Description
Practical Python for Time Series Analysis is a course on how to work with, analyze, and forecast time-dependent data using Python, published by LinkedIn Online Academy. This is a practical course designed to teach you how to work with, analyze, and forecast time-dependent data using Python. Whether you’re dealing with financial trends, weather patterns, sales forecasts, or sensor data, this course shows you how to effectively clean, visualize, model, and interpret time series. You’ll learn essential Python libraries like Pandas, NumPy, Matplotlib, Statsmodels, and modern forecasting tools, while gaining hands-on experience with real-world datasets. The course focuses on building intuition, understanding patterns like seasonality and trends, and applying statistical and machine learning techniques to produce meaningful forecasts.
This Python and Data Expert course focuses on practical time series analysis in Python, emphasizing the ability to explain and discover cause-and-effect relationships. Discover methods for understanding how patterns emerge, the variables that drive change over time, and how to interpret results responsibly. All topics are taught through practical examples, primarily from the energy sector, so you can apply insights directly to real-world data. Develop the understanding needed to transform raw time series data into meaningful explanations that inform strategic decision-making.
What you will learn in Practical Python for Time Series Analysis:
- Python (Programming Language)
- Time Series Analysis
- Data Analysis
- And …
Course specifications
Publisher: LinkedIn
Instructors: Jesus Lopez
Language: English
Level: Intermediate
Number of Lessons: 41
Duration: 4h 57m
Course topics
Practical Python for Time Series Analysis Prerequisites
None
Pictures
Practical Python for Time Series Analysis introduction video
Installation guide
After Extract, watch with your favorite Player.
English subtitle
Quality: 720p
Downloadly link
Rapidgator link
File password (s): www.downloadly.ir
Size
563 MB
