Python for data analytics

UIT VN

An overview of Python programming basics and its application in data analysis.

How to use the Pandas library to clean, filter, and transform data for analysis.

Techniques for exploring and summarizing data using the NumPy library.

Creating various types of visualizations, such as bar charts, scatter plots, and histograms, to communicate data insights effectively.

Performing statistical tests and calculations using Python libraries to derive meaningful insights from data.

An introduction to machine learning concepts and how to implement basic algorithms for classification and regression tasks.

Techniques for analyzing time series data, including smoothing, decomposition, and forecasting.

Extracting data from websites and APIs using Python to supplement analysis with external datasets.

Applying Python skills in data analytics to real-world problems and scenarios to showcase practical use cases.

#	Topic
1	Introduction to Python for Data Analytics An overview of Python programming basics and its application in data analysis.
2	Data Cleaning and Preparation with Pandas How to use the Pandas library to clean, filter, and transform data for analysis.
3	Exploratory Data Analysis with NumPy Techniques for exploring and summarizing data using the NumPy library.
4	Data Visualization with Matplotlib Creating various types of visualizations, such as bar charts, scatter plots, and histograms, to communicate data insights effectively.
5	Statistical Analysis with Python Performing statistical tests and calculations using Python libraries to derive meaningful insights from data.
6	Machine Learning Basics with Scikit-Learn An introduction to machine learning concepts and how to implement basic algorithms for classification and regression tasks.
7	Time Series Analysis Techniques for analyzing time series data, including smoothing, decomposition, and forecasting.
8	Web Scraping and API Integration Extracting data from websites and APIs using Python to supplement analysis with external datasets.
9	Case Studies and Real-World Applications Applying Python skills in data analytics to real-world problems and scenarios to showcase practical use cases.