Python Analytics
This course focuses on using Python for data analytics, equipping students with essential skills in data manipulation, analysis, and visualization. Python’s powerful libraries make it one of the most popular languages for data science. Students will cover several key areas, including:
- Data Manipulation with Pandas: Students will learn to use Pandas, a powerful Python library, for data cleaning, transformation, and handling large datasets efficiently. They will work with data frames to filter, sort, and aggregate data to prepare it for analysis.
- Data Visualization with Matplotlib and Seaborn: The course covers data visualization techniques using Matplotlib and Seaborn libraries. Students will create various types of charts, graphs, and interactive plots to make data insights accessible and understandable.
- Descriptive and Inferential Statistics: Students will gain an understanding of statistical concepts, including measures of central tendency, variance, and probability distributions. This section will also cover inferential statistics to draw conclusions from data samples.
- Data Analysis Workflows: Students will learn how to build end-to-end data analysis workflows. This includes loading, cleaning, transforming, and visualizing data, creating a complete pipeline to gain insights and make data-driven decisions.
- Working with Real-World Data: Through hands-on projects, students will work with real-world datasets, enabling them to apply their skills in practical scenarios. These projects help students build experience in analyzing and interpreting data for business and research purposes.
This course is suitable for beginners and those with basic Python knowledge who want to build a foundation in data analytics. By the end of the course, students will be able to handle data-driven projects, generate insights, and prepare data visualizations, making them well-prepared for roles in data analytics, business intelligence, and data science.