5 RULES FOR BEING INVINCIBLE IN THE WORLD OF DATA SCIENCE

1. Don’t get too worked up about data discrepancies.

If you begin working on data, develop models, and prepare descriptive analysis assuming the data is clean, you can generate incorrect hypotheses. Instead, searching for discrepancies in data will reveal a plethora of important trends.  For eg, if a column has more than 50% of its values missing, analyst ‘A’ would consider dropping the column.  However, if the same mistake was found in a data collection instrument, detecting it would help the company improve. Discovering such errors will provide openings for questions that can lead to a larger picture.

2. Excellent visualizing and communication skills

Data science requires the ability to tell stories using data.  The company’s board of directors and stakeholders will expect statistics plans and insights from you, which they will only understand if you master effective visualization to show them your data’s tale and effective communication skills to share your thoughts. Finding interesting patterns and presenting them with mundane visualization would be ineffective if you spend a lot of time gathering, washing, exploring, and modelling data.

3. Concentrate on the    necessary metric

Remember that every business issue is unique and should be approached in a unique manner.  For example, if a client requests that you optimize for active users, you can use sound judgment and encourage him to optimize the percentage of active users instead in order to understand how the client’s product is doing.  It is important to have the right metrics in place before modelling a data science project in order to obtain reliable insights.

4. Don’t forget about the science in data science

Accept the scientific aspect of data as well as the technical aspect. According to Colin Melody, senior manager in data science at Deloitte, data scientists must remember the scientist aspect of their work. “At all times, you are searching for proof that supports an idea. This means that from start to finish, you must question your assumptions and data, test and retest, refine, and restart. There are numerous resources and technologies available for data scientists, and while it is not important to know how to use all of them, try to get a sense of what it would take to expand your toolbox.”

5. Don’t wait to “know enough”

While you’re learning all the principles needed to be a pro data scientist, learn when to apply what you’ve learned.  Every day, new and different developments are made in the field of data science.  There is a chance that you will not know anything, and waiting for it will accomplish little.  The wait for “know enough” is not a constant.  The term is too arbitrary to risk developing a successful project or applying for a position.  But, after you’ve laid the groundwork, get out  there and apply what you’ve  learned everywhere you can.