Python for Business Data Analytics & Intelligence
in Graphic Design
Created by
Yihua Xiu
About this course
Description
What is Business Data Analytics? Why learn Business Analytics? What does a Business Data Analyst do?
Good questions, we're glad you asked!
We now live in a data-driven economy and companies around the world are in a race to make the best data-driven decisions.
Enter Business Data Analysts (a.k.a. future you).
Being a Business Analyst is like being a detective.
You use tools (like Python, Facebook Prophet, Google Causal Impact) to investigate and analyze data to understand the past and predict what is most likely to happen in the future. From there, you'll determine the best course of action to take.
Companies need these Analysts because they're able to turn data into money.
They use the tools and techniques (that we teach you in this course) to quickly interpret and analyze data and turn it into actionable information and insights. These insights are relied upon to make key business decisions.
And making the right decision can be the difference between gaining or losing millions of dollars.
That's why people with these data analysis skills are extremely in-demand. And why companies are willing to pay great salaries to attract them.
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1 Section 1 - Introduction
2 Parts
Python for Business Analytics & Intelligence
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The Modern Day Business Analyst
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15 Section 11 - Random Forest
18 Parts
CASE STUDY - Credit Cards #2 (Briefing)
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CHALLENGE - Introduction
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CHALLENGE - Solutions (Part 1)
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CHALLENGE - Solutions (Part 2)
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Parameter Tuning
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Python - Classification Report and F1 score
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Python - Directory and Libraries
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Python - Feature Importance
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Python - Isolate X and Y
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Python - Loading Data
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Python - Parameter Grid
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Python - Parameter Tuning
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Python - Random Forest Model
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Python - Summary Statistics
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Python - Training and Test Set
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Python - Transform Object into Numerical Variables
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Random Forest - Game Plan
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Random Forest Quirks
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13 Section 10 - Gaussian Mixture
16 Parts
AIC and BIC
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CASE STUDY - Credit Cards #1 (Briefing)
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CHALLENGE - Introduction
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CHALLENGE - Solutions
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Clustering
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Gaussian Mixture - Game Plan
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Gaussian Mixture Model
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My Experience with Segmentation
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Python - Cluster Prediction and Assignment
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Python - Directory and Data
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Python - Gaussian Mixture Model
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Python - Interpretation
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Python - Load Data
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Python - Load Data
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Python - Optimal Number of Clusters
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Python - Transform Character variables
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12 Section 9 - RFM (Recency, Frequency, Monetary) Analysis
19 Parts
CASE STUDY - Online Shopping (Briefing)
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CHALLENGE - Introduction
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CHALLENGE - Solutions
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Python - Applying RFM Function
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Python - Creating Sales Variable
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Python - Customer Level Aggregation
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Python - Date Variable
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Python - Directory and Libraries
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Python - Directory and Libraries
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Python - Loading Data
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Python - Monetary Variable
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Python - Quartiles
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Python - Results Summary
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Python - RFM Function
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Python - RFM Score
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Python - Tidying up Dataframe
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RFM - Game Plan
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RFM Model
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Value Based Segmentation
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10 Section 8 - Matching
21 Parts
CASE STUDY - Catholic Schools & Standardized Tests (Briefing)
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CHALLENGE - Introduction
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CHALLENGE - Solutions
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Common Support Region
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Matching
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Matching - Game Plan
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My Experience with Matching
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Python - Chi-square Loop
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Python - Chi-square Test
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Python - Cleaning and Preparing Dataset
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Python - Common Support Region Visualization
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Python - Comparing Means
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Python - Directory and Libraries
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Python - Loading Data
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Python - Logistic Regression and Debugging
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Python - Matching
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Python - Other Variables
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Python - Outcome Visualization
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Python - Preparing for Common Support Region
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Python - Race Variable Transformation
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Python - Robustness Check - Removing 1 confounder
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9 Section 7 - Google Causal Impact (Econometrics and Causal Inference)
18 Parts
Assumptions
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Causal Impact Step-by-Step
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CHALLENGE - Solutions
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Difference-in-Differences Framework
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Google Causal Impact - Game Plan
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Interpretation of Results
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Python - Bitcoin Price loading
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Python - Correlation
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Python - Defining Dates
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Python - Google Causal Impact
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Python - Google Causal Impact Setup
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Python - Impact Results
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Python - Installing and Importing Libraries
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Python - Load Control Groups
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Python - Preparing DataFrame
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Python - Preparing for Correlation Matrix
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Time Series Data
