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.