Introduction

In the realm of big data, proficiency in R or Python is essential, and when it comes to Python, mastery of Pandas and NumPy is paramount. These third-party packages are meticulously crafted for data analysis, making them indispensable tools for extracting valuable insights from real-world data. Our Python for Data Analysis course is meticulously designed to empower you with the statistical and mathematical skills needed to navigate through big data landscapes. Enroll now to delve into the world of statistics, unlock the potential of real-world data, and gain actionable insights for informed business decisions. Elevate your proficiency in Python, Pandas, and NumPy to harness the transformative power of mathematics in data analysis. Join us on a learning journey that seamlessly integrates statistical methodologies with practical business applications.

Course Outline

Python Data Analysis Libraries
- Data Analysis Components
- Data Analysis Steps
- Python Data Analysis Libraries
Overview of Numpy
- What is Numpy?
- 1D and 2D Array
- Array Arithmetics
- Special Functions
- Math Functions
- Selecting and Slicing Array Elements
- Filtering Array Elements
- Transforming Array
- Statistics
Data Analysis with Pandas
- Series
- Data Frame
- Selecting/Slicing Data
- Import/Export Data – CSV, Excel, Internet
- Filtering Data
- Missing Data
- Removing Duplicated Data
- Joining Data from Different Sources
- Transforming Data
- Aggregating Data
- Pandas Statistics
Data Visualization with Matplotlib & Seaborn
- Overview of Matplotlib & Seaborn
- Plot Types
- Plot Attributes
- Object Oriented Plots
- Time Series Plot
Intro to Machine Learning with Scikit-Learn
- Overview of Scikit-Learn
- Supervised and Unsupervised Learning
- Classification
- Regression
- Clustering
- PCA

Students looking to learn Python 3 may want to look at our Python 3 Programming course