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This course introduces basic concepts of data and big data terminologies and frameworks required for data science.
It also covers the DataFrames from the Pandas library in Python.
The concepts and techniques in this course will serve as building blocks for the advanced topics in Artificial Intelligence.
This course maps to N8101.
Co-branded certificate by FutureSkills Prime & Accenture.
Job Roles:
Data Quality Analyst
What will you learn in this course?
This course introduces the basic concepts of data required for data science, examining various categories of data and big data terminologies and frameworks.
This course also covers the DataFrames from the Pandas library in Python.
The concepts and techniques in this course will serve as building blocks for the advanced topics in data science. This course maps to N8101.
This course offers learning components, such as: videos explaining the concepts and techniques, knowledge checks to review and test concept retention, and hands-on exercise documents, guiding to apply the learning.
The platform required for this is Jupyter Notebook with Python 3.6 and above.
This course introduces the various types of data and the methods of acquiring and storing them.
During the run time 10 hours across 4 modules, the learner gets familiar with concepts like - data, Big data and its frameworks, metadata, categories of data and their applications, DataFrame and different data validation processes.
Platforms required for this course are Jupyter Notebook with Python 3.6 or above.