We are living in 2018, the year filled with news about Artificial Intelligence (AI) Robots, bots and innovative software’s and huge amount of data analysis that will solve many of unsolved problems of industry. It is evident that AI is going to play a major role in our lives. We see Job Portals having 40% jobs related to data sciences every day. Next big challenge is to fill the demand that this industry will create for Machine learning skill set. 2018 is very important for learning and mastering Data science domain.
Data science is an interdisciplinary field of scientific methods, processes, algorithms and systems to extract knowledge from data or insights from data and find hidden patterns from either structured and unstructured data While Machine Learning gives computer system the ability to “learn” with data, without being explicitly programmed.
Data science is the hottest topic for career path in 2018. According to LinkedIn’s 2017 U.S. Emerging Job Reports
Machine Learning Engineers and Data Scientists are Topes’ Jobs on LinkedIn. There are 13.5M times more machine learning engineers and data scientists working today than five years ago based on LinkedIn’s research, with 86109 open positions listed on the site today.
The Skills needed to become a data scientist is as follows:
This is most fundamental of a data scientist’s skill set
Use Statistical Knowledge to implement it.
Ability to analyze And applying logics of programming on large datasets that reaches millions of rows and many more
Build systems that organization can use to visualize data, creates frameworks to automatically analyze experiments, and managing the data at organization
Quantitative analysis is the heart of data scientist’s skills set.
Ability to perform different experiments to test different hypothesis.
Create prototypes to test assumptions, select and create features, and applying top down and bottom up approaches on data sets.
Domain Knowledge as a skill is ability to perform quantitative analysis on the system.
A data scientist who understands the product well can generate hypotheses about ways the system can behave if changed in a particular manner.
The traditional analytics skill set includes defining key primary and secondary metrics that the company can use to keep track of success at particular objectives
Results that are “incredible” are more often caused by bugs than actual “incredible” features of the system.
This skill is important to help significantly increase the leverage of all of the previous skills listed.
The important thing here is to communicate insights in a clear, concise, and valid way, so that others in the company can effectively act on those insights.
Sometimes there’s nothing more effective and satisfying than a good graph at making or conveying a point.
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