Statistical Analysis Guide for Data Scientists

Statistical analysis is considered one of the modern tools used in engineering projects. In 1955 AD, the scientist Henry Gantt was able to invent the method of straight lines, sometimes called the bar chart, which is still used to for engineering projects to this day. This method shows the implementation plan, the time schedule, and follow-up of work progress. The project is divided into work items or activities and a thick line is used to represent each item. This method is considered one of the simplest methods of planning projects.

In 1956, the Engineering Services Department of the American company Dupont, with the help of a number of computer specialists at Remington Rand, established the theoretical and practical foundations for the Critical Path Method (CPM). In 1961, John Fondal proposed a solution. An alternative to the critical assistant method is the Precedence Diagramming system. According to this method, the activity or process is clarified within the node, and then it is possible to dispense with the use of dummy activity. Also, the process of modification and correction in this. The method is considered easier than the critical path method, but if a computer is used, the sequential precedence system requires a larger place than the critical path method to store information.

Best Statistical Analysis Tips for Data Scientists

1- Study

You should start studying the basics of graphic analysis and statistics through available books, articles, and educational courses.

2- Use the right tools

You must use the right tools for graphical analysis and statistics, including programs such as Excel, SPSS, and R. And for complicated statistics assignments, you should get statistics assignment help.

3- Practical training

Practical training must be undertaken by solving a variety of analytical and statistical problems.

4- Communicate with experts

You should communicate with experts in the field and ask questions and inquiries about difficult or complex topics.

5- Self-analysis

You should do self-analysis of data and statistical analysis of your data, in order to improve your graphical analysis and statistics skills.

6- Practice

Continuous practice must be done by generating and analyzing data on a regular basis, and applying statistics to solve real problems.

7- Innovation

There must be innovation and renewal in the use of graphical analysis and statistics, and experimentation with new and advanced methods.

Conclusion

In general, your skills in the art of graphic analysis and statistics can be improved through study, use of the right tools, hands-on training, communication with experts, self-analysis, continuous practice, and innovation.

You must work hard and be dedicated to improving your skills in this field for success.