Python Fundamentals and Data Science Essentials
Learn Python programming and core data science skills with NumPy, Pandas, and machine learning algorithms—perfect for aspiring data scientists.
Learn Python programming and core data science skills with NumPy, Pandas, and machine learning algorithms—perfect for aspiring data scientists.
This comprehensive course bridges Python programming fundamentals with essential data science skills, making it ideal for beginners. Starting with Python basics such as installation, variables, operations, and control structures, you'll build a solid programming foundation before exploring core data structures like strings, lists, tuples, and dictionaries. The course then transitions into data analysis with NumPy for numerical computing and Pandas for data manipulation, where you'll learn to work with DataFrames, perform operations, and merge datasets. Building on these skills, you'll explore data visualization with Matplotlib and Seaborn, and dive into machine learning concepts including linear regression, gradient descent optimization, and classification algorithms like KNN and logistic regression. Each topic includes practical case studies that demonstrate real-world applications, ensuring you can apply theoretical knowledge to actual data problems. No prior programming or data analysis experience is required.
Instructors:
English
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What you'll learn
Run Python programs using variables, operations, and control structures
Manipulate and analyze data with NumPy arrays and Pandas DataFrames
Create effective data visualizations using Matplotlib and Seaborn
Implement linear regression models and understand their mathematical foundations
Master gradient descent for optimization problems
Build KNN classification models and evaluate their performance
Skills you'll gain
This course includes:
11.6 Hours PreRecorded video
5 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
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There are 10 modules in this course
This course provides a comprehensive introduction to Python programming and data science fundamentals. The first modules focus on core Python programming concepts, covering installation, variables, numeric operations, control structures, functions, and data structures like strings, lists, tuples, sets, and dictionaries. The course then progresses to data manipulation and analysis using NumPy and Pandas, where students learn array operations, DataFrame manipulation, data indexing, and merging datasets. The curriculum includes data visualization techniques with Matplotlib and Seaborn, followed by an introduction to machine learning concepts. Students explore linear regression, understanding both theory and implementation, along with optimization using gradient descent. The course concludes with classification algorithms, focusing on K-Nearest Neighbors (KNN) and logistic regression, with each topic reinforced through practical case studies and real-world applications.
Prerequisite - Python Fundamentals
Module 1 · 2 Hours to complete
Prerequisite - NumPy
Module 2 · 50 Minutes to complete
Prerequisite - Pandas
Module 3 · 1 Hours to complete
Prerequisite - Some Fun with Math
Module 4 · 1 Hours to complete
Prerequisite - Simple Linear Regression
Module 6 · 1 Hours to complete
Prerequisite - Gradient Descent
Module 7 · 1 Hours to complete
Prerequisite - Classification: KNN
Module 8 · 2 Hours to complete
Prerequisite - Logistic Regression
Module 9 · 44 Minutes to complete
Prerequisite - Advanced Machine Learning Algorithms
Module 10 · 2 Hours to complete
Instructor
Enhancing IT Education Through Expert-Led Learning
Packt Course Instructors are dedicated to delivering high-quality educational content across a wide range of IT topics, offering over 5,000 eBooks and courses designed to improve student outcomes in technology-related fields. With a focus on practical knowledge, instructors leverage their industry expertise to create engaging learning experiences that help students grasp complex concepts and apply them effectively. The courses cover diverse subjects, from programming languages to advanced data analysis, ensuring that learners at all levels can find relevant resources to enhance their skills. Additionally, Packt emphasizes personalized learning paths and provides analytics tools for educators to monitor student engagement and success, making it a valuable partner in academic settings.
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Frequently asked questions
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