“How do I get to acquire a job as a data scientist if I have no prior experience as a data scientist?” is the most-asked question across the world. Data-Driven Development has been in vogue for almost a decade. It is high time to have a look at the wider scope of what Data Science actually is and what all is necessary to get started on the path of becoming a Data Scientist. Data scientists are actually termed as “wranglers of big data”. They handle large amounts of data and employ their skills in efficiently organizing it. Then they implement all their analytic powers to reveal the hidden solutions to challenges faced by business.
If you can pull data, gather data, scrape data, or scale data through an application and create a data pipeline from those data sources you’ll be a highly valued commodity and you’ll be the ‘neck’ of any Data Scientist team. The guy who understands the data, the application, and the business problems will end up being the valued commodity no matter his title. However, there is a most common misconception that many people possess is that Data Science aspirants can learn the subject by self-teaching. But, it has proven to be completely wrong as the knowledge on statistics and scientific methods can be obtained only through thorough Data Science Training .
What Is a Data Scientist, Anyway?
Any Data Science team helps enterprises analyze their data to make predictions about their business. However, the path to becoming a Data Scientist is definitely not a clear one. The team includes:
These people from different backgrounds collaborate around a problem in an effective manner.
Data science isn’t just about being skilled with numbers. Rather, an effective data scientist also has an ability to see how particular subsets of data may be more useful than others, and what conclusions can be drawn from them.
A data scientist is required to perform the below operations, at any time:
• Perform undirected analysis and structure certain open-ended industry queries.
• Obtain enormous information through varied internal and external references.
• Engage advanced analytics, machine learning and statistical enquiries to assemble data for usage in prescriptive and predictive modeling.
• Efficiently purifies the data to remove information that is not relevant.
• Consider data through numerous angles to regulat edrawbacks, trends and other opportunities
• Formulate data-driven solutions
• Explore latest algorithms to aid solutions for the relevant issues and construct new tools.
• Interface predictions to management and other IT departments via effective visualizations and reports.
• Advise cost-effective changes to current methodologies and strategies.
• Machine learning methodologies
• IT skills
• Data mining, cleaning
• Data visualization and reporting
• R, SAS and querying languages
• SQL databases
• Python, C/C++ Java, Hadoop, Perl,Hive & Pig
• Amazon S3 Cloud
Different Roles of Data Scientist in the Industry
Meeting high-level challenges and employing right methodologies to get the maximum out of human resources
• Effective Communication : Exploring the techniques to both technical and non-technical audiences in an easyand understandable language.
• Intellectual Curiosity: Driving latest territories and finding the most creative and unusual ways to evolve at a solution for a query defined.
• Industry Knowledge: Comprehending the functions of an industry and acquire the analyzed and utilized data.
Good data scientists will not just address the typical business problems, but instead,they pick the right problems that carry most value to the enterprise.
As the business strategies are evolving gradually, the Data Scientist roles are carving their definite responsibilities with certain expectations. Because of the rareness and ever-growing demand for the interdisciplinary talent, Harvard has labeled Data Scientist is labeled as the sexiest job of the 21st Century. As there is huge demand for Data Scientists it is better for non-IT people to get Data Science Training and land in decent salary.
If you want to get experience doing data science, do data science.Distinguish a worthy query that can be replied with data sets, and formulate an approach to derive at a solution. Once you reach the necessary point, then start messing around with tools and structuring the external-facing parts of the desired project.
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