Data Science: Can everybody pursue it?
Ways to revive your career in Data Science
- 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.
- 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
- Set theory, including Venn diagrams
- Properties of the real number line
- Interval notation and algebra with inequalities
- Uses for summation and Sigma notation
- Math on the Cartesian (x,y) plane, slope and distance formulas
- Graphing and describing functions and their inverses on the x-y plane,
- The concept of instantaneous rate of change and tangent lines to a curve
- Exponents, logarithms, and the natural log function.
- Probability theory, including Bayes' theorem.
- Become conversant in the field and understand your role as a leader.
- Recruit, assemble, evaluate, and develop a team with complementary skill sets and roles.
- Navigate the structure of the data science pipeline by understanding the goals of each stage and keeping your team on target throughout.
- Overcome the common challenges that frequently derail data science projects.