you master a skill. When you complete this course, your progress will count towards your learning in any of these Specializations:
- Introduction to Data Science
- IBM Data Science Professional Certificate
- Create and access a database instance on the cloud
- Write basic SQL statements: CREATE, DROP, SELECT, INSERT, UPDATE, DELETE
- Filter, sort, group results, use built-in functions, access multiple tables
- Access databases from Jupyter using Python and work with real-world datasets
- The major steps involved in tackling a data science problem.
- The major steps involved in practicing data science, from forming a concrete business or research problem to collecting and analyzing data, to building a model, and understanding the feedback after model deployment.
- How data scientists think!
- A Specialization is a series of courses that help you master a skill. When you complete this course, your progress will count towards your learning in any of these Specializations:
- Introduction to Data Science
- IBM Data Science Professional Certificate