ZeroToMastery | Python for Business Data Analytics & Intelligence [FCO]
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| 1. Get Free Premium Accounts Daily On Our Discord Server!.txt | 1.3 KB | ||
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| AIC and BIC.mp4 | 5.8 MB | ||
| Accuracy KPIs (Key Performance Indicators).mp4 | 8.6 MB | ||
| Additive vs. Multiplicative Seasonality.mp4 | 6.1 MB | ||
| Arithmetic Mean.mp4 | 6.9 MB | ||
| Assumptions.mp4 | 5.8 MB | ||
| Basic Statistics - Game Plan.mp4 | 2.7 MB | ||
| CASE STUDY - Bitcoin Pricing (Briefing).mp4 | 6.2 MB | ||
| CASE STUDY - Catholic Schools & Standardized Tests (Briefing).mp4 | 3.5 MB | ||
| CASE STUDY - Credit Cards #1 (Briefing).mp4 | 2.6 MB | ||
| CASE STUDY - Credit Cards #2 (Briefing).mp4 | 1.2 MB | ||
| CASE STUDY - Diamonds (Briefing).mp4 | 2.8 MB | ||
| CASE STUDY - Moneyball (Briefing).mp4 | 1.4 MB | ||
| CASE STUDY - Moneyball.mp4 | 7.5 MB | ||
| CASE STUDY - Online Shopping (Briefing).mp4 | 1.6 MB | ||
| CASE STUDY - Professors' Salary (Briefing).mp4 | 1.5 MB | ||
| CASE STUDY - Remote Work Predictions (Briefing).mp4 | 2.3 MB | ||
| CASE STUDY - Spam Emails (Briefing).mp4 | 2.6 MB | ||
| CASE STUDY - Wikipedia (Briefing).mp4 | 2.4 MB | ||
| CASE STUDY - Wine Quality (Briefing).mp4 | 5.4 MB | ||
| CHALLENGE - Introduction.mp4 | 23.7 MB | ||
| CHALLENGE - Solutions (Part 1).mp4 | 56.7 MB | ||
| CHALLENGE - Solutions (Part 2).mp4 | 92.6 MB | ||
| CHALLENGE - Solutions (Part 3).mp4 | 63.7 MB | ||
| CHALLENGE - Solutions.mp4 | 66.5 MB | ||
| Causal Impact Step-by-Step.mp4 | 3.4 MB | ||
| Chi-square test.mp4 | 5.9 MB | ||
| Clustering.mp4 | 4.6 MB | ||
| Common Support Region.mp4 | 14.2 MB | ||
| Confidence Interval.mp4 | 36.1 MB | ||
| Confusion Matrix.mp4 | 15.2 MB | ||
| Correlation Recap and Stationarity.mp4 | 8.1 MB | ||
| Correlation.mp4 | 15.9 MB | ||
| Cross-validation.mp4 | 2.7 MB | ||
| Difference-in-Differences Framework.mp4 | 4.5 MB | ||
| Dummy Variable Trap.mp4 | 7.4 MB | ||
| Dynamic Holidays.mp4 | 4.9 MB | ||
| EXERCISE - Python - Chi-square.mp4 | 17.5 MB | ||
| EXERCISE - Python - Confidence Interval.mp4 | 14 MB | ||
| EXERCISE - Python - Correlation.mp4 | 15 MB | ||
| EXERCISE - Python - Linear Regression.mp4 | 25.3 MB | ||
| EXERCISE - Python - Mean.mp4 | 11.7 MB | ||
| EXERCISE - Python - Median.mp4 | 12.7 MB | ||
| EXERCISE - Python - Mode.mp4 | 11.9 MB | ||
| EXERCISE - Python - Normal Distribution.mp4 | 27 MB | ||
| EXERCISE - Python - Shapiro-Wilks.mp4 | 10.2 MB | ||
| EXERCISE - Python - Standard Deviation.mp4 | 4.2 MB | ||
| EXERCISE - Python - Standard Error.mp4 | 10.6 MB | ||
| EXERCISE - Python - T-test.mp4 | 22.8 MB | ||
| Ensemble Learning and Random Forest.mp4 | 4.3 MB | ||
| Exercise_ Meet Your Classmates and Instructor.pdf | 250.3 KB | ||
| Facebook Prophet - Game Plan.mp4 | 3.2 MB | ||
| Facebook Prophet Model.mp4 | 35.8 MB | ||
| Facebook Prophet Parameters.mp4 | 5.2 MB | ||
| Facebook Prophet.mp4 | 7.5 MB | ||
| Forecasting at Uber.mp4 | 9 MB | ||
| Gaussian Mixture - Game Plan.mp4 | 2.8 MB | ||
| Gaussian Mixture Model.mp4 | 8 MB | ||
| Google Causal Impact - Game Plan.mp4 | 3 MB | ||
| How Decision Trees Work.mp4 | 9.1 MB | ||
| How to Read Logistic Regression Coefficients.mp4 | 6.8 MB | ||
| Intermediary Statistics - Game Plan.mp4 | 1.9 MB | ||
| Interpretation of Results.mp4 | 11.7 MB | ||
| Introduction.mp4 | 25.1 MB | ||
| Linear Regression - Game Plan.mp4 | 3 MB | ||
| Linear Regression Output.mp4 | 34.3 MB | ||
