Python for Time Series Data Analysis from Udemy

Welcome to the best online resource for learning how to use the Python programming Language for Time Series Analysis.

This course will teach you everything you need to know to use Python for forecasting time series data to predict new future data points.

We'll start off with the basics by teaching you how to work with and manipulate data using the NumPy and Pandas libraries with Python. Then we'll dive deeper into working with Pandas by learning about visualizations with the Pandas library and how to work with time stamped data with Pandas and Python.

Then we'll begin to learn about the statsmodels library and its powerful built in Time Series Analysis Tools. Including learning about Error-Trend-Seasonality decomposition and basic Holt-Winters methods.

Afterwards we'll get to the heart of the course, covering general forecasting models. We'll talk about creating AutoCorrelation and Partial AutoCorrelation charts and using them in conjunction with powerful ARIMA based models, including Seasonal ARIMA models and SARIMAX to include Exogenous data points.

Afterwards we'll learn about state of the art Deep Learning techniques with Recurrent Neural Networks that use deep learning to forecast future data points.

This course even covers Facebook's Prophet library, a simple to use, yet powerful Python library developed to forecast into the future with time series data.

So what are you waiting for. Learn how to work with your time series data and forecast the future.

We'll see you inside the course.

What's inside

Learning objectives

Pandas for data manipulation
Numpy and python for numerical processing
Pandas for data visualization
How to work with time series data with pandas

Use statsmodels to analyze time series data
Use facebook's prophet library for forecasting
Understand advanced arima models for forecasting

Pandas for data manipulation
Numpy and python for numerical processing
Pandas for data visualization
How to work with time series data with pandas
Use statsmodels to analyze time series data
Use facebook's prophet library for forecasting
Understand advanced arima models for forecasting

Syllabus

Introduction

Course Overview - PLEASE DO NOT SKIP THIS LECTURE

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Course Curriculum Overview

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Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Provides foundational skills that are critical for data analysis and visualization in Python

Covers a comprehensive range of time series analysis techniques

Incorporates hands-on exercises and projects to reinforce learning

Taught by an experienced instructor with extensive knowledge in the field

Requires some prior knowledge of Python and data analysis concepts

Utilizes a combination of video lectures, readings, and quizzes to enhance understanding

Reviews summary

Excellent python time series analysis

Learners say this course provides an excellent and practical guide to Python for time series data analysis. Students frequently praise the clear explanations provided by the instructor and the inclusion of useful code examples and hands-on exercises that help solidify understanding. The curriculum is noted for its comprehensive coverage of diverse forecasting models, ranging from classical methods like ARIMA and SARIMAX to cutting-edge techniques including Recurrent Neural Networks and Facebook's Prophet library. While the course is seen as highly applicable to real-world problems, some reviewers mention that a prior solid understanding of Python and Pandas is beneficial due to the course's potentially fast pace in certain sections. Overall, it's considered a strong resource for professionals.

Assumes prior Python/Pandas skills.

"Make sure you're comfortable with Python and Pandas before starting."

"The pace requires you to have prerequisites in place."

"A strong Python foundation is highly recommended."

Skills are directly applicable to work.

"I was able to apply what I learned to my job immediately."

"Provides practical skills for real-world time series problems."

"The techniques taught are highly relevant for data analysis roles."

Hands-on exercises are very helpful.

"The code examples are practical and easy to implement."

"I learned the most from the hands-on coding sections."

"The exercises help solidify the concepts."

Covers many forecasting techniques.

"Covers ARIMA, Prophet, and RNNs, offering a broad perspective."

"It introduces a good variety of modern and traditional models."

"I appreciate the comprehensive coverage of different methods."

Instructor is very clear and effective.

"The instructor explains concepts clearly and makes complex topics understandable."

"His explanations are excellent and easy to follow."

"I found the lectures lucid and well-structured."

Moves quickly through some topics.

"Sometimes the course moves quite fast."

"Be prepared to pause and rewatch lectures."

"I felt some sections went too quickly, especially the theoretical parts."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Python for Time Series Data Analysis with these activities:

Create cheat sheet of key concepts and formulas

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Summarize important concepts and formulas for quick reference and improved memorization.

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Identify and list down essential concepts and formulas
Craft a visually appealing and concise cheat sheet
Regularly review and update cheat sheet as needed

Solve practice problems and exercises regularly

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Enhance comprehension and problem-solving skills by practicing with various exercises.

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Find practice problems and exercises from textbooks, online resources, or assignments
Set aside dedicated time for solving practice problems
Analyze and understand the solutions to identify areas for improvement

Explore online tutorials and resources for time series analysis

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Expand knowledge and gain different perspectives on time series analysis techniques.

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Identify reputable online platforms and resources
Select tutorials that align with course topics and learning objectives
Follow tutorials and complete exercises to reinforce understanding
Engage with online forums and discussion groups to seek further clarification and insights

Four other activities

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Create simple time series forecasting models

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Apply theoretical knowledge to practical applications and develop hands-on experience.

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Choose a simple time series dataset
Apply time series analysis techniques to build forecasting models
Validate and evaluate model performance
Refine and improve models based on evaluation results

Form study groups with classmates

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Foster collaboration, knowledge sharing, and peer support.

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Identify a group of classmates with diverse skills and perspectives
Establish regular meeting times and discussion topics
Actively participate in discussions, share insights, and collaborate on problem-solving

Develop a final project that showcases time series analysis skills

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Demonstrate comprehensive understanding and mastery of time series analysis principles.

