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:
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You will be introduced to powerful Python-based packages for financial data analysis.
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You will be introduced to both the commonly used techniques, visualization methods and machine/deep learning techniques that can be implemented for financial data.
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& 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
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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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