Data Science For Beginners| Data Science Tutorial | Data Science With Python | Edureka
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Explore the key concepts of data science that will help you build a successful future in your data science career. This ""Data Science for Beginners"" video targets individuals eager to embark on their data science journey, providing a comprehensive overview of core concepts. It encompasses an introduction to data science, real-life illustrations, insights into the roles and responsibilities of data scientists, discussions on data and information, and practical tutorials on model building.
✅00:00 - Introduction to Data Science
✅03:44 - Real-Life Examples of Data Science
✅05:14 - AI v/s ML v/s DL v/s Data Science
✅06:13 - Roles of Data Scientist
✅08:08 - Understanding Data and Information
✅09:08 - Model Building and Algorithms
✅11:38 - Hands-on Model Building Tutorial
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What is Python for Data Science?
Data is everywhere, and data science will help us extract it. Python has been used to implement numerous data science applications. Python is a generic programming language for creating websites, backend APIs, and scripting. Python's built-in libraries, frameworks, and tools can be used to execute various data science activities.
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Why should I learn Data Science with Python Course?
Python is unquestionably one of the most popular data science languages, capable of data analysis, manipulation, and visualization. It has access to many of Data Science libraries, making it ideal for designing applications and applying algorithms.
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What skills should a Data Science Expert know?
A Data Science Expert should have a combination of technical and non-technical skills, Here are the required Data scientists skills:
1) Strong programming skills in languages like Python, R, and SQL.
2) Proficiency in statistical analysis, machine learning, and data visualization techniques.
3) Knowledge of data structures, algorithms, and database systems.
4) Strong problem-solving skills and ability to work with large and complex datasets.
5) Understanding of business processes and ability to communicate effectively with stakeholders.
6) Knowledge of software engineering principles for building scalable and maintainable data pipelines.
7) Continual learning mindset to keep up with the latest trends and technologies in the field.
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