ETEAM Blog 6 Common Data Science Fallacies You Should Avoid

There’s a huge hidden gap between what data scientists and non-data scientists think about data science. This gap stems from the fact that most people believe that the data science discipline is all about analyzing data that is readily available and consistent.

The truth of the matter is that data science is a technical field. It goes beyond what employers and practitioners in other fields think.


What Data Science Entails

Being a data scientist requires one to collaborate with people from different career backgrounds to accomplish real-world data science tasks. It may not be possible for one person to have all the skills necessary to execute a data science project successfully. Note that the work of data scientists entails using algorithms and analytics programs to correctly extract meaning from datasets. These experts also use high-performance computers to achieve this goal.

The actual job of a data scientists is to take data (of all sizes) from various sources and interpret it for their respective employers or clients. These experts are knowledgeable on how to come up with new data analysis methodologies and use existing ones to their advantage. They can also apply their extensive research skills to draw meaningful analyses from certain datasets.



Common Fallacies About Data Science

One huge task in data science entails spending lots of time in finding sound datasets and formulating suitable scientific questions. Data scientists usually take a lot of time to make sure that they have the right data at hand for them to generate good results from their algorithms. It is sad to note that most people perceive data science as an activity that involves 10 percent data extracting and cleaning and 90 percent modeling. Explained below are other fallacies that people have regarding data science.

The Datasets are Accessible and are Always Up-to-date

As a budding data scientist, it is not wise to think that you will easily get access to relevant datasets. This process takes time, skill and patience. To be on the safe side, it is important to go through all the data that is accessible for you to ensure that it makes sense and it is up to date. You should also note that it is quite impossible to draw meaningful conclusions from a dataset you haven’t taken time to evaluate.

The Data and Analytics Outputs are Intuitively Understandable

It is not easy to understand a dataset with poorly-named or missing header fields, missing lookup tables and truncated text fields. For it to be understandable, the data needs to have a well-documented description. It is also fallacious for non-experts and budding data scientists to think that the analytics outputs are understandable and easy to share. The truth is that one needs some skill in data analytics to evaluate the analytics outputs.

The Datasets are Consistent

It is the joy of every data scientist to find a dataset in well-defined, self-consistent and nicely-structured format. The harsh truth is that one can only find those datasets if a data engineer or data scientist designed them. This means that it is up to the data scientist to work on the datasets for them to have meaning.

Goals of Data Science Projects are Always Achievable

With the right and adequate processing tools, a data science project may be achievable. However, people should stop assuming that all data science projects are achievable. They should note that these projects may take time to complete or be impossible to complete if errors exist in a dataset Furthermore, the experience levels of a data scientist also determine whether the project will reach its implementation stage or not. Factors such as lack of financial resources may also render it difficult to get adequate processing tools to handle a particular data science project.

Encryption isn’t Important in Data Science

Data scientists feel relieved when they have completed a data analysis activity. Problems arise when the employer or client asks them to share the analysis in plain-text to a given email address. In this context, the employer or client fails to realize that the analysis should have encryption to protect it against unauthorized access. People should note that technical tasks that involve the analysis of data are prone to various security threats. To curb threats such as information leaking or loss, data scientists must encrypt their work.

It is Easy to Re-execute Data Analyses

When giving out tasks to data scientists, employers or clients should always remember that it takes time for an analysis to be complete. Furthermore, it also takes time to re-run an analysis project. It is high time for these individuals to stop harassing or giving data scientists unrealistic deadlines for the assigned tasks. A typical data analysis process starts with the definition of questions and then the definition of measurement priorities. Other procedures involved in the process include the data collection, data analysis and interpretation of results.


Final Thoughts

For those venturing into the data science field, you will discover that the job involves a lot of coding. You should always be in a position to put your knowledge into practice when pursuing this discipline. The misconception most people have towards this field is that they don’t have to be good in math for them to be data scientists. They should note that it is impossible to expect instant results from this field since it is highly competitive in the global business sector. It is also important for people to stop basing their views on data science using the fallacies explained above.

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