Practical Financial Data Analysis With Python Data Science
- Description
- Curriculum
- FAQ
- Reviews
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!
-
6Introduction to PandasVideo lesson
-
7Read in CSV DataVideo lesson
-
8Read in Excel DataVideo lesson
-
9Read in HTML DataVideo lesson
-
10Basic Data Exploration With PandasVideo lesson
-
11Basic Data Handling With Conditional StatementsVideo lesson
-
12Drop Column/RowVideo lesson
-
13Merging and Joining DataVideo lesson
-
14Getting Stock Market Data From YahooVideo lesson
-
15Convert Pandas Datareader to Pandas Dataframe FormatVideo lesson
-
16Historical Stock Data From Yahoo FinanceVideo lesson
-
17Welcome to QuandlVideo lesson
-
18Accessing Quandl in PythonVideo lesson
-
19Accessing Financial Data Via ffnVideo lesson
-
23Principles of Data VisualizationVideo lesson
-
24Prep Up the Time Series DataVideo lesson
-
25Line Charts For Examining Temporal DataVideo lesson
-
26Plotting Multiple Lines on the Same ChartVideo lesson
-
27Histograms-Visualize the Distribution of Continuous Numerical VariablesVideo lesson
-
28Visualise the Daily ReturnsVideo lesson
-
29Visualize the Daily Percent ChangeVideo lesson
-
30Visualize the Cumulative ReturnsVideo lesson
-
31Correlation Between StocksVideo lesson
-
32Correlation Betwen Present and FutureVideo lesson
-
33Visualize the Relationship Between Multiple StocksVideo lesson
-
34Another Way of Correlation VisalizationVideo lesson
-
35Candlesticks VisualizationVideo lesson
-
36Moving Averages/Rolling MeansVideo lesson
-
37More Moving AveragesVideo lesson
-
38Different Components of Time Series DataVideo lesson
-
39Test For Stationarity: ADF Test TheoryVideo lesson
-
40Implement the ADF Test in PythonVideo lesson
-
41Make Your Time Series StationaryVideo lesson
-
42Other Ways Of Making Time Series Data StationaryVideo lesson
-
43Theory Behind Exponential SmoothingVideo lesson
-
44Smooth Exponential Smoothing-PrimerVideo lesson
-
45How Good is SES For Forecasting?Video lesson
-
46Holt's Linear Method For ForecastingVideo lesson
-
47Theory Behind ARIMAVideo lesson
-
48Implement Practical ARIMA For Time Series ForecastingVideo lesson
-
49What Is Machine Learning?Video lesson
-
50Setting Up the Analysis in Facebook's ProphetVideo lesson
-
51Implement the Prophet ModelVideo lesson
-
52Use Prophet to Forecast to the FutureVideo lesson
-
53Prophet ResultsVideo lesson
-
54Theory of k-NN (k-Nearest Neighbours)Video lesson
-
55kNN Regression Predictive ModelVideo lesson
-
56More KNNVideo lesson
-
57Theory of Random Forests (RF)Video lesson
-
58Implement RF Regression For ForecastingVideo lesson
-
59Ordinary Linear Squares (OLS) Regression-TheoryVideo lesson
-
60Implement OLS For ForecastingVideo lesson
External Links May Contain Affiliate Links read more