Python for Time Series Data Analysis

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 HighlightsDetails
Registration LinkApply Now!
PriceINR 449 (INR 2,79985% off
Duration15 Hours
Student Enrollment38,960 students
InstructorJose Portilla
Topics CoveredNumPy, Time Series with Pandas, General Forcasting Models, Facebook’s Prophet library.
Course LevelIntermediate
Total Student Reviews7,144

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
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
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

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.


  • 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.


  • 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

ParametersPython for Time Series Data AnalysisTime Series Analysis in Python 2023Python for Financial Analysis and Algorithmic Trading
OffersINR 449 (INR 2,799) 85% offINR 455 (INR 3,499) 87% offINR 455 (INR 3,499) 87% off
Duration15.5 hours7.5 hours16.5 hours
Rating4.6 /54.6 /54.3 /5
Student Enrollments38,96014,581115,114
InstructorsJose Portilla365 CareersJose Portilla
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