| Linear Regression.mp4 | 10.1 MB | ||
| Logistic Regression - Game Plan.mp4 | 2.9 MB | ||
| Logistic Regression.mp4 | 8.4 MB | ||
| Matching - Game Plan.mp4 | 5.5 MB | ||
| Matching.mp4 | 8.4 MB | ||
| Median and Mode.mp4 | 4.7 MB | ||
| Multilinear Regression - Game Plan.mp4 | 3.3 MB | ||
| My Experience with Matching.mp4 | 5.4 MB | ||
| My Experience with Segmentation.mp4 | 7.2 MB | ||
| Normal Distribution.mp4 | 7.2 MB | ||
| Outliers.mp4 | 4.7 MB | ||
| P-Value.mp4 | 14.5 MB | ||
| Parameter Tuning.mp4 | 5.9 MB | ||
| Parameters to tune.mp4 | 3.2 MB | ||
| Powerposing and p-hacking.mp4 | 10.6 MB | ||
| Python - Accuracy Assessment.mp4 | 21.7 MB | ||
| Python - Adding Constant.mp4 | 4.6 MB | ||
| Python - Applying RFM Function.mp4 | 8.6 MB | ||
| Python - Bitcoin Price loading.mp4 | 19.1 MB | ||
| Python - Black Friday.mp4 | 14.5 MB | ||
| Python - Categorical Variables.mp4 | 18.4 MB | ||
| Python - Chi-square Loop.mp4 | 23.2 MB | ||
| Python - Chi-square Test.mp4 | 38.8 MB | ||
| Python - Chi-square test.mp4 | 47.1 MB | ||
| Python - Classification Report and F1 score.mp4 | 24.9 MB | ||
| Python - Classification Report.mp4 | 15.9 MB | ||
| Python - Cleaning and Preparing Dataset.mp4 | 28.8 MB | ||
| Python - Cluster Prediction and Assignment.mp4 | 12.9 MB | ||
| Python - Combining Events and Preparing Dataframe.mp4 | 15 MB | ||
| Python - Common Support Region Visualization.mp4 | 9.1 MB | ||
| Python - Comparing Means.mp4 | 20 MB | ||
| Python - Confidence Interval.mp4 | 40.5 MB | ||
| Python - Confusion Matrix.mp4 | 27.4 MB | ||
| Python - Correlation Matrix.mp4 | 11.8 MB | ||
| Python - Correlation.mp4 | 17.9 MB | ||
| Python - Creating Dummy Variables.mp4 | 9.3 MB | ||
| Python - Creating Sales Variable.mp4 | 14.3 MB | ||
| Python - Cross-validation.mp4 | 48.7 MB | ||
| Python - Customer Level Aggregation.mp4 | 21.6 MB | ||
| Python - Date Variable.mp4 | 11.5 MB | ||
| Python - Defining Dates.mp4 | 8.6 MB | ||
| Python - Directory and Data.mp4 | 14.8 MB | ||
| Python - Directory and Libraries.mp4 | 13.5 MB | ||
| Python - Directory, Libraries and Data.mp4 | 39.9 MB | ||
| Python - Dummy Variable.mp4 | 14.6 MB | ||
| Python - Easter Holidays.mp4 | 22.9 MB | ||
| Python - Education Variables.mp4 | 44.2 MB | ||
| Python - Feature Importance.mp4 | 19.5 MB | ||
| Python - For Loop.mp4 | 17.6 MB | ||
| Python - Forecasting.mp4 | 21.3 MB | ||
| Python - Function to Read Coefficients.mp4 | 36.3 MB | ||
| Python - Future Dataframe.mp4 | 32.4 MB | ||
| Python - Gaussian Mixture Model.mp4 | 9.1 MB | ||
| Python - Google Causal Impact Setup.mp4 | 9 MB | ||
| Python - Google Causal Impact.mp4 | 38.3 MB | ||
| Python - Histogram and Outlier Removal.mp4 | 44.3 MB | ||
| Python - Impact Results.mp4 | 34.2 MB | ||
| Python - Installing and Importing Libraries.mp4 | 11.9 MB | ||
| Python - Interpretation.mp4 | 104 MB | ||
| Python - Isolate X and Y.mp4 | 14.3 MB | ||
| Python - Linear Regression Model and Summary.mp4 | 16.6 MB | ||
| Python - Load Control Groups.mp4 | 20.6 MB | ||
| Python - Load Data.mp4 | 20.4 MB | ||
| Python - Loading Data.mp4 | 11.2 MB | ||
| Python - Logistic Regression and Debugging.mp4 | 92.7 MB | ||
| Python - Logistic Regression.mp4 | 15.2 MB | ||
| Python - Manual Accuracy Assessment.mp4 | 22.7 MB | ||
| Python - Matching.mp4 | 43.5 MB | ||
| Python - Mean.mp4 | 38.9 MB | ||
| Python - Median.mp4 | 17.4 MB | ||
| Python - Mode.mp4 | 15.6 MB | ||
| Python - Model Predictions.mp4 | 5.9 MB | ||
| Python - Monetary Variable.mp4 | 5.2 MB | ||
