Crucial topics of an all-inclusive Python data science course

1,155 Views Updated: 22 May 2018
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Crucial topics of an all-inclusive Python data science course

Ever since the big data has surprised the business world, we have seen an exponential rise in the demand of skilled and knowledgeable data scientists.  Businesses are willing to unearth valuable insight and intelligence from the raw datasets, and they know that only skilled data scientists would be able to help them in this aspect. 

In order to perform that task adroitly, data scientists would need to leverage comprehensive expertise into Python, one of the most famous programming languages across the data science industry.  Considering the rise in demand of data scientists having comprehensive knowledge of Python, most of the aspirants are seeing this as a proficient opportunity and are willing to join Python data science course that can help them start preparation for the professional journey.

Although nobody can deny the fact that these Python courses and training programs are helping aspirants incessantly in gaining understanding of the industry, but there is another aspect attached to it as well.  All the aspirants actually learn various concepts and applications of Python in data science during these courses, but most of these courses and/or training programs that are organized by reputed global institute are not that comprehensive. 

It must be ensured that these courses cover all the crucial topics including clustering, data visualization, dimensionality reduction, feature importance, and so on.  This blog highlights and discuss some crucial, pivotal topics that must be included in any Python specific course for data scientists.

  1.  Detailed session on machine learning:  Aspiring data scientists must be taught about machine learning, its significance in data science, and the problems it can solve in the most competent manner as it relates to performing analytical functions on datasets.

  2. Data representation methods:  All the data scientists should know various effective ways in which data can be presented using the Python language.  It must be taught to them during Python data science course that data should be presented in a manner that can be helpful in deriving insights and valuable information.

  3. All the data visualization techniques:  Data scientists need to learn that there are various data visualization techniques, and each of these techniques has its own significance and contextual use.  It is so obvious that one cannot use the same data visualization technique for different types of datasets.

  4. Supervised learning algorithms:  Data scientists should be educated on how to apply supervised learning algorithms (including support vector classifier, random forest, and so on) to the wide range of datasets.  This will help them manage and analyze distinct datasets more efficiently.

In a few words:  Aforementioned are crucial aspects or pivotal topics that must be included in an all-inclusive Python data science course so as to help aspirants gain complete understanding of the subject.

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