Master the basic and advanced techniques utilized for analyzing data using the functionalities of python programming.
Analyzing Data with Python certification course is designed by IBM and taught by Joseph Santarcangelo - PhD., Data Scientist at IBM, which is delivered by edX, which is targeted at the students looking for a comprehensive learning environment that could assist in their mastery of the concepts and methods required to develop the skills necessary for analyzing data with the Python programming language. Students who are interested in enrolling in the Analyzing Data with Python online classes by IBM and edX should be familiar with the fundamentals of programming in Python.
Analyzing Data with Python online training is a 5-week learning program that includes approximately 20 hours of extensive video-based materials that cover topics such as data loading, data manipulation, data preparation, data cleaning, linear regression, data visualization, ridge regression, polynomial regression, and machine learning models. Students who successfully finish the verified version of this program will also receive a skill badge that validates their knowledge and skills. This self-paced programme free course and they learners can upgrade it to the verified mode and get certification by paying the course fee.
Yes
IBM
₹8,284
After completing the Analyzing Data with Python online certification, students will develop an in-depth understanding of the functionalities of python programming for data analysis and statistical operations. Students will explore the fundamentals of data sets, and data frames as well as will acquire the knowledge of the strategies involved with concepts like grouping, data cleaning, data preparation, data normalization, data loading, data visualization, and data manipulation using the features of Pandas, Numpy, SciPy, and ANOVA. Students will learn about machine learning models for making decisions as well as will acquire the knowledge of the methodologies involved with model evaluation, model development, ridge regression, polynomial regressions, and linear regression.
Mr Joseph Santarcangelo Data Scientist IBM
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