Data Analytics Engineering: Probability & Techniques
Learn data analytics fundamentals: Python, data structures, wrangling, and exploratory analysis. Master probability techniques for real-world data.
Learn data analytics fundamentals: Python, data structures, wrangling, and exploratory analysis. Master probability techniques for real-world data.
This course offers a comprehensive introduction to data analytics engineering, focusing on probability and essential techniques. Students will learn Python programming fundamentals, work with modern data structures, and apply data cleaning and wrangling operations. The curriculum covers conceptual and practical applications of probability and distribution, cluster analysis, text analysis, and time series analysis. Through hands-on modules, learners will master Python basics, explore various data structures, understand DataFrames for efficient data manipulation, and develop skills in exploratory data analysis. By the end of the course, students will be equipped with the necessary tools and techniques to analyze real-world data effectively, preparing them for more advanced topics in data analytics engineering.
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What you'll learn
Understand Python programming fundamentals and control structures
Master various data structures including lists, dictionaries, and arrays
Learn to work with DataFrames for efficient data manipulation and analysis
Develop skills in data cleaning, transformation, and aggregation
Gain proficiency in exploratory data analysis techniques
Apply statistical summaries and data visualization methods
Skills you'll gain
This course includes:
1 Hours PreRecorded video
4 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This course provides a comprehensive introduction to data analytics engineering, focusing on probability and essential techniques. Students will learn Python programming fundamentals, working with various data structures like lists, dictionaries, and arrays. The curriculum covers DataFrames for efficient data manipulation, including cleaning, transformation, and aggregation. Learners will develop skills in exploratory data analysis, using statistical summaries and data visualization techniques to uncover patterns and trends in real-world data. Through hands-on practice and quizzes, students will gain practical experience in applying these concepts, preparing them for more advanced topics in data analytics.
Introduction to Python
Module 1 · 5 Hours to complete
Data Structures
Module 2 · 4 Hours to complete
Modern Data Structures and Data Wrangling
Module 3 · 4 Hours to complete
Exploratory Data Analysis
Module 4 · 4 Hours to complete
Fee Structure
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Financial Aid
Instructor
Associate Teaching Professor in Mechanical and Industrial Engineering
Srinivasan Radhakrishnan is an Associate Teaching Professor in the Department of Mechanical and Industrial Engineering at Northeastern University. He is known for his significant contributions to the field, which earned him the Faculty Research Team Award in 2024. His research interests encompass data analytics, artificial intelligence, and operations research, showcasing his dedication to advancing knowledge and practice in these critical areas.
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