Original article can be found here (source): Deep Learning on Medium
Importance of storytelling for Data Science profession
A story that explains a very significant aspect of an interview for data science or some other discipline or any project where you work for a company or stockholder on a project you’ve been working on or working on.
It is a great communication ability because a data scientist requires to communicate with the various stockholders and business knowledge, that a data scientist wants to interpret the data and observe the project life cycle of the data analysis project, and that there is a lot to explain to them about the different criteria.
The storytelling is an important role in interviews, because the interviewer asks, first of all, that you tell me about yourself, that means that the interviewer asked about your strength and domain knowledge and on which the project you worked, to be a data science enthusiast you should talk about from the start of your project and to the end, from where you collected the data, how did you analyze it, which Machine learning or Deep Learning model you used to build that project and what was the business problems what solution you made for it and how.
In narration, you need to demonstrate your better talents and how you
did the project by observing the life cycle of the data science project.
There are three main aspects of storytelling that you need to concentrate on:
Data is the most important thing about data science, as it is based on data just and if you have gathered data because you are an accomplished data scientist, did you think about the company data and its features, such as the numerical or categorical data and the scale of the data collection, so how many features there are, and from them what features you have used in your project, how you interpret the data set.
Before analyzing the data scientist cleans the data because billions of data are generated on a daily basis, so what methodology you used to clean up the data after extracting the data is how to delete the conflicting data, how many columns have zero meaning, how do you minimize the dataset and do some statistical analysis as if to figure out the inconsistency of the data with outliers and how they could affect your data.
Representation of the report uncovers the intuition behind the interpretation of the results, through the representation of the details and visuals of the relevant data. Simulation teaches you of a future that helps to solve the questions that need to be solved, with a simulation that will inform you how the dataset is colinear, how covariate, and how it fits the patterns in the model.
It’s really critical after the simulation of how you describe your plot.
The collision of images and perceptions is also a key element in pushing interviewers to change their decisions. Although the narrative tries to explain and inspires understanding, the graphics strengthen the tactile interaction that catches the imagination of the viewer. This mixture of narrative and image makes the viewer more emotionally sensitive to the post. However, your profile is also adequate to demonstrate how much more you have been committed to your work.
How you start your story?
1. Begin your story interactively enough that the interviewer’s curiosity in your story shows him how you’ve found issues with the data you’ve received, how you’ve tried to fix it, and what’s the outcome of the particular question and how you’ve taken business decisions.
2. You need to work further and define and reassure them about your enthusiasm for the project and reassure them about your opportunities and why you’re so enthusiastic about the job.
3. Be particular about your subjects, use only one-statement clearing for your research, diagram, description, and better vision to make the interviewer understand your views.
4. Explain more about your project about what model you used and why, for example, if you focused on numerical linear data in such a manner that you had to use Linear Regression and if you had categorical data, tell us about a classification process such as decision tree and random forest or which process you focused on.
5. Tell about the predictive and prescriptive analytics of the model, and which visualization and analytical tools you used.
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