Practical Financial Investment Analysis in R and tidyquant
What you'll learn
- LEARN To Obtain Real World Financial Data FREE From Yahoo and Quandl
- BE ABLE To Read In, Pre-process & Visualize Financial Data
- LEARN How To Use Different R-based Packages For Financial Analysis (including tidyquant)
- USE Common Financial Analytics Technique For Financial Analysis
Requirements
- Prior Familiarity With The Interface Of RStudio and Package Installation
- Be Able To Carry Out Data Reading And Pre-Processing Tasks Such As Data Cleaning In R
- Interest In Working With Quantitative Financial Data
Description
THIS IS YOUR COMPLETE GUIDE TO FINANCIAL DATA ANALYSIS IN R!
This course is your complete guide to analyzing real-world financial data using R All the main aspects of analyzing financial data- statistics, data visualization, time series analysis and machine learning will be covered in 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 proficiently analysing financial 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.
Throughout my research, I realised almost all the R data science courses and books out there do not account for the multidimensional nature of the topic.
So, unlike other instructors, I dig deep into the data science features of R and give you a one-of-a-kind grounding in data science-related topics!
With this course, in around 3 hours, you’ll get familiar with some of the most common R packages for obtaining, cleaning, visualizing and analyzing financial data for making data-driven decisions (either for yourself or your company)
Among other things:
· Obtain long-term stock market data from platforms such as Yahoo and Quandl
· Learn to visualize temporal financial data and produce clear graphs and visualizations
· Implement standard analysis techniques, including moving averages and the Sharpe ratio.
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 the R Programming Language.
My course will help you implement REAL DATA methods 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 and financial analysis packages in RStudio
You’ll understand the underlying concepts to understand what algorithms and methods best suit 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!
Who this course is for:
- Anyone Who Wants To Become Proficient In Financial Data Analysis Working With Real Life Data
- Anyone Who Wants Master Financial Data Analysis In RStudio
- Anyone Who Wants To Become An Expert Financial Data Scientist
Instructor
- 4.4 Instructor Rating
- 21,013 Reviews
- 116,797 Students
- 54 Courses
I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics.
I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing and given guest lectures on prestigious forums such as Open Data Science Conference (ODSC).