Time series analysis is an important technique used in various fields such as finance, economics, meteorology, and engineering. It involves analyzing data that changes over time to uncover patterns or trends that can aid in making predictions or forecasting future values. With the increasing availability of online learning platforms, there are various options for individuals seeking to enhance their knowledge and skills in time series analysis. In this article, we will explore some of the best courses available online for individuals interested in learning about time series analysis.
Here’s a look at the Best Time Series Analysis Courses and Certifications Online and what they have to offer for you!
10 Best Time Series Analysis Courses and Certifications Online
- 10 Best Time Series Analysis Courses and Certifications Online
- 1. Time Series Analysis and Forecasting using Python by Start-Tech Academy (Udemy) (Our Best Pick)
- 2. Time Series Analysis Real World Projects in Python by Shan Singh (Udemy)
- 3. Python for Time Series Data Analysis by Jose Portilla (Udemy)
- 4. Time Series Analysis in Python 2022 by 365 Careers (Udemy)
- 5. Complete Time Series Analysis With Python by Minerva Singh (Udemy)
- 6. Forecasting and Time Series Analysis in Tableau by R-Tutorials Training (Udemy)
- 7. Time Series Analysis, Forecasting, and Machine Learning by Lazy Programmer Team, Lazy Programmer Inc. (Udemy)
- 8. Complete Time Series Data Analysis Bootcamp In R by Minerva Singh (Udemy)
- 9. Python for Time Series Analysis and Forecasting by R-Tutorials Training (Udemy)
- 10. Applied Time Series Analysis in Python by Marco Peixeiro (Udemy)
This course titled “Time Series Analysis and Forecasting using Python” is being offered by Start-Tech Academy. It aims to teach individuals about time series analysis and forecasting models in Python, covering topics such as time data visualization, AR, MA, ARIMA, regression, and ANN. Upon completion of the course, individuals will have the skills to implement time series forecasting and analysis models such as AutoRegression, Moving Average, ARIMA, SARIMA, and multivariate time series forecasting models based on linear regression and neural networks. The course is designed to help students confidently practice, discuss, and understand different time series forecasting, analysis models, and Python time series techniques used by organizations. A verifiable Certificate of Completion is presented to all students who complete the course on time series analysis and Python time series applications.
The course is suitable for business managers or executives, or students who want to learn and apply forecasting models to real-world problems. The course content is structured to teach concepts through how-to examples, with theoretical concepts and use cases of different forecasting models, time series forecasting, and analysis. Step-by-step instructions are provided to implement time series forecasting models in Python, along with downloadable code files and class notes to revise and practice the concepts. Practical classes where models are created for each of these strategies differentiate the course from others available online.
The course instructors are Abhishek and Pukhraj, managers in a global analytics consulting firm, who have used their experience to include practical aspects of marketing and data analytics in the course. They have in-depth knowledge of time series forecasting, analysis, and Python time series techniques. The course creators are also responsible for some of the most popular online courses, with over 170,000 enrollments and thousands of 5-star reviews.
The course promises to teach students time series forecasting, analysis, and Python time series techniques to help businesses make data-driven decisions for production schedules, inventory management, manpower planning, and other parts of the business.
The Time Series Analysis Real World Projects in Python course is designed for individuals interested in advancing their knowledge and skills in data science, AI, and time series analysis and forecasting. The course is suitable for those looking for employment in quantitative financial analysis, data analytics, data science, or those seeking to take their careers to the next level as seasoned AI practitioners.
The course aims to provide practical, easy, and fun learning experiences for students with real-world datasets. The goal is to equip students with the knowledge of the key aspects of data science and time series applications in business.
The course is structured into four tasks, with each task dedicated to solving real business problems. Task one focuses on predicting temperature using time series analysis algorithms. Task two is centered on predicting COVID-19 cases using Facebook Prophet. Task three aims to predict stock prices using automated time series models.
The course covers a comprehensive range of topics, including an introduction to time series analysis algorithms, predicting temperature using time series analysis algorithms, predicting COVID-19 cases using Facebook Prophet, and predicting stock prices using automated time series models. There is also a bonus section that provides students with additional resources on time series analysis algorithms.
Overall, the Time Series Analysis Real World Projects in Python course is a comprehensive resource for individuals looking to learn how to use Python programming language for time series analysis.
The Python for Time Series Data Analysis course, led by Jose Portilla, is designed to equip learners with the necessary skills to utilize Python, Pandas, Numpy, and Statsmodels for time series forecasting and analysis. The course starts with an introduction to working with and manipulating data using the NumPy and Pandas libraries with Python. It then delves deeper into Pandas, covering visualizations and working with timestamped data. The course covers the powerful built-in Time Series Analysis Tools of the statsmodels library, including Error-Trend-Seasonality decomposition and basic Holt-Winters methods.
