English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 238 Lessons (15h 23m) | 3.11 GB
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.
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
Table of Contents
1 Python for Business Analytics & Intelligence
2 Introduction
3 Setting up the Course Material
4 The Modern Day Business Analyst
5 Basic Statistics – Game Plan
6 Arithmetic Mean
7 CASE STUDY: Moneyball (Briefing)
8 Python – Directory, Libraries and Data
9 Python – Mean
10 EXERCISE: Python – Mean
11 Median and Mode
12 Python – Median
13 EXERCISE: Python – Median
14 Python – Mode
15 EXERCISE: Python – Mode
16 Correlation
17 Python – Correlation
18 EXERCISE: Python – Correlation
19 Standard Deviation
20 Python – Standard Deviation
21 EXERCISE: Python – Standard Deviation
22 CASE STUDY: Moneyball
23 Intermediary Statistics – Game Plan
24 Normal Distribution
25 CASE STUDY: Wine Quality (Briefing)
26 Python – Preparing Script and Loading Data
27 Python – Normal Distribution Visualization
28 EXERCISE: Python – Normal Distribution
29 P-Value
30 Shapiro-Wilks Test
31 Python – Shapiro-Wilks Test
32 EXERCISE: Python – Shapiro-Wilks
33 Standard Error of the Mean
34 Python – Standard Error
35 EXERCISE: Python – Standard Error
36 Z-Score
37 Confidence Interval
38 Python – Confidence Interval
39 EXERCISE: Python – Confidence Interval
40 T-test
41 CASE STUDY: Remote Work Predictions (Briefing)
42 Python – T-test
43 EXERCISE: Python – T-test
44 Chi-square test
45 Python – Chi-square test
46 EXERCISE: Python – Chi-square
47 Powerposing and p-hacking
48 Linear Regression – Game Plan
49 CASE STUDY: Diamonds (Briefing)
50 Linear Regression
51 Python – Preparing Script and Loading Data
52 Python – Isolate X and Y
53 Python – Adding Constant
54 Linear Regression Output
55 Python – Linear Regression Model and Summary
56 Python – Plotting Regression
57 Dummy Variable Trap
58 Python – Dummy Variable
59 EXERCISE: Python – Linear Regression
60 Multilinear Regression – Game Plan
61 The Concept of Multilinear Regression
62 CASE STUDY: Professors’ Salary (Briefing)
63 Python – Preparing Script and Loading Data
64 Python – Summary Statistics
65 Outliers
66 Python – Plotting Continuous Variables
67 Python – Correlation Matrix
68 Python – Categorical Variables
69 Python – For Loop
70 Python – Creating Dummy Variables
71 Python – Isolate X and Y
72 Python – Adding Constant
73 Under and Over Fitting
74 Training and Test Set
75 Python – Train and Test Split
76 Python – Multilinear Regression
77 Accuracy KPIs (Key Performance Indicators)
78 Python – Model Predictions
79 Python – Accuracy Assessment
80 CHALLENGE: Introduction
81 CHALLENGE: Solutions
82 Logistic Regression – Game Plan
83 CASE STUDY: Spam Emails (Briefing)
84 Logistic Regression
85 Python – Preparing Script and Loading Data
86 Python – Summary Statistics
87 Python – Histogram and Outlier Removal
88 Python – Correlation Matrix
89 Python – Transforming Dependent Variable
90 Python – Prepare X and Y
91 Python – Training and Test Set
92 How to Read Logistic Regression Coefficients
93 Python – Logistic Regression
94 Python – Function to Read Coefficients
95 Python – Predictions
96 Confusion Matrix
97 Python – Confusion Matrix
98 Python – Manual Accuracy Assessment
99 Python – Classification Report
100 CHALLENGE: Introduction
101 CHALLENGE: Solutions
102 Why Econometrics and Causal Inference
103 Google Causal Impact – Game Plan
104 Time Series Data
105 CASE STUDY: Bitcoin Pricing (Briefing)
106 Difference-in-Differences Framework
107 Causal Impact Step-by-Step
108 Python – Installing and Importing Libraries
109 Python – Defining Dates
110 Python – Bitcoin Price loading
111 Assumptions
112 Python – Load Control Groups
113 Python – Preparing DataFrame
114 Python – Preparing for Correlation Matrix
115 Correlation Recap and Stationarity
