Data Preparation and Analysis: Master Python Data Analysis
Master data preparation and analysis with Python. Learn EDA, feature screening, and advanced modeling techniques.
Master data preparation and analysis with Python. Learn EDA, feature screening, and advanced modeling techniques.
This comprehensive course introduces essential concepts and techniques for data analysis, focusing on the process from data preparation to result interpretation. Students will learn Exploratory Data Analysis, Feature Screening, Segmentation, Association Rules, Clustering, Decision Trees, Regression, and Performance Evaluation. The course covers statistical theory, matrix algebra, and computational techniques using Python. By the end, students will have built a robust inventory of data analysis codes and gained confidence in advocating their propositions to business stakeholders.
Instructors:
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What you'll learn
Apply appropriate techniques for generating insights from data
Present actionable solutions with confidence to business stakeholders
Perform Exploratory Data Analysis and data preparation
Implement feature screening and selection methods
Conduct market basket analysis using association rules
Apply clustering techniques for segmentation
Skills you'll gain
This course includes:
4.42 Hours PreRecorded video
32 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 9 modules in this course
This comprehensive course introduces essential concepts and techniques for data analysis, focusing on the process from data preparation to result interpretation. Students will learn Exploratory Data Analysis (EDA), Feature Screening, Segmentation, Association Rules, Clustering, Decision Trees, Regression, and Performance Evaluation. The course covers statistical theory, matrix algebra, and computational techniques using Python. Practical applications include market basket analysis, customer segmentation, and predictive modeling. By the end, students will have built a robust inventory of data analysis codes and gained confidence in advocating their propositions to business stakeholders.
Process of Preparing and Analyzing Data
Module 1 · 11 Hours to complete
Measure and Visualize Correlation
Module 2 · 9 Hours to complete
Market Basket Analysis
Module 3 · 8 Hours to complete
Partitioning, Segmenting, and Clustering of Observations
Module 4 · 9 Hours to complete
Linear Regression
Module 5 · 9 Hours to complete
Binary Logistic Regression
Module 6 · 8 Hours to complete
Decision Trees - The CART Algorithm
Module 7 · 9 Hours to complete
Evaluating the Performance of Models
Module 8 · 9 Hours to complete
Summative Course Assessment
Module 9 · 3 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructors
Professor
Jawahar Panchal is an instructor at the Illinois Institute of Technology, teaching Computer Science in the College of Computing and Finance in the Stuart School of Business, with a focus on data mining and quantitative investment strategies. With a strong foundation in mathematics and technology, his expertise spans asset management and capital markets, encompassing both quantitative research and software development to provide a holistic understanding of front and back-office operations in these industries. Jawahar has a deep interest in physics, computer science, and electrical/computer engineering, and he is currently completing his PhD research in artificial intelligence and machine learning.
Prof.
Ming-Long Lam is an instructor at Illinois Institute of Technology, where he teaches the course Data Preparation and Analysis on Coursera. His expertise lies in applied data science, focusing on equipping students with the skills necessary to effectively prepare and analyze data for various applications. In addition to his teaching role, Lam has a background that includes experience in data collection and analysis, emphasizing the importance of perseverance and patience in dealing with complex datasets, particularly from governmental sources. His course aims to provide learners with practical insights and techniques essential for data-driven decision-making in diverse fields. Through his instruction, students gain a solid foundation in data preparation processes, which are critical for successful analysis and interpretation of data.
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