...

Full Bio

Today's Technology-Data Science

256 days ago

How to build effective machine learning models?

256 days ago

Why Robotic Process Automation Is Good For Your Business?

256 days ago

IoT-Advantages, Disadvantages, and Future

257 days ago

Look Artificial Intelligence from a career perspective

257 days ago

Every Programmer should strive for reading these 5 books

578409 views

Why you should not become a Programmer or not learn Programming Language?

236877 views

See the Salaries if you are willing to get a Job in Programming Languages without a degree?

151785 views

Highest Paid Programming Languages With Highest Market Demand

136632 views

Have a look of some Top Programming Languages used in PubG

132003 views

### 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.