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Identify a complex time series dataset
Apply advanced time series analysis techniques to model and forecast the dataset
Create a comprehensive report detailing the project, methodology, and results
Present the project findings to the class or in a public forum

Share knowledge and mentor junior students in time series analysis

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Strengthen knowledge retention and communication skills while supporting others.

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Identify opportunities to assist junior students with time series analysis concepts
Provide guidance, answer questions, and share resources
Create study materials or organize workshops to share knowledge

Career center

Learners who complete Python for Time Series Data Analysis will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists forecast using relative statistical models and make data driven recommendations. This course covers the NumPy library for Numerical Processing and covers AutoCorrelation, Partial AutoCorrelation, ARIMA, SARIMA, and SARIMAX modeling. It also covers how to work with time-stamped data using Pandas and Python, which is the foundation of time series analysis in any domain.

See salaries and explore the career path for Data Scientist

Market Researcher

Market Researchers often use time series analysis to predict future trends in their industry. This course provides a great foundation in NumPy for Numerical Processing, Pandas for Data Manipulation and Visualization, covering handling time series data. The course conitinues on to explore the use of Statsmodels for analyzing Time Series data with advanced ARIMA models including SARIMA and SARIMAX.

See salaries and explore the career path for Market Researcher

Financial Analyst

Financial Analysts regularly analyze financial trends in time series to predict future market trends. This course helps build a foundation in NumPy for Numerical Processing, Pandas for Data Manipulation and Visualization. The course also goes on to cover advanced ARIMA models including SARIMA and SARIMAX using Statsmodels, which is very useful for Financial Analysts in their domain.

See salaries and explore the career path for Financial Analyst

Business Analyst

Business Analysts will be able to use their skills in time-series forecasting to make critical business decisions. This course covers the NumPy library for Numerical Processing and covers AutoCorrelation, Partial AutoCorrelation, ARIMA, SARIMA, and SARIMAX modeling. It also covers how to work with time-stamped data using Pandas and Python, which is incredibly useful for time series analysis.

See salaries and explore the career path for Business Analyst

Quantitative Analyst

Quantitative Analysts use data to make investment decisions. While not always using time series data, it can be quite helpful when trying to predict future financial trends. This course helps build a foundation in NumPy for Numerical Processing, Pandas for Data Manipulation and Visualization. The course also goes on to cover advanced ARIMA models including SARIMA and SARIMAX using Statsmodels, which is very useful for Quantitative Analysts.

See salaries and explore the career path for Quantitative Analyst

Product Manager

Product Managers need to be able to understand how to forecast future trends to make decisions on product development strategy. This course provides a great foundation in NumPy for Numerical Processing, Pandas for Data Manipulation and Visualization, covering handling time series data. The course conitinues on to explore the use of Statsmodels for analyzing Time Series data with advanced ARIMA models including SARIMA and SARIMAX.

See salaries and explore the career path for Product Manager

Operations Research Analyst

Operations Research Analysts use mathematical techniques and data to help businesses make effective decisions. This course provides a great foundation in NumPy for Numerical Processing, Pandas for Data Manipulation and Visualization, covering handling time series data. The course conitinues on to explore the use of Statsmodels for analyzing Time Series data with advanced ARIMA models including SARIMA and SARIMAX, which is helpful to predict future trends.

See salaries and explore the career path for Operations Research Analyst

Statistician

Statisticians use data to make informed decisions. This course provides a great foundation in NumPy for Numerical Processing, Pandas for Data Manipulation and Visualization, covering handling time series data. The course conitinues on to explore the use of Statsmodels for analyzing Time Series data with advanced ARIMA models including SARIMA and SARIMAX, which is helpful to predict future trends.

See salaries and explore the career path for Statistician

Machine Learning Engineer

Machine Learning Engineers build and maintain systems that use data to make predictions and recommendations. This course provides a great foundation in NumPy for Numerical Processing, Pandas for Data Manipulation and Visualization, covering handling time series data. The course conitinues on to explore the use of Statsmodels for analyzing Time Series data with advanced ARIMA models including SARIMA and SARIMAX, which is helpful to build machine learning models.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

Data Analysts preprocess, clean, and visualize data to derive meaningful insights. This course provides a great foundation in NumPy for Numerical Processing, Pandas for Data Manipulation and Visualization, covering handling time series data. It also covers Deep Learning in the context of time series with TensorFlow and Keras, which is valuable for those in Data Analysis.

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Software Engineer

Software Engineers design, develop, and maintain computer programs. While this is mostly not in the context of time series data, there are times when time series can be useful. This course provides a great foundation in NumPy for Numerical Processing, Pandas for Data Manipulation and Visualization, covering handling time series data.

See salaries and explore the career path for Software Engineer

Computer Scientist

Computer Scientists conduct research in computer science and develop new technologies. While this is mostly not in the context of time series data, there are times when time series can be useful. This course provides a great foundation in NumPy for Numerical Processing, Pandas for Data Manipulation and Visualization, covering handling time series data.

See salaries and explore the career path for Computer Scientist

Data Engineer

Data Engineers build and maintain systems and tools to store, process, and analyze large amounts of data. While this is mostly not in the context of time series data, there are times when time series can be useful. This course provides a great foundation in NumPy for Numerical Processing, Pandas for Data Manipulation and Visualization, covering handling time series data.

See salaries and explore the career path for Data Engineer