  In recent years, data science has become an indispensable part of our
society. Over time, we have become reliant on this technology because of its
opportunity to gain value and new insights from data in any field - business,
socializing, research and society. At the same time, it raises questions about
how justified we are in placing our trust in these technologies. There is a
risk that such powers may lead to biased, inappropriate or unintended actions.
Therefore, ethical considerations which might occur as the result of data
science practices should be carefully considered and these potential problems
should be identified during the data science lifecycle and mitigated if
possible. However, a typical data scientist has not enough knowledge for
identifying these challenges and it is not always possible to include an ethics
expert during data science production. The aim of this study is to provide a
practical guideline to data scientists and increase their awareness. In this
work, we reviewed different sources of biases and grouped them under different
stages of the data science lifecycle. The work is still under progress. The aim
of early publishing is to collect community feedback and improve the curated
knowledge base for bias types and solutions.