In the heart of the course, learners will explore general forecasting models, including AutoCorrelation and Partial AutoCorrelation charts, ARIMA-based models, Seasonal ARIMA models, and SARIMAX to include Exogenous data points. Learners will also learn about state-of-the-art deep learning techniques with Recurrent Neural Networks that use deep learning to forecast future data points.
The course even covers Facebook’s Prophet library, a simple yet powerful Python library that is developed for forecasting into the future with time series data. The course is comprehensive and designed for learners of all levels, with a bonus section that provides a thank you message. Enrollees will gain the necessary skills to work with their time series data and forecast the future.
The Time Series Analysis in Python 2022 course, instructed by 365 Careers, is a comprehensive and practical training program that provides the necessary skills for quantitative finance analysts, data analysts, and data scientists. The course covers various quantitative methods for real-life business cases, such as forecasting a bank’s loan portfolio performance or estimating an investment manager’s stock portfolio risk. The course offers an easy-to-understand, packed-with-exercises, and resources curriculum that is not only timeless but also to the point.
The course first explores the fundamental time series theory and then employs Python libraries, such as NumPy, matplotlib, and StatsModels, to train students on the widely used time series models, such as AR, MA, ARMA, ARIMA, ARIMAX, SARIA, SARIMA, SARIMAX, ARCH, GARCH, and VARMA.
The course also provides supplementary materials, including quiz questions, exercise, notebook files, and course notes, to help students understand and apply the concepts to real-life cases. Additionally, students receive active Q&A support, access to future updates, and a certificate of completion upon finishing the course.
To ensure that students have a thorough understanding of time series and its applications, the course offers a 30-day money-back guarantee, with no risks for the students. The course is divided into sections, including Introduction, Setting Up the Environment, Introduction to Time Series in Python, Creating a Time Series Object in Python, and Forecasting – to name a few.
Overall, the Time Series Analysis in Python 2022 course offers a comprehensive and practical training program for individuals interested in mastering time series and its applications.
The Complete Time Series Analysis with Python course, taught by Minerva Singh, provides a comprehensive guide to time series analysis using Python. The course covers all aspects of analyzing temporal data in depth and aims to eliminate the need for other courses or books on Python-based data analysis. By mastering time series data analysis in Python, learners can gain a competitive edge in the era of big data and advance their careers.
Minerva Singh is an expert data scientist with over five years of experience analyzing real-life data using data science techniques. Unlike many other Python data science courses, Singh’s course focuses on the multidimensional nature of the topic, providing learners with a one-of-a-kind grounding in data science related topics. The course covers data reading and cleaning and progresses to the implementation of powerful statistical and machine learning algorithms for analyzing time series data.
The course introduces learners to powerful Python-based packages for time series analysis, commonly used techniques, visualization methods, and machine/deep learning techniques that can be applied to time series data. Learners will also learn to apply these frameworks to real-life data, including temporal stocks and financial data. No prior Python, statistics, or machine learning knowledge is required.
The course starts by teaching learners the most valuable Python data science basics and techniques, using easy-to-understand, hands-on methods to simplify the most difficult concepts in Python. Learners will implement methods using real data from different sources, allowing them to apply Python-based data science in real-life situations. After completing the course, learners will be able to use common time series packages in Python and understand the underlying concepts to determine the best algorithms and methods for their data.
The course includes sections on introduction, reading in data from external sources, preprocessing and visualizing time series data in Python, characteristics and conditions of time series data, basic time series forecasting, machine learning for time series, using deep learning for time series data, and miscellaneous lectures.
The course titled “Forecasting and Time Series Analysis in Tableau” is designed to teach users how to handle time series data in Tableau, generate forecasts, and add R functionality to enhance Tableau. The course covers the rules and implications of time-based data, as well as the exponential smoothing forecasting tool offered by Tableau. Participants will also learn how to integrate the R forecast package into Tableau for ARIMA modeling, even without prior R knowledge.
The course is divided into several sections, beginning with general knowledge about working with time series data, including data roles, moving averages, time-based filtering, and creating parameters. The first section is reinforced with practical exercises. The second section focuses on forecasting with Tableau’s exponential smoothing tool, and participants will learn how to manually modify the forecast settings and interpret the results. The last section covers advanced forecasting using R integration in Tableau.
Participants should have some prior knowledge of Tableau, including how to import data and navigate the interface. R skills are not required, as the course outlines the methods in a way that is easy to follow. The course is recommended for those who want to learn valuable skills in the latest technologies, which can boost their career.
The course is divided into four sections: introduction, working with time series in Tableau, creating a forecast using Tableau’s internal toolbox, and custom forecasting using R integration in Tableau. The course includes practical exercises to reinforce the learning objectives.
