Boost your data science skills by mastering NumPy, Pandas, SciPy, and powerful visualization tools in Python.
Sub Category
- Data Science
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Objectives
- Introduction to Python for Data Science
- Overview of NumPy, Pandas, Matplotlib, and SciPy
- Creating NumPy Arrays
- Mathematical Operations with NumPy Arrays
- Working with Random Numbers and Simulations
- Advanced Array Manipulation and Linear Algebra
- NumPy for Statistical Computations (Mean, Median, Standard Deviation)
- Performance Optimization with NumPy
- Loading and Saving Data with Pandas (CSV, Excel, SQL, etc.)
- Indexing, Selecting, and Filtering Data in DataFrames
- Advanced Pandas Techniques
- Matplotlib Data Visualization
- Seaborn Advanced Visualization Techniques
- SciPy Scientific Computing
- Combining Libraries for Real World Data Science
- And more........
Pre Requisites
- Basic understanding of Python programming (variables, data types, loops, functions).
- No prior experience with NumPy, Pandas, SciPy, Matplotlib, or Seaborn is required.
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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