| Python - Multilinear Regression.mp4 | 21.4 MB | ||
| Python - Normal Distribution Visualization.mp4 | 36.8 MB | ||
| Python - Optimal Number of Clusters.mp4 | 29.3 MB | ||
| Python - Other Variables.mp4 | 23.8 MB | ||
| Python - Outcome Visualization.mp4 | 9.1 MB | ||
| Python - Parameter Grid.mp4 | 22.7 MB | ||
| Python - Parameter Tuning.mp4 | 47.1 MB | ||
| Python - Plotting Continuous Variables.mp4 | 22 MB | ||
| Python - Plotting Regression.mp4 | 21.6 MB | ||
| Python - Predictions.mp4 | 15.5 MB | ||
| Python - Prepare X and Y.mp4 | 8.8 MB | ||
| Python - Preparing DataFrame.mp4 | 46 MB | ||
| Python - Preparing Script and Loading Data.mp4 | 25.2 MB | ||
| Python - Preparing for Common Support Region.mp4 | 35 MB | ||
| Python - Preparing for Correlation Matrix.mp4 | 15.6 MB | ||
| Python - Quartiles.mp4 | 23 MB | ||
| Python - RFM Function.mp4 | 11.4 MB | ||
| Python - RFM Score.mp4 | 6.6 MB | ||
| Python - Race Variable Transformation.mp4 | 86.1 MB | ||
| Python - Random Forest Model.mp4 | 11.7 MB | ||
| Python - Regressor Coefficients.mp4 | 7.8 MB | ||
| Python - Renaming Variables.mp4 | 9.1 MB | ||
| Python - Results Summary.mp4 | 14.9 MB | ||
| Python - Robustness Check - Removing 1 confounder.mp4 | 29.4 MB | ||
| Python - Robustness Check - Repeated experiments.mp4 | 57.4 MB | ||
| Python - Shapiro-Wilks Test.mp4 | 41.8 MB | ||
| Python - Standard Deviation.mp4 | 18.6 MB | ||
| Python - Standard Error.mp4 | 25.9 MB | ||
| Python - Stationarity.mp4 | 46.4 MB | ||
| Python - Summary Statistics.mp4 | 25.5 MB | ||
| Python - T-Test Loop.mp4 | 23.8 MB | ||
| Python - T-Test.mp4 | 26.2 MB | ||
| Python - T-test.mp4 | 56.8 MB | ||
| Python - Tidying up Dataframe.mp4 | 12.1 MB | ||
| Python - Train and Test Split.mp4 | 8.3 MB | ||
| Python - Training and Test Set.mp4 | 23.7 MB | ||
| Python - Transform Character variables.mp4 | 14.2 MB | ||
| Python - Transform Object into Numerical Variables.mp4 | 9.9 MB | ||
| Python - Transforming Date Variable.mp4 | 17.5 MB | ||
| Python - Transforming Dependent Variable.mp4 | 18.1 MB | ||
| Python - Visualization.mp4 | 30.6 MB | ||
| Python for Business Analytics & Intelligence.mp4 | 169.8 MB | ||
| RFM - Game Plan.mp4 | 2.3 MB | ||
| RFM Model.mp4 | 8.1 MB | ||
| Random Forest - Game Plan.mp4 | 3 MB | ||
| Random Forest Quirks.mp4 | 3.8 MB | ||
| Robustness Checks.mp4 | 4.6 MB | ||
| Setting up the Course Material.mp4 | 34.5 MB | ||
| Shapiro-Wilks Test.mp4 | 4.6 MB | ||
| Standard Deviation.mp4 | 4.3 MB | ||
| Standard Error of the Mean.mp4 | 5.9 MB | ||
| Structural Time Series.mp4 | 6.8 MB | ||
| T-test.mp4 | 5.3 MB | ||
| Thank You!.mp4 | 89 MB | ||
| The Concept of Multilinear Regression.mp4 | 2.9 MB | ||
| The Curse of Dimensionality.mp4 | 5.3 MB | ||
| The Modern Day Business Analyst.mp4 | 11 MB | ||
| Time Series Data.mp4 | 2.9 MB | ||
| Training and Test Set.mp4 | 1.7 MB | ||
| Unconfoundedness.mp4 | 5.2 MB | ||
| Under and Over Fitting.mp4 | 2.8 MB | ||
| Value Based Segmentation.mp4 | 4.8 MB | ||
| What are Econometrics & Causal Inference and why are they important.pdf | 190.7 KB | ||
| What are Predictive Analytics and why are they important.pdf | 186.1 KB | ||
| What are Statistics and why are they important.pdf | 169.6 KB | ||
| What is Segmentation and why is it important.pdf | 208.5 KB | ||
| Why Econometrics and Causal Inference.mp4 | 11.2 MB | ||
| Z-Score.mp4 | 5.8 MB | ||
| ▲ 247 total files | |||
Description