7. Time Series Analysis, Forecasting, and Machine Learning by Lazy Programmer Team, Lazy Programmer Inc. (Udemy)
The Time Series Analysis, Forecasting, and Machine Learning course focuses on modern developments in the field, such as deep learning, time series classification, and more. The course covers various techniques, including ETS and Exponential Smoothing, Holt’s Linear Trend Model, ARIMA, SARIMA, SARIMAX, and Auto ARIMA, as well as machine learning models and deep learning models. Applications covered include time series forecasting of sales data, stock prices and stock returns, and smartphone data to predict user behavior. The VIP version of the course covers additional topics such as AWS Forecast, GARCH, and Facebook Prophet.
The course includes a welcome section, a getting set up section, and sections covering time series basics, Exponential Smoothing and ETS Methods, ARIMA, Vector Autoregression, machine learning methods, and various deep learning models such as Artificial Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks. The VIP version of the course includes additional sections on GARCH, AWS Forecast, and Facebook Prophet.
Participants will receive lifetime access to the course, a certificate of completion, and the skills to use the latest time series analysis techniques. The course is suitable for beginners and those with some experience with Python coding. Participants also have access to extra help with Python coding for beginners and effective learning strategies for machine learning. A FAQ section is provided for further support.
The “Complete Time Series Data Analysis Bootcamp In R” course is a comprehensive guide to analyzing temporal data using R. The course covers all aspects of time series analysis in depth, eliminating the need for additional courses or books on the subject. Proficiency in analyzing time series data using R can provide businesses with a competitive edge and boost one’s career.
The course is taught by Minerva Singh, an expert data scientist with over five years of experience in analyzing real-life data using data science techniques. Singh provides a unique grounding in data science-related topics, including data reading, cleaning, and implementing statistical and machine learning algorithms for analyzing time series data. Students will be introduced to powerful R-based packages for time series analysis, commonly used techniques, visualization methods, and machine/deep learning techniques. They will also learn to apply these frameworks to real-life data, including temporal stocks and financial data.
No prior R or statistics/machine learning knowledge is required to take the course. Singh uses easy-to-understand, hands-on methods to simplify even the most difficult concepts in R. The course includes real data obtained from different sources, allowing students to implement R-based data science in real-life. Students will have access to all the code and data used in the course.
The course is divided into several sections, including an introduction to key concepts and software tools, starting with time series data, important pre-conditions of time series modelling, time series-based forecasting, machine learning techniques for time series data, detecting sudden changes/major events, and miscellaneous lectures. Students will learn the underlying concepts to understand which algorithms and methods are best suited for their data.
Overall, the “Complete Time Series Data Analysis Bootcamp In R” course is a valuable resource for anyone looking to analyze temporal data using statistical modelling and machine learning techniques in R.
The Python for Time Series Analysis and Forecasting course is designed to teach statistical programming for time-related data using Python. The course covers time series analysis, forecasting, and predictive analytics. The course aims to provide learners with the skills to understand patterns in time series data, model the data, and make forecasts.
The course emphasizes the importance of time series analysis and forecasting in decision making, especially as companies collect more data. The course is structured to cover the general idea behind time series analysis and forecasting, statistical methods used for time series, and how to read time series charts. Learners will also learn different models and how to set them up in Python for forecasting and predictive analytics, such as ARIMA, exponential smoothing, seasonal decomposition, and simple models.
Time series analysis and forecasting can be applied in nearly any field, including econometrics, finance, academia, medicine, business, and marketing. However, the material can be quite technical and requires prior knowledge in maths and Python. The course is designed to make modeling and forecasting as intuitive and simple as possible. While some knowledge in maths and Python is necessary, the course is meant for people without a background in quantitative fields.
Learners are advised to have prior knowledge of handling standard tasks in Python, but the course content is structured to be accessible to anyone dealing with time data regularly. The course is divided into sections covering Introduction, Time Series Analysis Background Knowledge, ARIMA for Univariate, Non-Seasonal Data, Models for Seasonal Data, Multivariate Time Series Analysis, and Homework Solutions.
The Applied Time Series Analysis in Python course is taught by Marco Peixeiro and combines statistical and deep learning techniques for time series analysis. The course covers basic concepts of time series, including stationarity, augmented Dicker-Fuller test, seasonality, white noise, random walk, autoregression, moving average, ACF and PACF, and model selection with AIC. The course then moves on to more complex statistical models such as ARIMA, SARIMA, and SARIMAX, as well as multiple time series forecasting with VAR, VARMA, and VARMAX models.
In the deep learning section, Tensorflow is used to apply different deep learning techniques for time series analysis, including simple linear models, DNN, CNN, LSTM, CNN + LSTM models, ResNet, and autoregressive LSTM. Students will complete more than 5 end-to-end projects in Python with all source code available.
The course is broken down into Introduction, Statistical Learning Approach: The Building Blocks, Statistical Learning Approach: Basic Models, Statistical Learning Approach: Advanced Models, Deep Learning Approach: Theory, Deep Learning Approach: End-to-end Project, Conclusion and References. In addition, students will have access to a bonus section on Automated Time Series Analysis with Prophet.