Mô tả

THIS IS YOUR COMPLETE GUIDE TO FINANCIAL DATA ANALYSIS IN PYTHON!

This course is your complete guide to analyzing real-world financial data using Python. 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 Python-based data analysis.  

In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal. By becoming proficient in analysing financial data in Python, 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 realised almost all the Python 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 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 finally implementing powerful statistical and machine learning algorithms for analyzing financial data.

Among other things:

  • You will be introduced to powerful Python-based packages for financial data analysis.

  • You will be introduced to both the commonly used techniques, visualization methods and machine/deep learning techniques that can be implemented for financial data.

  • & you will learn to apply these frameworks to real-life data including temporal stocks and financial data.  

NO PRIOR PYTHON OR STATISTICS/MACHINE LEARNING KNOWLEDGE IS REQUIRED!

You’ll start by absorbing the most valuable Python Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in Python.

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 Python-based data science in real-life.

After taking this course, you’ll easily use the common time-series and financial analysis packages in Python...

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!

Bạn sẽ học được gì

LEARN To Obtain Real World Financial Data FREE From Yahoo and Quandl

BE ABLE To Read In, Pre-process & Visualize Time Series Data

IMPLEMENT Common Data Processing And Visualisation Techniques For Financial Data in Python

LEARN How To Use Different Python-based Packages For Financial Analysis

MODEL Time Series Data To Forecast Future Values With Classical Time Series Techniques

USE Machine Learning Regression For Building Predictive Models of Stock prices

LEARN How to Use Facebook's Powerful Prophet Algorithm For Modelling Financial Data

IMPLEMENT Deep learning methods such as LSTM For Forecasting Stock Data

Yêu cầu

  • Prior Familiarity With The Interface Of Jupiter Notebooks and Package Installation
  • Prior Exposure to Basic Statistical Techniques (Such As p-Values, Mean, Variance)
  • Be Able To Carry Out Data Reading And Pre-Processing Tasks Such As Data Cleaning In Python
  • Interest In Working With Time Series Data Or Data With A Time Component To Them

Nội dung khoá học

8 sections

Introduction To the Course

5 lectures
Welcome To The Course
02:20
Data and Scripts Used in the Course
00:04
Introduction to the Python Data Science Environment
10:57
Upgraded Python3 Installation
05:44
Introduction to iPython/Jupyter
19:13

Read in and Preprocess Data From External Data Sources

8 lectures
Introduction to Pandas
12:06
Read in CSV Data
05:42
Read in Excel Data
05:31
Read in HTML Data
05:58
Basic Data Exploration With Pandas
04:30
Basic Data Handling With Conditional Statements
05:24
Drop Column/Row
04:42
Merging and Joining Data
10:47

Accessing Financial Data

6 lectures
Getting Stock Market Data From Yahoo
03:22
Convert Pandas Datareader to Pandas Dataframe Format
03:22
Historical Stock Data From Yahoo Finance
07:56
Welcome to Quandl
03:09
Accessing Quandl in Python
04:56
Accessing Financial Data Via ffn
04:30

Preprocessing Time Series Data in Python

3 lectures
Some Date Specific Python Functions
02:36
An Example of Time Series Data in Python
02:28
More Details on Datetime
02:49

Important Visualization Techniques For Financial Data

13 lectures
Principles of Data Visualization
06:46
Prep Up the Time Series Data
02:36
Line Charts For Examining Temporal Data
03:43
Plotting Multiple Lines on the Same Chart
03:49
Histograms-Visualize the Distribution of Continuous Numerical Variables
05:56
Visualise the Daily Returns
04:24
Visualize the Daily Percent Change
03:42
Visualize the Cumulative Returns
02:41
Correlation Between Stocks
04:53
Correlation Betwen Present and Future
03:31
Visualize the Relationship Between Multiple Stocks
06:02
Another Way of Correlation Visalization
03:16
Candlesticks Visualization
05:50

Basic Time Series For Deriving Patterns and Forecasts From Financial Data

13 lectures
Moving Averages/Rolling Means
05:03
More Moving Averages
06:41
Different Components of Time Series Data
02:54
Test For Stationarity: ADF Test Theory
04:52
Implement the ADF Test in Python
04:13
Make Your Time Series Stationary
05:44
Other Ways Of Making Time Series Data Stationary
05:06
Theory Behind Exponential Smoothing
05:11
Smooth Exponential Smoothing-Primer
05:23
How Good is SES For Forecasting?
09:58
Holt's Linear Method For Forecasting
03:58
Theory Behind ARIMA
06:08
Implement Practical ARIMA For Time Series Forecasting
10:10

Machine Learning For Financial Data Forecasting

12 lectures
What Is Machine Learning?
05:32
Setting Up the Analysis in Facebook's Prophet
06:41
Implement the Prophet Model
03:31
Use Prophet to Forecast to the Future
04:23
Prophet Results
04:22
Theory of k-NN (k-Nearest Neighbours)
05:04
kNN Regression Predictive Model
04:48
More KNN
08:39
Theory of Random Forests (RF)
01:54
Implement RF Regression For Forecasting
06:02
Ordinary Linear Squares (OLS) Regression-Theory
10:44
Implement OLS For Forecasting
04:41

Deep Learning Based Forecasting

7 lectures
Some Theoretical Concepts
05:40
What Is Keras?
03:29
Install Keras On Windows
05:16
Install Keras On Mac
04:19
Implement Keras Based LSTM On Stock Data
07:20
Tackling Unseen Values
05:12
Posit On POSIT
03:31

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