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 0k 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.
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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:
These are the topics covered in this full course:
• Types of statistics – 6:18
• What is Time Series Forecasting? – 21:12
• Components of Time Series – 55:23
• Additive Model and Multiplicative Model in Time Series – 1:16:48
• Measures of Forecast Accuracy – 3:04:55
• Exponential Smoothing – 3:47:50
Time Series Analysis explanation :
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Probability and Statistics Full Course: https://www.youtube.com/watch?v=z9siRCCElls
Machine Learning with Python: https://www.youtube.com/watch?v=RnFGwxJwx-0&t=287s
Tableau Training for Beginners: https://www.youtube.com/watch?v=6mBtTNggkUk&t=994s
Python for Data Science: https://www.youtube.com/watch?v=edvg4eHi_Mw&t=17669s
Hadoop Full Course: https://www.youtube.com/watch?v=JK2MdJAWEGc
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A Tool written in Java that performs regression on a dataset sample gotten from https://archive.ics.uci.edu/ml/datasets/Individual+household+electric+power+consumption
The project repo is https://github.com/AdeyemiA/TimeSeriesAnalysis or https://github.com/Sapphirine/TimeSeriesAnalysis
The tool can be adapted for any dataset with only restrictions on the first two columns being the date and time. Video Rating: / 5
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This Time Series Analysis (Part-1) in R tutorial will help you understand what is time series, why time series, components of time series, when not to use time series, why does a time series have to be stationary, how to make a time series stationary and at the end, you will also see a use case where we will forecast car sales for 5th year using the given data.
Link to Time Series Analysis Part-2: https://www.youtube.com/watch?v=Y5T3ZEMZZKs
You can also go through the slides here: https://goo.gl/RsAEB8
A time series is a sequence of data being recorded at specific time intervals. The past values are analyzed to forecast a future which is time-dependent. Compared to other forecast algorithms, with time series we deal with a single variable which is dependent on time. So, lets deep dive into this video and understand what is time series and how to implement time series using R.
Below topics are explained in this “ Time Series in R Tutorial “ –
1. Why time series?
2. What is time series?
3. Components of a time series
4. When not to use time series?
5. Why does a time series have to be stationary?
6. How to make a time series stationary?
7. Example: Forcast car sales for the 5th year
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Become an expert in data analytics using the R programming language in this data science certification training course. You’ll master data exploration, data visualization, predictive analytics and descriptive analytics techniques with the R language. With this data science course, you’ll get hands-on practice on R CloudLab by implementing various real-life, industry-based projects in the domains of healthcare, retail, insurance, finance, airlines, music industry, and unemployment.
Why learn Data Science with R?
1. This course forms an ideal package for aspiring data analysts aspiring to build a successful career in analytics/data science. By the end of this training, participants will acquire a 360-degree overview of business analytics and R by mastering concepts like data exploration, data visualization, predictive analytics, etc
2. According to marketsandmarkets.com, the advanced analytics market will be worth .53 Billion by 2019
3. Wired.com points to a report by Glassdoor that the average salary of a data scientist is 8,709
4. Randstad reports that pay hikes in the analytics industry are 50% higher than IT
The Data Science Certification with R has been designed to give you in-depth knowledge of the various data analytics techniques that can be performed using R. The data science course is packed with real-life projects and case studies, and includes R CloudLab for practice.
1. Mastering R language: The data science course provides an in-depth understanding of the R language, R-studio, and R packages. You will learn the various types of apply functions including DPYR, gain an understanding of data structure in R, and perform data visualizations using the various graphics available in R.
2. Mastering advanced statistical concepts: The data science training course also includes various statistical concepts such as linear and logistic regression, cluster analysis and forecasting. You will also learn hypothesis testing.
3. As a part of the data science with R training course, you will be required to execute real-life projects using CloudLab. The compulsory projects are spread over four case studies in the domains of healthcare, retail, and the Internet. Four additional projects are also available for further practice.
The Data Science with R is recommended for:
1. IT professionals looking for a career switch into data science and analytics
2. Software developers looking for a career switch into data science and analytics
3. Professionals working in data and business analytics
4. Graduates looking to build a career in analytics and data science
5. Anyone with a genuine interest in the data science field
6. Experienced professionals who would like to harness data science in their fields
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