The ‘Python for Time Series Data Analysis’ course will teach you how to use Python, Pandas, Numpy, and Statsmodels for Time Series Forecasting and Analysis. By the end of this course, you can also understand advanced Arima models for Forecasting. This course even covers Facebook’s Prophet library, powerful Python library developed to forecast into the future with time series data.
This course will teach you everything you need to know to use Python for forecasting time series data to predict new future data points. The course is usually available for INR 2,799 on Udemy but you can click on the link and get the ‘Python for Time Series Data Analysis’ for INR 449.
Who all can opt for this course?
- Python developers who are curious about time series data forecasting
Course Highlights
| Key Highlights | Details |
|---|---|
| Registration Link | Apply Now! |
| Price | INR 449 ( |
| Duration | 15 Hours |
| Rating | 4.6/5 |
| Student Enrollment | 38,960 students |
| Instructor | Jose Portilla https://www.linkedin.com/in/joseportilla |
| Topics Covered | NumPy, Time Series with Pandas, General Forcasting Models, Facebook’s Prophet library. |
| Course Level | Intermediate |
| Total Student Reviews | 7,144 |
Learning Outcomes
- For Data Manipulation, use Pandas
- Python and NumPy are used for numerical processing
- Data visualisation with Pandas
- How to Use Pandas to Work with Time Series Data
- Time series data analysis with Statsmodels
- Make predictions using Facebook’s Prophet Library
- Recognize sophisticated Arima forecasting models
Course Content
| S.No. | Module (Duration) | Topics |
|---|---|---|
| 1. | Introduction (13 minutes) | Course Overview – PLEASE DO NOT SKIP THIS LECTURE |
| Course Overview Check | ||
| Course Curriculum Overview | ||
| FAQ – Frequently Asked Questions | ||
| 2. | Course Set Up and Install (15 minutes) | Installing Anaconda Python Distribution and Jupyter |
| 3. | NumPy (47 minutes) | NumPy Section Overview |
| NumPy Arrays – Part One | ||
| NumPy Arrays – Part Two | ||
| NumPy Indexing and Selection | ||
| NumPy Operations | ||
| NumPy Exercises | ||
| NumPy Exercise Solutions | ||
| 4. | Pandas Overview (01 hour 26 minutes) | 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 | ||
| 5. | Data Visualization with Pandas (42 minutes) | 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 | ||
| 6. | Time Series with Pandas (01 hour 41 minutes) | 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 | ||
| 7. | Time Series Analysis with Statsmodels (01 hour 28 minutes) | 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 | ||
| 8. | General Forecasting Models (05 hours 46 minutes) | 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 | ||
| 9. | Deep Learning for Time Series Forecasting (02 hours 12 minutes) | 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 on MultiVariate Time Series Notebook and Data | ||
| BONUS: Multivariate Time Series with RNN | ||
| 10. | Facebook’s Prophet Library (46 minutes) | Overview of Facebook’s Prophet Library |
| Facebook’s Prophet Library | ||
| Facebook Prophet Evaluation | ||
| Facebook Prophet Trend | ||
| Facebook Prophet Seasonality | ||
| 11. | BONUS SECTION: THANK YOU! (10 seconds) | BONUS LECTURE |
Resources Required
- Basic Python Skills (knowledge up to functions)
Featured Review
Leonel Q. (5/5) : Good explanation of the main algorithms on Time Series analysis, a little bit short on Prophet, but good summary overall.
Pros
- Snigdha Ghosh (5/5) : This is the best tutorial on Time – Series Data Analysis!
- Snigdha Cheekoty (5/5) : This is an excellent course! Covers A-Z basics of time series modelling/forecasting.
- Colby Wilkinson (5/5) : Perfect balance of theory and practical application for an applied data scientist.
- Ken Devonald (5/5) : This course gave an excellent overview and in-depth explanation of Time Series.
Cons
- Mustafa ER (2/5) : The libraries used are outdated, theory sections are not explanatory and the creators of the course seem that they didnt even bother to update the course content.
- Rakshit Sinha (1/5) : The course was extremely outdated (except the RNN and Prophet part)
- Priyabrata Thatoi (2/5) : Presentation for Deep Learning should be used with a pointer, it was very difficult to follow what is being said.
- Dissorial Mogortevo (1/5) : It was only after I went through approximately 33% of the course that the author finally started using .csv datasets instead of creating random numpy and pandas arrays.
About the Author
The instructor of this course is Jose Portilla who is a Head of Data Science at Pierian Training. With 4.6 Instructor Rating and 1,022,369 Reviews on Udemy, he/she offers 60 Courses and has taught 3,290,752 Students so far.
- Jose Marcial Portilla holds degrees in mechanical engineering from Santa Clara University (BS and MS), and he has years of experience working as a qualified instructor and trainer for Python programming, machine learning, and data science
- He has written articles and received patents in a number of disciplines, including data science, materials science, and microfluidics
- He has acquired a set of abilities for data analysis throughout the course of his career, and he wants to combine both his teaching and data science knowledge to educate others the power of programming, how to analyse data, and how to display the data in attractive visualisations
- He currently serves as the Head of Data Science for Pierian Training, where he trains people at prestigious organisations like General Electric, Cigna, The New York Times, Credit Suisse, McKinsey, and others in data science and python programming on-site
- Please click the website link to learn more about the available training options
Comparison Table
| Parameters | Python for Time Series Data Analysis | Time Series Analysis in Python 2023 | Python for Financial Analysis and Algorithmic Trading |
|---|---|---|---|
| Offers | INR 449 ( | INR 455 ( | INR 455 ( |
| Duration | 15.5 hours | 7.5 hours | 16.5 hours |
| Rating | 4.6 /5 | 4.6 /5 | 4.3 /5 |
| Student Enrollments | 38,960 | 14,581 | 115,114 |
| Instructors | Jose Portilla | 365 Careers | Jose Portilla |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
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