4.37 (308 reviews)
☑ Implement Common Data Cleaning And Visualization Techniques In R
☑ Be Able To Read In, Pre-process & Visualize Time Series Data
☑ The Basic Conditions Time Series Data Must Fulfill & How To Check For These
☑ Model Time Series Data To Forecast Future Values
☑ Use Machine Learning Regression For Forecasting Future Values
☑ Detect Sudden Changes In The Values During A Given Time Period
THIS IS YOUR COMPLETE GUIDE TO TIME SERIES DATA ANALYSIS IN R!
This course is your complete guide to time series analysis using R. So, all the main aspects of analyzing temporal data will be covered n depth..
If you take this course, you can do away with taking other courses or buying books on R based data analysis.
In this age of big data, companies across the globe use R to sift through the avalanche of information at their disposal. By becoming proficient in in analyzing time series data in R, you can give your company a competitive edge and boost your career to the next level.
LEARN FROM AN EXPERT DATA SCIENTIST WITH +5 YEARS OF EXPERIENCE:
Hey, my name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University.
I have +5 years of experience in analyzing real life data from different sources using data science related techniques and i have produced many publications for international peer reviewed journals.
Over the course of my research I realized almost all the R data science courses and books out there do not account for the multidimensional nature of the topic .
So, unlike other R instructors, I dig deep into the data science features of R and gives you a one-of-a-kind grounding in data science related topics!
You will go all the way from carrying out data reading & cleaning to to finally implementing powerful statistical and machine learning algorithms for analyzing time series data.
Among other things:
You will be introduced to powerful R-based packages for time series analysis.
You will be introduced to both the commonly used techniques, visualization methods and machine/deep learning techniques that can be implemented for time series data.
& you will 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!
You’ll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R.
My course will help you implement the methods using REAL DATA obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real-life.
After taking this course, you’ll easily use the common time series packages in R...
You’ll even understand the underlying concepts to understand what algorithms and methods are best suited for your data.
We will work with real data and you will have access to all the code and data used in the course.
JOIN MY COURSE NOW!
INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools
Introduction to the Course
Data and Scripts For the Course
Installing R and R Studio
Read in CSV & Excel Data
Remove Missing Values
More Data Cleaning
Exploratory Data Analysis(EDA): Basic Visualizations with R
Start With Time Series Data
Works With Dates in R
Pre-Processing Data With Times
Visualize Temporal Data in R
Components of Time Series Data
Moving Averages (MA) For Visualizing a Trend/Pattern
Detecting Significant Trend
Other Ways Of Identifying Trend in Time Series Data
Visualize Monthly Temporal Data
Identify Cyclical Behavior with Fourier Transforms
Work With Seasonality
Important Pre-Conditions of Time Series Modelling
Is My Time Series Stationary?
Differencing: Make A Non-Stationary Time Series Stationary
Make the Mean & Variance Constant
Detrending Time Series With Linear Regression
Detrending Time Series With Mean Subtraction
Time Series Based Forecasting
Simple Exponential Smoothing for Short Term Forecasts
Other Basic Forecasting Techniques
Moving Averages (MA) For Forecasting
Simple Moving Average
Theta Lines For Forecasting
Forecasting On the Fly
Linear Regression For Predicting Values As a Function of Time
Linear Regression For Forecasting With Trend & Seasonality
Automatic ARIMA Model Fitting and Forecasting
Automatic ARIMA With Real Life Data
ARIMA With Fourier Terms
BATS For Forecasting
Machine Learning Techniques For Time Series Data
Linear Regression With "timetk"
Linear Regression On Real Data
Machine Learning Regression Models for Non-Parametric Data For Forecasting
XGBoost For Time Series Forecasting
Theory Behind ANN (Artificial Neural Network) and DNN (Deep Neural Networks)
Neural Network for Forecasting
RNNs With Temporal Data
Evaluate the Performance of an RNN Model
Detecting Sudden Changes/Major Events
Detect An Anomaly in Time Series Data
Breaks For Additive Season and Trend (BFAST) For Time Series in R
Structural Change Detection
Structural Changes in Forex Regime
Dr. Singh's courses are always very, very good. As a professional forecaster, this is a review for me. However, I wanted to make sure I have a good basis in R for future time series use. I look forward to working on this code as the course progresses! Thank you Dr. Singh, please keep up your great work.
the codes are good to learn something new in this course but for few topics there is no data available
The explanation of the concept is very poor. The parameters of the function are not explained in detailed manner which creates a gap in understanding of the concept and next video.
The course was very good. It reinforced my knowledge of R and provided me tips and knowledge regarding how to best approach a time series analysis problem. I also gained insights on using other approaches and frameworks such as machine learning and h2o. I will most likely go back and review a few sections in more detail to make sure I understand the code. Overall I found the material useful and applicable to the work I am currently doing. There were some minimal annoyances such as code files with ".txt" extensions in the course content. I needed to save them as ".R" files to take full advantage of "R" Studio features but, I consider that minor.
The course needs to be updated. A few of the packages (such as HEAT) do not exist any more. Also some of the filenames are wrong. I found I could use a bit more explanation as to why the instructor picked the syntax/process she did. On the positive side the course does contain a plethora of examples that the student can go through and work out. I know R but I found I had to go to Google and understand a few things regarding the syntax used in the scripts.
Excellent presentation. The material is well organized and explained very well in clear and concise terms.
There are some errors in the course and concepts are not explained clearly. It's necessary to google yourself as you go along with the course. As the complexity increases the amount of explanation decreases.
It was good experience to learn new things. but sometimes i feel more explanation is needed to understand the concepts. Thank you
The course is of good content. I am finding great value. I have one suggestion. The transcript should be in paragraphs. Currently the sentences are broken and it is difficult to read coherently. very impressive and targeted delivery.
The course introduced me towards some additional methods for understanding & predicting time series data. However, the explanation provided in the lectures is pretty limited and rather focused on just the execution part. I was still able to understand the content (for most part) as I had some previous background on time series forecasting in R. For someone new, it would be kind of difficult to understand why we are doing certain steps.
Its starts by focussing on classical time series models before moving to more esoteric data science techniques
This is a very practical course with many examples and useful techniques. A lot of helpful packages are introduced. In the end, one can start to work with time-series data. To go even further (theoretical part), additional research is recommended.
Pathetic narration. It seems that the codes are generated by another person and the narrator has little idea on the subject.
I am very excited because it is a good match. But I need more practical experience to have a better understanding of the course.
Wonderful course! A better understanding of how to undertake time-series analysis on real world problems