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ZeroToMastery - Python for Business Data Analytics & Intelligence [FCO]
About
Become a top Business Data Analyst. We’ll teach you everything you need to go from a complete beginner to getting hired as an analytics professional. You’ll learn to use Python and the latest industry tools and techniques to make data-driven decisions.
Course Overview
We guarantee you this is the most up-to-date and comprehensive course on learning how to use Python and the latest industry tools and techniques for business data analysis. You'll learn analytics by using real-world data and examples, including the data used in the hit movie Moneyball, to become a top Business Data Analyst and get HIRED this year.
What You'll Learn
- The skills to become a professional Business Analyst and get hired
- Step-by-step guidance from an industry professional
- Learn to use Python for statistics, causal inference, econometrics, segmentaiton, matching, and predictive analytics
- Master the latest data and business analysis tools and techniques including Google Causal Impact, Facebook Prophet, Random Forest and much more
- Participate in challenges and exercises that solidify your knowledge for the real world
- Learn what a Business Analyst does, how they provide value, and why they're in demand
- Analyze real datasets related to Moneyball, wine quality, Wikipedia searches, employee remote work satisfaction, and more
- Learn how to make data-driven decisions
- Enhance your proficiency with Python, one of the most popular programming languages
- Use case studies to learn how analytics have changed the world and help individuals and companies succeed
Are there any prerequisites for this course?
- A computer (Windows, Mac, or Linux) with an internet connection
- Basic Python knowledge. Don't know Python? No problem, you'll get access to our Python Bootcamp here as well where we'll teach you Python from scratch
- A willingness and enthusiasm to learn and take action
Who is this course for?
- Developers that want a step-by-step guide to learn and master Business Data Analytics from scratch all the way to being able to get hired at a top company
- Students who want to go beyond all of the "beginner" Python and Data Analytics tutorials out there
- Developers that want to use their skills in a new discipline
- Programmers who want to learn one of the most in-demand skills
- Developers that want to be in the top 10% of Business Data Analysts
- Students who want to gain experience working on large, interesting datasets
- Bootcamp or online tutorial graduates that want to go beyond the basics
- Students who want to learn from an industry professional with real-world experience, not just another online instructor that teaches off of documentation
Instructor(s)
Hi, I'm Diogo Resende!
Diogo has been working for over a decade as a data scientist. He loves harnessing the power of data and analytics to understand what has happened, what will happen next, and how to use that information to your advantage.
Your instructor (Diogo) isn't just an expert with years of real-world professional experience. He has been in your shoes. He makes learning fun. He makes complex topics feel simple. He will motivate you. He will push you. And he will go above and beyond to help you succeed.
General Info:
Author(s)
: Diogo Resende
Language
: English
Updated
: 2022
Videos Duration
: 15.5 hours
Course Source
: https://zerotomastery.io/courses/learn-business-analytics/
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