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Description

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!
Who this course is for:
Anyone Who Wants Master Financial Data Analysis In Python
Anyone Who Wants To Become Proficient In Financial Data Analysis Working With Real Life Data
People Interested in Applying Machine Learning Techniques to Financial Data
Anyone Who Wants To Become An Expert Data Scientist

What you'll learn

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

Requirements

  • You will need a copy of Adobe XD 2019 or above. A free trial can be downloaded from Adobe.
  • No previous design experience is needed.
  • No previous Adobe XD skills are needed.

Course Content

27 sections • 95 lectures
Expand All Sections
1-Introduction To the Course
5
1.1-Welcome To The Course
1.2-Data and Scripts Used in the Course
1.3-Introduction to the Python Data Science Environment
1.4-Upgraded Python3 Installation
1.5-Introduction to iPython/Jupyter
2-Read in and Preprocess Data From External Data Sources
8
2.1-Introduction to Pandas
2.2-Read in CSV Data
2.3-Read in Excel Data
2.4-Read in HTML Data
2.5-Basic Data Exploration With Pandas
2.6-Basic Data Handling With Conditional Statements
2.7-Drop Column/Row
2.8-Merging and Joining Data
3-Accessing Financial Data
6
3.1-Getting Stock Market Data From Yahoo
3.2-Convert Pandas Datareader to Pandas Dataframe Format
3.3-Historical Stock Data From Yahoo Finance
3.4-Welcome to Quandl
3.5-Accessing Quandl in Python
3.6-Accessing Financial Data Via ffn
4-Preprocessing Time Series Data in Python
3
4.1-Some Date Specific Python Functions
4.2-An Example of Time Series Data in Python
4.3-More Details on Datetime
5-Important Visualization Techniques For Financial Data
13
5.1-Principles of Data Visualization
5.2-Prep Up the Time Series Data
5.3-Line Charts For Examining Temporal Data
5.4-Plotting Multiple Lines on the Same Chart
5.5-Histograms-Visualize the Distribution of Continuous Numerical Variables
5.6-Visualise the Daily Returns
5.7-Visualize the Daily Percent Change
5.8-Visualize the Cumulative Returns
5.9-Correlation Between Stocks
5.10-Correlation Betwen Present and Future
5.11-Visualize the Relationship Between Multiple Stocks
5.12-Another Way of Correlation Visalization
5.13-Candlesticks Visualization
6-Basic Time Series For Deriving Patterns and Forecasts From Financial Data
13
6.1-Moving Averages/Rolling Means
6.2-More Moving Averages
6.3-Different Components of Time Series Data
6.4-Test For Stationarity: ADF Test Theory
6.5-Implement the ADF Test in Python
6.6-Make Your Time Series Stationary
6.7-Other Ways Of Making Time Series Data Stationary
6.8-Theory Behind Exponential Smoothing
6.9-Smooth Exponential Smoothing-Primer
6.10-How Good is SES For Forecasting?
6.11-Holt's Linear Method For Forecasting
6.12-Theory Behind ARIMA
6.13-Implement Practical ARIMA For Time Series Forecasting
7-Machine Learning For Financial Data Forecasting
12
7.1-What Is Machine Learning?
7.2-Setting Up the Analysis in Facebook's Prophet
7.3-Implement the Prophet Model
7.4-Use Prophet to Forecast to the Future
7.5-Prophet Results
7.6-Theory of k-NN (k-Nearest Neighbours)
7.7-kNN Regression Predictive Model
7.8-More KNN
7.9-Theory of Random Forests (RF)
7.10-Implement RF Regression For Forecasting
7.11-Ordinary Linear Squares (OLS) Regression-Theory
7.12-Implement OLS For Forecasting
8-Deep Learning Based Forecasting
8
8.1-Some Theoretical Concepts
8.2-What Is Keras?
8.3-Install Keras On Windows
8.4-Install Keras On Mac
8.5-Implement Keras Based LSTM On Stock Data
8.6-Tackling Unseen Values
8.7-Posit On POSIT
8.8-Distributed Computing