Basics Of Python For Data Science Full Course (for Beginners)

Hey Guys! Welcome! In this comprehensive data science tutorial, you'll learn the fundamental concepts and techniques of Python programming language that are essential for data science.
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This step-by-step guide is designed specifically for beginners and covers a wide range of topics such as data analysis, visualization, and machine learning. With practical examples and exercises, you'll gain hands-on experience working with real-world datasets and learn how to apply Python's powerful libraries to extract insights from data.
Whether you're a student, a professional, or just curious about data science, this full course will provide you with a solid foundation in Python programming for data science.
▶️In this tutorial we discussed following the topics:
• Introduction to Python and Data science
• Basic Python syntax and Data structures
• Importing and cleaning data with pandas, introduction to the pandas library
• Exploratory data analysis with pandas
• Data preprocessing and feature engineering
• Introduction to machine learning with Scikitlearn
• Advanced data science techniques
Data are the new oil. Data capture, storage, and analysis are the backbones of any modern information technology system, as evidenced by this statement, whether it's making business decisions, predicting the weather, studying the structures of proteins in biology, or planning a marketing campaign.
These scenarios require a multidisciplinary approach that uses mathematical models, statistics, graphs, databases, and the business or scientific logic behind the data analysis. So, we need a programming language to meet all these different data science needs.
Python stands out as one of these languages because it has many libraries and built-in features that make it easy to meet the needs of Data science.
Since its early days as a utility language, Python has become a major player in artificial intelligence (AI), machine learning (ML), big data, and analytics. It's one of the fastest-growing programming languages in the world.
Python is a high-level programming language widely used to make mobile apps, websites, and software and to analyze and compute scientific and numerical data.
If you learn Python for data science or another career, it can open up a lot of doors for you and improve your career opportunities. Even if you don't work in AI, ML, or data analytics, Python is still important for developing websites and graphical user interfaces (GUIs).
Python is used a lot in data science because it has shown time and time again that it can solve complex problems quickly. With the help of data-focused libraries like NumPy and Pandas, anyone familiar with Python's rules and syntax can soon use it as a powerful tool to process, change, and display data.
If you get stuck, it's also pretty easy to solve Python-related problems because so much free documentation is available. For example, Python Data Science Handbook: Essential Tools for Working with Data is a popular book you can read online for free.
Python has become popular with people who don't work in software engineering or other technical fields. It makes data analysis possible for people from backgrounds like business and marketing.
Python is easy to learn because it is expressive, short, and easy to read. This makes it much easier for new coders to get started quickly, and the community that supports the language will have enough resources to solve problems whenever they come up.
So, watch the video till the end and share your thoughts in the comments.
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