Unlocking Data Insights with Python and Pandas

This course, Python: Data Management with Pandas, is designed to take you from fundamental concepts to advanced techniques in using Pandas, the essential Python library for data manipulation and analysis. We start with the basics of Pandas, explaining its importance in data analysis, followed by instructions for installation and setup. You will get hands-on experience loading data and performing basic explorations to understand data structure and content.

Delve deeper into data manipulation where you will create and manipulate Series and DataFrames—the core data structures in Pandas. You will learn to work with time-series data and explore index objects’ features and functionalities. The course will enhance your skills with advanced operations like reindexing, deleting rows and columns, and filtering data. We cover arithmetic functions, data alignment, and applying custom functions and mappings, equipping you with the tools necessary for sophisticated data handling.

The course also focuses on aggregation and summarization methods for statistical computing, analyzing correlations and covariances, and counting unique values. You will learn essential techniques for dealing with missing data, filtering based on specific criteria, and transforming data. By the end of the course, you will have the skills to concatenate DataFrames, conduct joins, and utilize advanced grouping techniques with groupby. With the integration of Pandas with SQL databases and visualization libraries like Matplotlib and Seaborn, this course culminates in a final project to apply all your acquired knowledge, ensuring you are ready to take your data analysis skills to a professional level.

What you will learn:

• Perform descriptive and exploratory data analysis (EDA) using grouping, aggregation, and statistical summaries techniques.
• Handle and transform data from various sources (CSV, Excel, SQL) using Pandas data structures for Machine Learning models.
• Create clear and effective visualizations using libraries like Matplotlib and Seaborn to uncover trends in data with charts and diagrams.

Course Content:

• Sections: 11 sections
• Lectures: 38 lectures
• Duration: 5h 3m

Requirements:

• Access to a computer with Python installation, Pandas, and related libraries (NumPy, Matplotlib, Seaborn).
• Basic knowledge of Python: Understanding the basic syntax, data types comprehension (lists, dictionaries, tuples, etc.), and knowledge of control flow structures (loops, conditionals).
• Fundamental programming skills: Experience in writing and executing Python scripts, basic knowledge of functions and modules, and the ability to work with development environments (IDEs) or text editors.
• Basic mathematics and statistics: Understanding of basic statistical concepts such as mean, median, and standard deviation.
• Basic data handling experience: Knowledge about the import and export of data from/to CSV files or Excel.

Who is it for?

• Students and Data Science professionals looking to acquire practical data analysis, statistics, and data science skills.
• Data Analysts and BI professionals needing to manipulate and analyze large data sets.
• Researchers in disciplines requiring data analysis, like social sciences, biology, and economics.
• Software developers wanting to expand Python skills and learn to use Pandas for data handling.
• Marketing and business professionals aiming to utilize data for informed decision-making and analysis.
• Anyone interested in Data Analysis eager to learn how to handle and analyze data using advanced tools.

What are you waiting for to get started?

Enroll today and take your skills to the next level. Coupons are limited and may expire at any time!

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