Practical Financial Data Analysis With Python Data Science (Udemy.com)
Conduct Financial Analysis With Forecasting & Machine Learning in Python. Obtain & Work With Real Financial Data
Created by: Minerva Singh
Last updated November 2024
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
How it compares
Against the other 376 rated Udemy courses in Finance & Accounting on CourseDuck:
- Rating4.2 out of 5higher than 10% of them
- Reviews194#272 of 377 by number of reviews
- Length6.1 hourslonger than 58% of them
- Last updatedNovember 2024earlier than 60% of them
Course 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!
Instructor Details
- 4.2 Rating
194 Reviews
Minerva Singh
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
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Reviews
By Raymond Roberts on 10/27/2023
Good overview of forecasting modules and techniques. A lot of Jupyter notebooks are included. The material however has some issues. Many instances of outdated code, and there are a couple missing data files. You will have to make adjustments to the code, most are minor fixes. There are more difficult fixes at the end which will require a deeper understanding of the models than is provided here. There are also some blurry videos that students have asked to have fixed, but remain unchanged.
By Luca B on 6/22/2023
Dataset not update, some command in python are obsolete, I spent too much time trying to find the correct one.
By Raveena Sarkar on 11/25/2021
This is an ideal course highlighting the importance and usefulness of Python data science for financial data analysis. The concept is capable of being used for financial data analysis with amazing results. The instructor has impressive knowledge of the subject and her delivery is interesting and engaging.
By Ravi Srivastava on 10/7/2021
An excellent course for analyzing financial data with machine learning in Python. The course is very beneficial and useful for my work. The instructor is simply brilliant in explaining finer details of the concept and has the ability of making the lectures interesting.
By Raj Kumar Majumdar on 9/13/2021
This is ideally structured course for practical data analysis with Python data science. The concept will certainly enhance the quality of my work several notches. The instructor has complete knowledge of the subject and her delivery is engaging.
By Rishilal Yadav on 8/26/2021
This is an amazing course for real data analysis with Python data analysis. The course contains quality contents which have immense potential for practical use. The instructor is knowledgeable about the subject and her delivery is impressive and confident.
By Rajesh Kumar on 7/29/2021
The course encompasses very important tools for analysing real financial data. The concepts of this course can, when applied to real time data, can certainly produce amazing results. the instructor has very effectively brought home salient features of the concepts of the course.
By RKM singh on 7/24/2021
This course deals with very important tools for analysing practical financial data. It will significantly enhance the quality of my work. The knowledge and performance of the instructor is impressive.
By KM Maurya on 1/28/2021
It has been a great learning experience going through lectures of this course. The course has immense potential for practical applicability. the delivery of the instructor is quite engaging.
By Trino Guzman Marquez on 11/3/2020
Content is fine as expected, but the material is incomplete: There are several missing Jupyter Notebooks needed to follow/understand the lectures, there are missing datasets needed to work with existing Jupyter notebooks. Missing information either Jupyter Notebooks or Datasets compromise a right learning experience. Until now I had to locate/download/fabricate over 2 datasets and 6 notebooks using the videos.
Quality Score
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Overall Score : 84 / 100












