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Jose Portilla
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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/rtgh1s/python
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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
Quick Check In!
Course Curriculum Overview
Read more
Syllabus
Introduction
Course Overview - PLEASE DO NOT SKIP THIS LECTURE
Quick Check In!
Course Curriculum Overview
Read more
FAQ - Frequently Asked Questions
Let's get you up and running!
Installing Anaconda Python Distribution and Jupyter
Let's learn the basics of Numpy and Python!
NumPy Section Overview
NumPy Arrays - Part One
NumPy Arrays - Part Two
NumPy Indexing and Selection
NumPy Operations
NumPy Exercises
NumPy Exercise Solutions
Let's learn how to analyze data with Python using Pandas!
Introduction to Pandas
Series
DataFrames - Part One
DataFrames - Part Two
Missing Data with Pandas
Group By Operations
Common Operations
Data Input and Output
Pandas Exercises
Pandas Exercises Solutions
Let's see the built in capabilities of Pandas Data Visualization!
Overview of Capabilities of Data Visualization with Pandas
Visualizing Data with Pandas
Customizing Plots created with Pandas
Pandas Data Visualization Exercise
Pandas Data Visualization Exercise Solutions
Let's learn how to work with Time Series data with Pandas
Overview of Time Series with Pandas
DateTime Index
DateTime Index Part Two
Time Resampling
Time Shifting
Rolling and Expanding
Visualizing Time Series Data
Visualizing Time Series Data - Part Two
Time Series Exercises - Set One
Time Series Exercises - Set One - Solutions
Time Series with Pandas Project Exercise - Set Two
Time Series with Pandas Project Exercise - Set Two - Solutions
Let's see how we can use the Statsmodels library with Python to work with Time Series Data!
Introduction to Time Series Analysis with Statsmodels
Introduction to Statsmodels Library
ETS Decomposition
EWMA - Theory
EWMA - Exponentially Weighted Moving Average
Holt - Winters Methods Theory
Holt - Winters Methods Code Along - Part One
Holt - Winters Methods Code Along - Part Two
Statsmodels Time Series Exercises
Statsmodels Time Series Exercise Solutions
Let's see how to use Statsmodels to Forecast Time Series with Python!
Introduction to General Forecasting Section
Introduction to Forecasting Models Part One
Evaluating Forecast Predictions
Introduction to Forecasting Models Part Two
ACF and PACF Theory
ACF and PACF Code Along
ARIMA Overview
Autoregression - AR - Overview
Autoregression - AR with Statsmodels
Descriptive Statistics and Tests - Part One
Descriptive Statistics and Tests - Part Two
Descriptive Statistics and Tests - Part Three
ARIMA Theory Overview
Choosing ARIMA Orders - Part One
Choosing ARIMA Orders - Part Two
ARMA and ARIMA - AutoRegressive Integrated Moving Average - Part One
ARMA and ARIMA - AutoRegressive Integrated Moving Average - Part Two
SARIMA - Seasonal Autoregressive Integrated Moving Average
SARIMAX - Seasonal Autoregressive Integrated Moving Average Exogenous - PART ONE
SARIMAX - Seasonal Autoregressive Integrated Moving Average Exogenous - PART TWO
SARIMAX - Seasonal Autoregressive Integrated Moving Average Exogenous - PART 3
Vector AutoRegression - VAR
VAR - Code Along
VAR - Code Along - Part Two
Vector AutoRegression Moving Average - VARMA
Vector AutoRegression Moving Average - VARMA - Code Along
Forecasting Exercises
Forecasting Exercises - Solutions
Let's learn how to use Tensorflow, Keras, Python , and Deep Learning for Time Series!
