The ever increasing computational capacity has enabled us to acquire, process and analyse larger data-sets and information. We increasingly want to take a data-driven lens to solve business problems. But business problems are inherently ‘wicked in nature’ - with multiple stakeholder, different problem definition, different solutions interdependence, constraints, amplifying loops etc. There is no one trick to solve them. What is required is learning a structured approach to problem solving that can be applied to large set of these problems. One possible way is to use a *Hypotheses Driven Approach* - problems definition, scoping, issue identification and hypothesis generation - as a starting point for this. In this workshop, you will learn how to apply this hypotheses driven approach through seven pragmatic steps - Frame, Acquire, Refine, Transform, Explore, Model, and Insight - to any business problem. The focus will be to learn the principles through an applied case study and by actually coding in R or Python or JavaScript to solve this.

## Objective

- Imbibe the underlying principles of data analytics and learn how to use the data science pipeline
- Develop proficiency in using R or python data stack and libraries like ggplot, dplyr, stats (for R) and pandas, scikit-learn (for python)
- Learn how to employ statistical and machine learning algorithms to solve real life problems

## Approach

- Taught by real life practicioners
- Tested and practical curriculum with real data sets
- Interactive and live coding sessions

## Target Audicence

- Professionals interested in learning data science
- Programmers interested in building data driven products
- Journalist, scientist, researchers interested in telling data stories
- Business Intelligence analysts and consultants

The workshop is ideal for anyone who wants to learn how to use open source software - **R** or **Python** stack for statistical analysis and visualization. If you are not using R or Python for statistical analysis, then existing familiarity with data analysis in some other tool would help. There is no pre-requisite requirement to be familiar with the R or Python libraries mentioned above.

## Software Requirements

For doing the exercise during the workshop, we would be using R and R IDE - R Studio or Anaconda Distribution for Python. Please install the same in your machine prior to the workshop session. For attendees more curious, we will be using Rmarkdown or Jupyter Notebook as our IDE. Some of the main libraries we will using in the session are:

- For R:
`dplyr`

,`tidyr`

,`ggplot`

,`ggmap`

,`plotly`

,`rmarkdown`

,`purr`

,`prophet`

and`forcats`

- For python:
`numpy`

,`plotnine`

,`seaborn`

,`matplotlib`

,`prophet`

and`scikit-learn`

.

The working repo for this workshop is at https://github.com/amitkaps/art-data-science