Data Science Mastery: Frameworks, Algorithms & Applications
Published 2/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 263.04 MB | Duration: 0h 51m
Master data science frameworks, machine learning algorithms and rea-world applications with practical case studies.
What you'll learn
Master Data Science Frameworks & Workflows. Understand Industry related frameworks like CRISP DM. Learn how to structure Data Science projects efficiently.
Gain Deep Understanding of Machine Learning Paradigms - Differentiate between Supervised, Unsupervised and Semi-supervised learning, Identify right algorithms.
Apply Data Science concepts to Real-world problems. Learn to frame business problems as data science problems. Develop hands-on experience to solve problems.
Enhance Decision-Making with Data-Driven Insights. Interpret model outputs & evaluate performance metrics. Make actionable business recommendations.
Requirements
No prior data science experience required.
Basic understanding of mathematics & statistics.
Familiarity with fundamental concepts like Mean, Variance, Probability, Linear Algebra will be helpful but NOT mandatory.
Some exposure to programming (preferably Python).
No advanced coding skills required.
Python knowledge (variables, loops, functions) will help in following along with practical examples.
Curiosity & problem-solving mindset.
Description
Are you looking to master data science from the ground up? Data Science Mastery: Frameworks, Algorithms, and Applications is designed to give you a structured, practical understanding of data science workflows, key machine learning concepts, and real-world applications.In this course, you will learn industry-standard data science frameworks like CRISP-DM to structure your projects efficiently. You'll explore different machine learning paradigms, including supervised, unsupervised, and semi-supervised learning, understanding when and how to use them. Additionally, we will dive into data types and measurement scales, ensuring you can properly analyze and preprocess data for modeling.Beyond theory, this course emphasizes real-world applications through practical case studies, helping you bridge the gap between knowledge and execution. Whether you are an aspiring data scientist, a professional looking to transition into data science, or a business expert working with data-driven insights, this course will provide a strong foundation.By the end of this course, you will be able to structure data science projects, select appropriate algorithms, and derive actionable insights to solve business problems effectively.No prior experience in data science is required-just a basic understanding of math, some programming familiarity (preferably Python), and a problem-solving mindset! Start your Data Science journey with Confidence today!
Overview
Section 1: Introduction
Lecture 1 Introduction - Data Science Roadmap
Lecture 2 Frameworks and Processes for Data Science Projects
Lecture 3 Python For Data Science
Lecture 4 Implementation Of Lists In Python
Lecture 5 Implementation Of Dictionaries In Python
Beginner Python Developers Curious about Data Science,Aspiring Data Scientists & Analysts,Professionals Looking to Pivot into Data Science,Students & Recent Graduates,Business & Domain Experts Working with Data,Data Science Enthusiasts Seeking Practical Understanding
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