Practical Financial Data Analysis With Python Data Science

Practical Financial Data Analysis With Python Data Science, available at $74.99, has an average rating of 4.3, with 67 lectures, based on 126 reviews, and has 3819 subscribers.

You will learn about 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 This course is ideal for individuals who are Anyone Who Wants Master Financial Data Analysis In Python or Anyone Who Wants To Become Proficient In Financial Data Analysis Working With Real Life Data or People Interested in Applying Machine Learning Techniques to Financial Data or Anyone Who Wants To Become An Expert Data Scientist It is particularly useful for Anyone Who Wants Master Financial Data Analysis In Python or Anyone Who Wants To Become Proficient In Financial Data Analysis Working With Real Life Data or People Interested in Applying Machine Learning Techniques to Financial Data or Anyone Who Wants To Become An Expert Data Scientist.

Enroll now: Practical Financial Data Analysis With Python Data Science

Summary

Title: Practical Financial Data Analysis With Python Data Science

Price: $74.99

Average Rating: 4.3

Number of Lectures: 67

Number of Published Lectures: 67

Number of Curriculum Items: 67

Number of Published Curriculum Objects: 67

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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 isMinerva 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!

Course Curriculum

Chapter 1: Introduction To the Course

Lecture 1: Welcome To The Course

Lecture 2: Data and Scripts Used in the Course

Lecture 3: Introduction to the Python Data Science Environment

Lecture 4: Upgraded Python3 Installation

Lecture 5: Introduction to iPython/Jupyter

Chapter 2: Read in and Preprocess Data From External Data Sources

Lecture 1: Introduction to Pandas

Lecture 2: Read in CSV Data

Lecture 3: Read in Excel Data

Lecture 4: Read in HTML Data

Lecture 5: Basic Data Exploration With Pandas

Lecture 6: Basic Data Handling With Conditional Statements

Lecture 7: Drop Column/Row

Lecture 8: Merging and Joining Data

Chapter 3: Accessing Financial Data

Lecture 1: Getting Stock Market Data From Yahoo

Lecture 2: Convert Pandas Datareader to Pandas Dataframe Format

Lecture 3: Historical Stock Data From Yahoo Finance

Lecture 4: Welcome to Quandl

Lecture 5: Accessing Quandl in Python

Lecture 6: Accessing Financial Data Via ffn

Chapter 4: Preprocessing Time Series Data in Python

Lecture 1: Some Date Specific Python Functions

Lecture 2: An Example of Time Series Data in Python

Lecture 3: More Details on Datetime

Chapter 5: Important Visualization Techniques For Financial Data

Lecture 1: Principles of Data Visualization

Lecture 2: Prep Up the Time Series Data

Lecture 3: Line Charts For Examining Temporal Data

Lecture 4: Plotting Multiple Lines on the Same Chart

Lecture 5: Histograms-Visualize the Distribution of Continuous Numerical Variables

Lecture 6: Visualise the Daily Returns

Lecture 7: Visualize the Daily Percent Change

Lecture 8: Visualize the Cumulative Returns

Lecture 9: Correlation Between Stocks

Lecture 10: Correlation Betwen Present and Future

Lecture 11: Visualize the Relationship Between Multiple Stocks

Lecture 12: Another Way of Correlation Visalization

Lecture 13: Candlesticks Visualization

Chapter 6: Basic Time Series For Deriving Patterns and Forecasts From Financial Data

Lecture 1: Moving Averages/Rolling Means

Lecture 2: More Moving Averages

Lecture 3: Different Components of Time Series Data

Lecture 4: Test For Stationarity: ADF Test Theory

Lecture 5: Implement the ADF Test in Python

Lecture 6: Make Your Time Series Stationary

Lecture 7: Other Ways Of Making Time Series Data Stationary

Lecture 8: Theory Behind Exponential Smoothing

Lecture 9: Smooth Exponential Smoothing-Primer

Lecture 10: How Good is SES For Forecasting?

Lecture 11: Holt's Linear Method For Forecasting

Lecture 12: Theory Behind ARIMA

Lecture 13: Implement Practical ARIMA For Time Series Forecasting

Chapter 7: Machine Learning For Financial Data Forecasting

Lecture 1: What Is Machine Learning?

Lecture 2: Setting Up the Analysis in Facebook's Prophet

Lecture 3: Implement the Prophet Model

Lecture 4: Use Prophet to Forecast to the Future

Lecture 5: Prophet Results

Lecture 6: Theory of k-NN (k-Nearest Neighbours)

Lecture 7: kNN Regression Predictive Model

Lecture 8: More KNN

Lecture 9: Theory of Random Forests (RF)

Lecture 10: Implement RF Regression For Forecasting

Lecture 11: Ordinary Linear Squares (OLS) Regression-Theory

Lecture 12: Implement OLS For Forecasting

Chapter 8: Deep Learning Based Forecasting

Lecture 1: Some Theoretical Concepts

Lecture 2: What Is Keras?

Lecture 3: Install Keras On Windows

Lecture 4: Install Keras On Mac

Lecture 5: Implement Keras Based LSTM On Stock Data

Lecture 6: Tackling Unseen Values

Lecture 7: Posit On POSIT

Instructors

Minerva Singh
Bestselling Instructor & Data Scientist(Cambridge Uni)

Rating Distribution

1 stars: 5 votes
2 stars: 4 votes
3 stars: 11 votes
4 stars: 18 votes
5 stars: 88 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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