Python for Data Science and Data Analysis

Python-for-Data-Science

Once you’re done, you’ll have a better idea as to why you should choose Python for data
analysis. Looking forward to a career as a data Scientist? Check out the Data Science with
Python Training Course and get certified today. Data Analysis: An Overview What does a data
analyst do, anyway? A little refresher on the role of a data analyst may help make it easier to
answer the question about why Python’s a good fit. The better you understand a job, the
better choices you will make in the tools needed to do the job. data analysts are responsible for
interpreting data and analyzing the results utilizing statistical techniques and providing ongoing
reports. They develop and implement data analyses, data collection systems, and other
strategies that optimize statistical efficiency and quality. They are also responsible for acquiring
data from primary or secondary data sources and maintaining databases. Data analysts
conduct full lifecycle analyses to include requirements, activities, and design, as well as
developing analysis and reporting capabilities. They also monitor performance and quality
control plans to identify improvements. Finally, they use the results of the above
responsibilities and duties to better work with management to prioritize business and
information needs. One needs only to briefly glance over this list of data-heavy tasks to see
that having a tool that can handle mass quantities of data easily and quickly is an absolute
must. Considering the proliferation of Big data (and it’s still on the increase), it is important to
be able to handle massive amounts of information, clean it up, and process it for use. Python
fits the bill since its simplicity and ease of performing repetitive tasks means less time needs to
be devoted to trying to figure out how the tool works.

Summary: Data analysts conduct full lifecycle analyses to include requirements, activities, and design, as well as
developing analysis and reporting capabilities. One needs only to briefly glance over this list of data-heavy tasks to see that having a tool that can handle mass quantities of data easily and quickly is an
absolute must. Python fits the bill since its simplicity and ease of performing repetitive tasks means less
time needs to be devoted to trying to figure out how the tool works.

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