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Updated in [February 21st, 2023]

What does this course tell?
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Time Series Analysis is a major component of a Data Scientist’s job profile and the average salary of an employee who knows Time Series is 18 lakhs per annum in India and $110k in the United States. So, it becomes a necessity for you to master time series analysis, if you want to get that high-profile data scientist job.

This full course on Time Series Analysis will be taught by Dr Abhinanda Sarkar. Dr Sarkar is the Academic Director at Great Learning for Data Science and Machine Learning Programs. He is ranked amongst the Top 3 Most Prominent Analytics & Data Science Academicians in India.

He has taught applied mathematics at the Massachusetts Institute of Technology (MIT) as well as been visiting faculty at Stanford and ISI and continues to teach at the Indian Institute of Management (IIM-Bangalore) and the Indian Institute of Science (IISc).

Thus, keeping in mind, the importance of time series analysis, we have come up with this Full-course:


We consider the value of this course from multiple aspects, and finally summarize it for you from three aspects: personal skills, career development, and further study:
(Kindly be aware that our content is optimized by AI tools while also undergoing moderation carefully from our editorial staff.)
Time Series Analysis Time Series Forecasting Time Series Analysis in R PhD (Stanford)

This full course on Time Series Analysis will be taught by Dr Abhinanda Sarkar, the Academic Director at Great Learning for Data Science and Machine Learning Programs. He has taught applied mathematics at the Massachusetts Institute of Technology (MIT) as well as been visiting faculty at Stanford and ISI and continues to teach at the Indian Institute of Management (IIM-Bangalore) and the Indian Institute of Science (IISc).



This course on Time Series Analysis is designed to help learners gain a comprehensive understanding of the fundamentals of time series analysis and forecasting. It will provide learners with the necessary skills to apply time series analysis in their data science projects and to pursue a career in data science.


By taking this course, learners can develop their skills in time series analysis and forecasting, which can open up a range of career opportunities in data science. Learners can also use this course as a stepping stone to pursue a PhD in Time Series Analysis from Stanford.


Learners can supplement their learning by taking related courses such as Machine Learning, Statistics, and Data Visualization. They can also explore other topics such as Time Series Modeling, Time Series Forecasting, and Time Series Analysis in R. Additionally, learners can practice their skills by working on real-world datasets.

[Applications]
It is suggested that those who have completed this course on Time Series Analysis apply their knowledge to real-world problems. They can use the skills they have acquired to analyze and forecast time series data in R and use the insights to make informed decisions. Additionally, they can also pursue a PhD in Time Series Analysis from Stanford to further their knowledge and expertise in the field.

[Career Paths]
1. Data Scientist: Data Scientists use time series analysis to identify patterns and trends in data, which can be used to make predictions and inform decisions. They use a variety of tools and techniques, such as machine learning, statistical analysis, and data mining, to analyze large datasets. As the demand for data-driven insights continues to grow, the demand for data scientists with expertise in time series analysis is also increasing.

2. Business Analyst: Business Analysts use time series analysis to identify trends in customer behavior, market conditions, and other factors that can help inform business decisions. They use a variety of tools and techniques, such as predictive analytics, to analyze large datasets and identify patterns and trends. As the demand for data-driven insights continues to grow, the demand for business analysts with expertise in time series analysis is also increasing.

3. Research Analyst: Research Analysts use time series analysis to identify patterns and trends in data, which can be used to inform research projects. They use a variety of tools and techniques, such as machine learning, statistical analysis, and data mining, to analyze large datasets. As the demand for data-driven insights continues to grow, the demand for research analysts with expertise in time series analysis is also increasing.

4. PhD in Time Series Analysis: A PhD in Time Series Analysis is a research-based degree that focuses on the application of time series analysis to solve real-world problems. Students in this program will learn advanced techniques for analyzing time series data, such as machine learning, statistical analysis, and data mining. As the demand for data-driven insights continues to grow, the demand for PhDs with expertise in time series analysis is also increasing.