...

Full Bio

Artificial Intelligence, Neural Networks, and Smart Computers. How do they serve us?

today

How about knowing Data Security In Cloud Computing?

today

Must watch some Deep Learning Tutorials on Youtube

today

Pursuing Data Science Course is a safe option for youth's career

yesterday

Factors to Be Considered Before Joining Big Data Analytics Courses

yesterday

Every Programmer should strive for reading these 5 books

545058 views

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

196827 views

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

143547 views

Highest Paid Programming Languages With Highest Market Demand

125700 views

Python Programming Language can easily be acquired easily. How?

113994 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.