Practical Financial Investment Analysis in R and tidyquant

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Minerva Singh

3:02:57

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1 - Welcome To the CoursePractical Financial Analysis in R

1 - Introduction To Course.mp4

02:20

2 - Data and Code.html

3 - Install R and RStudio.mp4

06:36

4 - Different Data Types.mp4

03:37

2 - Preliminary Data Processing in R

5 - Read CSV and Excel Data.mp4

09:56

6 - Read in Data from Online HTML TablesPart 1.mp4

04:13

7 - Read in Data from Online HTML TablesPart 2.mp4

06:24

8 - Editing Data Within R.mp4

02:31

9 - Data Cleaning in R.mp4

17:12

10 - More Data Cleaning.mp4

08:05

11 - Introduction to dplyr for Data SummarizingPart 1.mp4

06:11

12 - Introduction to dplyr for Data SummarizingPart 2.mp4

04:44

3 - Obtaining Financial Data in R

13 - Lets Get the Tesla Stock Prices.mp4

03:25

14 - Obtaining More Stocks and Data.mp4

04:01

15 - Getting Started With Quandl.mp4

02:48

16 - Quandl Financial Data.mp4

05:02

17 - Get Cryptocurrency Data.mp4

03:17

4 - Basic Visualisations and Processing Financial Data With Tidyquant

18 - Data VisualisationConceptual Foundations.mp4

09:33

19 - Basic Visualisations For Financial Data.mp4

04:17

20 - Some More Basic Visualisations.mp4

04:19

21 - What Are the Returns.mp4

08:14

22 - Visualise The Returns.mp4

09:53

23 - Stock Data At Different Time Scales.mp4

08:39

5 - Analysing Financial Performance

24 - Simple Moving Average SMA.mp4

11:19

25 - Moving average convergencedivergence MACD.mp4

06:14

26 - Lets Look At the Annual Returns.mp4

03:10

27 - Identify Most Frequent Large Positive Returns.mp4

04:39

28 - What Is the Sharpe Ratio.mp4

01:59

29 - Identify RiskAdjusted Returns With the Sharpe Ratio.mp4

08:03

30 - Compare To a Baseline.mp4

06:50

31 - Capital Asset Pricing Model CAPM.mp4

05:26

Description

Conduct Financial Analysis and Portfolio Assessment With RStudio and tidyquant. Obtain & Work With Real Financial Data

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

Who is this 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

What You Need to Know?

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

More details

Description
THIS IS YOUR COMPLETE GUIDE TO FINANCIAL DATA ANALYSIS IN R!
This course is yourÂ complete guideÂ to analyzing real-world financial data using R All the main aspectsÂ 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. Â
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 dataÂ inÂ R, you can give your company a competitive edge and boost your career to the next level.
Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â
LEARN FROM AN EXPERT DATA SCIENTISTÂ WITH +5 YEARS OF EXPERIENCE:
Hey,Â my name is Minerva Singh,Â and IÂ am an Oxford University MPhil (Geography and Environment), graduate. I recently finished aÂ PhD at Cambridge University.
I have +5 yearsÂ ofÂ 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.
Â Throughout my research, I realised almost all the R data science courses and books out thereÂ do not account for the multidimensional nature of the topic.
So, unlike other instructors, I dig deep into the data scienceÂ features of R and give you a one-of-a-kind grounding inÂ data science-related topics!
With this course, in around 3 hours, youâ€™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:
Â· Obtain long-term stock market data from platforms such as Yahoo and Quandl
Â· Learn to visualize temporal financial data and produce clear graphs and visualizations
Â· Implement standard analysis techniques, including moving averages and the Sharpe ratio.

Youâ€™ll start by absorbing the most valuable R Data Science basics and techniques. I useÂ easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in the R Programming Language.
My course willÂ help youÂ implement REAL DATA methodsÂ 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, youâ€™ll easily use the common time series and financial analysis packages in RStudio
Youâ€™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.Â
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

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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).

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