Data science workflow is a non-linear, iterative process, and that there are many skills and tools required to cover the full data science process

1. Hypothesis
Objective
Data
Acquisition
Exploration
Cleaning
Analysis
Modeling
Visualization
Results
Deployment
Strategy
Who is your
client?
What is the
problem you
are trying to
solve?
Collect the raw
data needed to
solve the
problem i.e.
- databases
- crawling
- streams
- binaries
- APIs
- and so on
Clean the data
to convert it to a
form that you
can further
analyze i.e.
- parsing
- cleaning
- converting
- transforming
- and so on
Perform in-
depth analysis
i.e.
- descriptive
- explorative
- machine
learning
- statistical
models
- algorithms
- and so on
Communicate
results of the
analysis i.e.
- visualizations
- storytelling
- interactive
plots
- dashboards
- data apps
- usability
- and so on
Transform your
results into a
data product
i.e.
- data
engineering
- architecture
- usage
patterns
- security
- and so on
Domain-Know
Data Science