Python Data Analysis Essentials
Master data analysis fundamentals using Python libraries like pandas, numpy, and scikit-learn to process, analyze, and visualize data.
Master data analysis fundamentals using Python libraries like pandas, numpy, and scikit-learn to process, analyze, and visualize data.
This comprehensive course teaches practical data analysis using Python's powerful data science libraries. Students learn to import, clean, and analyze datasets using pandas DataFrames and numpy arrays. The curriculum covers essential statistical analysis, data visualization, and machine learning concepts. Through hands-on labs and assignments, participants gain experience in building predictive models using scikit-learn and creating meaningful data visualizations.
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What you'll learn
Import and prepare datasets for analysis using Python
Master data manipulation with Pandas DataFrames and NumPy arrays
Create insightful data visualizations and statistical summaries
Build and evaluate machine learning models using scikit-learn
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
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There are 6 modules in this course
This practical course focuses on data analysis using Python's essential libraries. The curriculum is structured into five comprehensive modules covering data importing, cleaning, summarization, model development, and evaluation. Students learn through a combination of lectures and hands-on labs, gaining experience with real-world datasets and analysis techniques. The course emphasizes practical skills in statistical analysis, data visualization, and machine learning.
Importing Data Sets
Module 1
Data Wrangling
Module 2
Exploratory Data Analysis
Module 3
Model Development
Module 4
Model Evaluation
Module 5
Final Assignment
Module 6
Fee Structure
Instructor
Pioneering Data Scientist Bridging AI Research and Education
Dr. Joseph Santarcangelo, a Data Scientist at IBM, brings a unique blend of academic excellence and practical expertise to the field of data science and artificial intelligence. With a Ph.D. in Electrical Engineering, his groundbreaking research focused on the intersection of machine learning, signal processing, and computer vision to understand how video content influences human cognitive processes. At IBM, he has established himself as a prominent educator and course developer, creating comprehensive learning materials that have reached hundreds of thousands of students worldwide. His teaching portfolio encompasses a wide range of technical subjects, from foundational Python programming to advanced topics in artificial intelligence, machine learning, and computer vision. Santarcangelo's ability to translate complex technical concepts into accessible learning experiences has made him an influential figure in data science education, maintaining consistently high ratings from learners while continuing to push the boundaries of applied machine learning and artificial intelligence research.
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