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Python for Data Science Intermediate
Learn the essential skills to write Python code at the intermediate levelPreview the first lesson free — get full access to all 30 lessons.
Intermediate Provider : Nizar Dajani 8 chapters · 30 Lessons · 3h 37m in English
Course Description
The topics discussed in this course will build on your understanding of the constantly changing data science landscape. Python has grown into a very popular programming language, especially for data science. Its simplicity in coding, and easy-to-use concepts make it the go-to language for programmers, as well as non-programmers like data scientists. The good news is that Python is a fun and easy-to-learn programming language. You will write more advanced code as you work with datasets and learn ways to manipulate and visualize the data to make sense out of it.
In this Python for Data Science intermediate course, you will build on what you previously learned about the fundamentals of Python, and the basic programming skills needed to use it for data science. These new coding skills will allow you to work with datasets of any size to better analyze and understand them, helping you to make key decisions based on your datasets.
What You'll Learn
- Describe data preparation using Python
- Recognize the different data sourcing libraries of Python, including Pandas, SQLite, BeautifulSoup, and Scrapy
- Explain how to clean and manipulate data in Python, covering filtering, treating missing values, handling duplicates, and transforming data
- Identify the different data visualization libraries to use with Python, such as Matplotlib, Seaborn, and Plotly
- Apply core Python libraries like Pandas, NumPy, SciPy, and Regex to work with datasets
- Use object-oriented concepts including classes, objects, inheritance, magic methods, encapsulation, and abstraction
Key Takeaways
- Python is a popular, simple, and easy-to-learn programming language widely used for data science.
- This intermediate course builds on the fundamentals of Python and the basic programming skills needed to use it for data science.
- The course teaches ways to manipulate and visualize data to make sense of it, working with datasets of any size.
- Data manipulation skills covered include filtering and selecting data, treating missing values, handling duplicate data, concatenating and transforming data, grouping, and aggregation.
- These coding skills help learners better analyze and understand datasets to make key decisions based on them.
Frequently Asked Questions
Who is this course for?
It is designed for learners who have already studied the fundamentals of Python and the basic programming skills needed to use it for data science, and who want to build on that foundation with more advanced coding for working with datasets.
What will I learn in this course?
You will learn to prepare data, recognize Python's data sourcing libraries, clean and manipulate data, and identify the different data visualization libraries to use with Python, while writing more advanced code to work with datasets of any size.
What topics and libraries does the course cover?
It covers libraries and tools including Pandas, NumPy, SciPy, Regex, SQLite, BeautifulSoup, Scrapy, Matplotlib, Seaborn, and Plotly, along with object-oriented concepts such as classes, objects, inheritance, magic methods, encapsulation, and abstraction.
What prerequisites are needed?
The course builds on what you previously learned about the fundamentals of Python and the basic programming skills needed to use it for data science, so prior knowledge of Python fundamentals is expected.
What skills will I gain?
You will gain skills in Data Abstraction, Data Classification, Data Driven Instruction, Data Science, Python (Programming Language), and Scikit-Learn (Python Package).
