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Data Science Mastery: Frameworks, Algorithms & Applications
Rating: 4.9 out of 5(4 ratings)
28 students

Data Science Mastery: Frameworks, Algorithms & Applications

Master data science frameworks, machine learning algorithms and rea-world applications with practical case studies.
Created byManjunatha Gb
Last updated 2/2025
English
English [Auto],

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.

Course content

1 section • 5 lectures • 50m total length

  • Introduction - Data Science Roadmap1:09

    Follow a concrete data science roadmap from ground zero that guides your learning journey, with strategic tips for students or professionals pivoting into data science.

  • Frameworks and Processes for Data Science Projects13:50

    Learn the crisp-dm data mining framework and agile data science practices, guiding data science projects through six phases—business understanding, data understanding, data preparation, modeling, evaluation, deployment—via iterative delivery.

  • Python For Data Science16:45

    Python powers data science with core libraries like NumPy, pandas, Matplotlib, and SciPy, and offers hands-on data analysis and machine learning demos in the Jupiter Notebook via the Anaconda platform.

  • Python Basic Quiz 1
  • Implementation Of Lists In Python10:22

    Learn to create lists with [] and the list constructor, then manipulate them with indexing, slicing, append, insert, extend, remove, index, count, sort, and reverse.

  • Python Basic Quiz 2
  • Implementation Of Dictionaries In Python8:51

    Create and modify Python dictionaries with curly braces, access values by keys, and print results; use keys(), values(), length, copy, clear, and delete to manage data.

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!

Who this course is for:

  • 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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