Course Overview
- Course Title: Complete Guide to NumPy, Pandas, SciPy, Matplotlib & Seaborn
- Instructor: Sheikh Coding Institute
- Target Audience:
- Beginners eager to enter the world of data
- Experienced programmers looking to deepen their skills
- Prerequisites:
- Basic understanding of Python programming (variables, data types, loops, functions)
- No prior experience with NumPy, Pandas, SciPy, Matplotlib, or Seaborn required
Curriculum Highlights
- Key Topics Covered:
- 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
- Key Skills Learned:
- Work with multidimensional arrays, broadcasting, indexing, and performance optimization in NumPy
- Master dataframes, series, grouping, filtering, merging, and time series data in Pandas
- Dive into scientific computing with optimization, statistics, interpolation, signal processing, and more in SciPy
- Create insightful and beautiful visualizations, from basic plots to advanced charts in Matplotlib and Seaborn
- Clean, transform, and prepare data for analysis and modeling
Course Format
- Duration: 4.5 hours on-demand video
- Format: Self-paced online course
- Resources:
- Access on mobile and TV
- Certificate of completion