The document discusses the similarities and differences between scientific writing and technical writing, highlighting that both are concise and effective but serve different audiences. Scientific writing is domain-specific, while technical writing aims for broader understanding. Additionally, it outlines the processes and benefits of data analysis within technical writing, emphasizing efficiency and accuracy.

1. Scientific Writing and Technical
Writing
Similatries
Differences

2. Similarities
Scientific writing and technical writing are quit
similar.
Scientific writing and technical writing share
some attributes like Conciseness, Flow,
Effectiveness.
Both are straightforward easy to understand.

3. Effective Writings.
Both Technical and Scientific writings are Effective
writing.
Effects on reader.
Main Purpose is to understand our reader why they
are reading this?
What they hope to gain from reading?

4. Conti.
They both use jargon, argot, code, special terms that
mean something to their own people.

5. Difference between Scientific and
Technical writing
Real difference is in there application.
 Scientific writing is expected to be kept within its
own field.
Technical writing is expected to be read and
understood by folks outside their own kind.

6. Conti.
Technical Science writing, is writing about science for
non-scientific audiences
Technical writing is all about documentation.
Technical writers write manuals. They write white
papers. They help write and edit journal papers

7. Characteristics of Scientific writing
Data in scientific writing is verifiable.
Calculated and precise.
Avoid padding.
Avoid verbosity.
Avoid pomposity.

8. Data Analysis in Technical writing
Analysis of data is a process of inspecting,
cleaning, transforming, and modeling data.
The results of data analysis are represented in the
form of graphs that are easy to understand.

9. Process of Data analysis
Data requirements
Data collection
Data processing
Data cleaning
Exploratory data analysis
Modeling and algorithms

10. Advantages of Data Analysis
 Quality and Compliance
Eliminate transcription errors Manual data entry and
manipulation results in data errors.
 Time
Reduced cycle time Reducing the time it takes to perform data
analysis is one of the most obvious benefits of automation.

11. Advantages of Data Analysis
Resources
Reduction in resources is also one of the most
obvious benefits of automation.
 Costs
Direct cost savings needs to be carefully considered.
Any cost savings need to be carefully weighed against the
cost in implementing, validating, and supporting the
solution.