Free Download Python For Data Science by The Ai Academy
Published 6/2024
Created by The Ai Academy
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 24 Lectures ( 8h 21m ) | Size: 3.37 GB
Master Python for Data Science: Classification, Regression, Data Visualization, and Feature Engineering Techniques
What you'll learn:
Master Python Programming Basics including understanding data types, control structures, and functions, which are essential for any data science task.
Data Manipulation and Analysis using powerful Python libraries such as Pandas and NumPy to manipulate, analyze, and visualize data effectively.
Data Visualization Techniques to create insightful and compelling data visualizations using Matplotlib and Seaborn
Machine Learning Fundamentals including building and evaluating models using algorithms like Logistic Regression and KNN, tackle real-world data science problem
Requirements:
No programming language required, you will learn everything you need to learn
Description:
Welcome to "Python for Data Science: From Basics to Advanced Techniques"! This comprehensive course is designed to equip you with the essential skills and knowledge required to excel in data science using Python. Whether you are a beginner or looking to advance your data science capabilities, this course covers everything you need to know.We start with an introduction to Python and its application in data science, ensuring you are comfortable with programming fundamentals. Next, we dive into classification problems, using real-world datasets to classify income levels and understand how different attributes influence these predictions.Our journey continues with regression analysis, where you'll learn to predict continuous values, such as the price of pre-owned cars. We will guide you through data cleaning, exploratory data analysis, and building regression models using various techniques, including linear regression and random forests.Data visualization is another critical area covered in this course. You will master tools like Matplotlib and Seaborn to create insightful visualizations that help you interpret data trends and patterns effectively.Feature engineering is also a focal point, teaching you how to preprocess and transform raw data into meaningful features that improve model performance. We emphasize practical, hands-on exercises and real-world projects to solidify your understanding and application of these concepts.By the end of this course, you will have a robust foundation in data science, equipped with the skills to tackle complex data problems and derive actionable insights using Python. Join us on this exciting journey and take your data science expertise to the next level!
Who this course is for:
This course is designed for a diverse range of learners who are eager to delve into the world of data science using Python. Here's who will find this course valuable: Beginners in Data Science: Individuals with little to no prior experience in data science who want to build a strong foundation in Python and data analysis. Aspiring Data Scientists: Those looking to transition into a data science career and need to acquire essential skills and knowledge in Python programming and data manipulation. Students and Academics: University students, researchers, and academics who want to enhance their data analysis capabilities using Python. Professionals Seeking to Upskill: Working professionals from various fields, such as finance, marketing, and healthcare, who wish to leverage data science to make data-driven decisions and enhance their career prospects. Tech Enthusiasts and Hobbyists: Individuals passionate about technology and data who want to learn how to analyze and visualize data using Python. Business Analysts: Analysts who aim to improve their data handling and analysis skills to provide more insightful business intelligence. This course is structured to cater to learners at different stages of their data science journey, providing comprehensive knowledge and practical skills to excel in the field.
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