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Why Econometrics and Causal Inference
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7 Section 6 - Logistic Regression
19 Parts
CASE STUDY - Spam Emails (Briefing)
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CHALLENGE - Introduction
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CHALLENGE - Solutions
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Confusion Matrix
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How to Read Logistic Regression Coefficients
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Logistic Regression
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Logistic Regression - Game Plan
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Python - Classification Report
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Python - Confusion Matrix
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Python - Function to Read Coefficients
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Python - Histogram and Outlier Removal
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Python - Manual Accuracy Assessment
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Python - Manual Accuracy Assessment
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Python - Predictions
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Python - Prepare X and Y
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Python - Preparing Script and Loading Data
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Python - Summary Statistics
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Python - Training and Test Set
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Python - Transforming Dependent Variable
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6 Section 5 - Multilinear Regression
20 Parts
CASE STUDY - Professors' Salary (Briefing)
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CHALLENGE - Introduction
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CHALLENGE - Solutions
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Outliers
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Python - Accuracy Assessment
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Python - Adding Constant
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Python - Categorical Variables
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Python - Correlation Matrix
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Python - Creating Dummy Variables
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Python - For Loop
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Python - Isolate X and Y
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Python - Model Predictions
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Python - Model Predictions
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Python - Multilinear Regression
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Python - Plotting Continuous Variables
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Python - Preparing Script and Loading Data
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Python - Summary Statistics
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Python - Train and Test Split
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The Concept of Multilinear Regression
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Training and Test Set
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5 Section 4 - Linear Regression
11 Parts
CASE STUDY - Diamonds (Briefing)
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Dummy Variable Trap
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Linear Regression
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Linear Regression - Game Plan
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Linear Regression Output
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Python - Dummy Variable
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Python - Dummy Variable
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Python - Isolate X and Y
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Python - Linear Regression Model and Summary
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Python - Plotting Regression
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Python - Preparing Script and Loading Data
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4 Section 3 - Intermediary Statistics
21 Parts
CASE STUDY - Remote Work Predictions (Briefing)
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CASE STUDY - Wine Quality (Briefing)
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Chi-square test
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Confidence Interval
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EXERCISE - Python - Chi-square
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EXERCISE - Python - Confidence Interval
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EXERCISE - Python - Normal Distribution
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EXERCISE - Python - Standard Error
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EXERCISE - Python - T-test
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Intermediary Statistics - Game Plan
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Normal Distribution
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P-Value
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Powerposing and p-hacking
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Powerposing and p-hacking
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Python - Chi-square test
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Python - Confidence Interval
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Python - Normal Distribution Visualization
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Python - Preparing Script and Loading Data
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Python - Shapiro-Wilks Test
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Shapiro-Wilks Test
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Standard Error of the Mean
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3 Section 2 - Basic Statistics
17 Parts
Arithmetic Mean
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Basic Statistics - Game Plan
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CASE STUDY - Moneyball
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CASE STUDY - Moneyball (Briefing)
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Correlation
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EXERCISE - Python - Correlation
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EXERCISE - Python - Mean
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EXERCISE - Python - Median
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EXERCISE - Python - Mode
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EXERCISE - Python - Standard Deviation
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Median and Mode
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Python - Correlation
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Python - Directory, Libraries and Data
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Python - Mean
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Python - Median
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Python - Mode
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Standard Deviation
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16 Section 12 - Facebook Prophet
21 Parts
Additive vs. Multiplicative Seasonality
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CASE STUDY - Wikipedia (Briefing)
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CHALLENGE - Introduction
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CHALLENGE - Solutions (Part 1)
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CHALLENGE - Solutions (Part 2)
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CHALLENGE - Solutions (Part 3)
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Cross-validation
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Dynamic Holidays
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Facebook Prophet - Game Plan
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Facebook Prophet
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Facebook Prophet Model
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Facebook Prophet Parameters
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Forecasting at Uber
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Parameters to tune
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Python - Accuracy Assessment
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Python - Future Dataframe
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Python - Loading Data
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Python - Parameter Grid
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Python - Parameter Tuning
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Python - Regressor Coefficients
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Python - Training and Test Set
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