Python for data analytics

Techniques for exploring and summarizing data using the NumPy library.

Exploratory Data Analysis with NumPy

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.

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.