Elite Financial Prediction Toolkit - Python Data Science for Stock Markets
Created by John Bura
01
Skill level
All levels
02
Sections
5
03
Lectures
66
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Instructor
John Bura
What's inside
This course includes.
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5
Sections
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66
Lectures
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65
Resources
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Certificate of completion
Included
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Mobile and desktop access
Included
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AI learning assistance
Included
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Course content
Curriculum & lectures.
+ 01 Stock Market Data Analysis and Visualization with Python, Pandas, NumPy, Seaborn and Matplotlib 10 lectures
00 Project Preview
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01 Fetch Stock Data
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02 Visualize Stock Data Features
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03 Calculate Daily Return
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04 Compare Returns Of Different Stocks
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05 Compare Closing Prices
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06 Visualize Standard Deviation And Expected Returns
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07 Calculate Value At Risk
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08 Monte Carlo Analysis To Estimate Risk
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09 Visualize Price Distribution
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+ 02 Build a Stock Ticker Dashboard Web App with Python, Dash and Pandas 5 lectures
01 Project Preview
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02 Import Stock Data
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03 Build A Dash Web App
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04 Build Stock And Date Range Pickers
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05 Show Stock Data In The Web App
Locked
+ 03 Algorithmic Trading with Python, Statistics and Pandas - Build Investing Strategies 25 lectures
01 Make An API Call
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02 Project Preview
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03 Convert Data To A Pandas Dataframe
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04 Batch API Calls To Improve Performance
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05 Calculate The Number Of Shares To Buy
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06 Build An Excel File From The Pandas Dataframe
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07 Project 2 Preview
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08 Make An API Call
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09 Execute A Batch API Call
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10 Remove Low Momentum Stocks
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11 Calculate New Number Of Shares To Buy
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12 Find High Quality Momentum Stocks
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13 Calculate Momentum Percentiles
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14 Find The 50 Best Value Stocks
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15 Calculate New Number Of Shares To Buy
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16 Build An Excel File
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17 Project 3 Preview
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18 Build A Dataframe
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19 Remove Glamour Stocks
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20 Calculate The Number Of Shares To Buy
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21 Build A Composite Of Valuation Metrics
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22 Clean Dataframe
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23 Calculate Value Percentiles
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24 Find The 50 Best Momentum Stocks
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25 Calculate New Number Of Shares To Buy
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+ 04 Predict Stock Trends with Twitter Sentiment Analysis ML 16 lectures
01 Fetch Twitter Sentiment And Stock Prices Datasets
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02 Merge Datasets Into Dataframe
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03 Project Preview
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04 What Is The Random Forest Classifier Model
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05 Processing And Sorting Sentiment Data
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06 Process Stock And Sentiment Dataframe
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07 Calculate Stock Trend (Rising Or Falling)
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08 Build A Binary Encoding Of Sentiment
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09 Split And Scale Data
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10 Build A Random Forest Classifier Model
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11 Evaluate The Model
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12 Project Preview
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13 What Is Gradient Boosting
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14 Test Different Learning Rates
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15 Make A Prediction With A Gradient Boosting Classifier
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16 Evaluate The Model
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+ 05 Feature Analysis and Data Science with Stocks for Beginners 10 lectures
00 Course Overview
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01 Load And Create Data
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02 Perform Exploratory Data Analysis
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03 Visualize Data With Different Plots
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04 Analyze Features With More Plots
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05 Build Plots With Seaborn
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06 Build A Bokeh Plot
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07 Build A 3D Scatter Plot
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08 Rank Feature Importance
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09 Compare Positive And Negative Returns
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Instructors
Taught by people who ship.
John Bura
Founder and CEO of Mammoth Club and Course Pro, the #1 AI-powered Learning Management System for course and content development, training and evaluation.
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