Introduction to Deep Learning Section
Perceptron Model
Introduction to Neural Networks
Keras Basics
Recurrent Neural Network Overview
LSTMS and GRU
Keras and RNN Project - Part One
Keras and RNN Project - Part Two
Keras and RNN Project - Part Three
Keras and RNN Exercise
Keras and RNN Exercise Solutions
BONUS: Multivariate Time Series with RNN
Quick check
Good to know
Good to know
Know what's good ,
what to watch for , 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
Save this course
Save Python for Time Series Data Analysis to your list so you can find it easily later:
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Reviews summary
Well-explained
Students say that Jose объясняет explains and teaches very well. All the notebooks are completed with further reading links and information. This increases the quality of each lesson. One student says that it's a very recomendable course.
Jose explains and teaches very well.
"Jose explains and teach very well"
All the notebooks are completed with further reading links and information.
"all the notebooks are completed with futher reading links and information"
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
Show steps
Summarize important concepts and formulas for quick reference and improved memorization.
Show steps
-
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
Show steps
Enhance comprehension and problem-solving skills by practicing with various exercises.
Show steps
-
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
Show steps
Expand knowledge and gain different perspectives on time series analysis techniques.
Show steps
-
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
Expand to see all activities and additional details
Show all seven activities
Create simple time series forecasting models
Show steps
Apply theoretical knowledge to practical applications and develop hands-on experience.
Show steps
-
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
Show steps
Foster collaboration, knowledge sharing, and peer support.
Show steps
-
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
Show steps
Demonstrate comprehensive understanding and mastery of time series analysis principles.
Show steps
-
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
Show steps
Strengthen knowledge retention and communication skills while supporting others.
Show steps
-
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
Create cheat sheet of key concepts and formulas
Show steps
Summarize important concepts and formulas for quick reference and improved memorization.
Show steps
Show steps
Show steps
- 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
Show steps
Enhance comprehension and problem-solving skills by practicing with various exercises.
Show steps
Show steps
Show steps
- 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
Show steps
Expand knowledge and gain different perspectives on time series analysis techniques.
Show steps
Show steps
Show steps
- 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
Expand to see all activities and additional details
Show all seven activities
Create simple time series forecasting models
Show steps
Apply theoretical knowledge to practical applications and develop hands-on experience.
Show steps
Show steps
Show steps
- 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
Show steps
Foster collaboration, knowledge sharing, and peer support.
Show steps
Show steps
Show steps
- 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
Show steps
Demonstrate comprehensive understanding and mastery of time series analysis principles.
Show steps
Show steps
Show steps
- 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
Show steps
Strengthen knowledge retention and communication skills while supporting others.
Show steps
Show steps
Show steps
- 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 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.
Market Researcher
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.
Financial Analyst
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.
Business 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.
Quantitative 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.
Product Manager
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.
Operations Research Analyst
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.
Statistician
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.
Machine Learning Engineer
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.
Data Analyst
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.
Software Engineer
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.
Computer Scientist
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.
Data Engineer
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.
For more career information including salaries, visit:
OpenCourser.com/course/rtgh1s/python
Reading list
We've selected six books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Python for Time Series Data Analysis.
Save
Is commonly used as a textbook for time series analysis courses. It comprehensive reference for time series analysis theory and methods. It uses the R statistical software package to demonstrate the methods.
Save
Practical guide to forecasting methods. It covers a wide range of forecasting techniques, from simple to advanced. It is written in a clear and concise style, and it is packed with real-world examples.
Classic textbook on time series analysis. It provides a clear and concise introduction to the subject. It is written in a conversational style, and it is packed with examples and exercises.
Classic textbook on time series analysis in Russian. It provides a clear and concise introduction to the subject. It is written in a conversational style, and it is packed with examples and exercises.
Practical guide to time series analysis using the R statistical software package. It covers a wide range of topics, including data exploration, forecasting, and model selection.
Gentle introduction to time series analysis for social scientists. It covers a wide range of topics, from data collection to forecasting. It is written in a clear and concise style, and it is packed with examples and exercises.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/rtgh1s/python
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Effort
15.5 total hours
Via
Udemy
Instructor
Jose Portilla
Language
English
Good to know
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
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