116 Python – Stationarity
117 Python – Correlation
118 Python – Google Causal Impact Setup
119 Python – Google Causal Impact
120 Interpretation of Results
121 Python – Impact Results
122 CHALLENGE: Introduction
123 CHALLENGE: Solutions
124 Matching – Game Plan
125 Matching
126 CASE STUDY: Catholic Schools & Standardized Tests (Briefing)
127 Python – Directory and Libraries
128 Python – Loading Data
129 Unconfoundedness
130 Python – Comparing Means
131 Python – T-Test
132 Python – T-Test Loop
133 Python – Chi-square Test
134 Python – Chi-square Loop
135 Python – Other Variables
136 The Curse of Dimensionality
137 Python – Race Variable Transformation
138 Python – Education Variables
139 Python – Cleaning and Preparing Dataset
140 Common Support Region
141 Python – Logistic Regression and Debugging
142 Python – Preparing for Common Support Region
143 Python – Common Support Region Visualization
144 Python – Matching
145 Robustness Checks
146 Python – Robustness Check – Repeated experiments
147 Python – Outcome Visualization
148 Python – Robustness Check – Removing 1 confounder
149 CHALLENGE: Introduction
150 CHALLENGE: Solutions
151 My Experience with Matching
152 RFM – Game Plan
153 Value Based Segmentation
154 RFM Model
155 CASE STUDY: Online Shopping (Briefing)
156 Python – Directory and Libraries
157 Python – Loading Data
158 Python – Creating Sales Variable
159 Python – Date Variable
160 Python – Customer Level Aggregation
161 Python – Monetary Variable
162 Python – Tidying up Dataframe
163 Python – Quartiles
164 Python – RFM Score
165 Python – RFM Function
166 Python – Applying RFM Function
167 Python – Results Summary
168 CHALLENGE: Introduction
169 CHALLENGE: Solutions
170 Gaussian Mixture – Game Plan
171 Clustering
172 Gaussian Mixture Model
173 CASE STUDY: Credit Cards #1 (Briefing)
174 Python – Directory and Data
175 Python – Load Data
176 Python – Transform Character variables
177 AIC and BIC
178 Python – Optimal Number of Clusters
179 Python – Gaussian Mixture Model
180 Python – Cluster Prediction and Assignment
181 Python – Interpretation
182 CHALLENGE: Introduction
183 CHALLENGE: Solutions
184 My Experience with Segmentation
185 Random Forest – Game Plan
186 Ensemble Learning and Random Forest
187 How Decision Trees Work
188 CASE STUDY: Credit Cards #2 (Briefing)
189 Python – Directory and Libraries
190 Python – Loading Data
191 Python – Transform Object into Numerical Variables
192 Python – Summary Statistics
193 Random Forest Quirks
194 Python – Isolate X and Y
195 Python – Training and Test Set
196 Python – Random Forest Model
197 Python – Predictions
198 Python – Classification Report and F1 score
199 Python – Feature Importance
200 Parameter Tuning
201 Python – Parameter Grid
202 Python – Parameter Tuning
203 CHALLENGE: Introduction
204 CHALLENGE: Solutions (Part 1)
205 CHALLENGE: Solutions (Part 2)
206 Facebook Prophet – Game Plan
207 Structural Time Series
208 Facebook Prophet
209 CASE STUDY: Wikipedia (Briefing)
210 Python – Directory and Libraries
211 Python – Loading Data
212 Python – Transforming Date Variable
213 Python – Renaming Variables
214 Dynamic Holidays
215 Python – Easter Holidays
216 Python – Black Friday
217 Python – Combining Events and Preparing Dataframe
218 Training and Test Set
219 Python – Training and Test Set
220 Facebook Prophet Parameters
221 Additive vs. Multiplicative Seasonality
222 Facebook Prophet Model
223 Python – Regressor Coefficients
224 Python – Future Dataframe
225 Python – Forecasting
226 Python – Accuracy Assessment
227 Python – Visualization
228 Cross-validation
229 Python – Cross-validation
230 Parameters to tune
231 Python – Parameter Grid
232 Python – Parameter Tuning
233 CHALLENGE: Introduction
234 CHALLENGE: Solutions (Part 1)
235 CHALLENGE: Solutions (Part 2)
236 CHALLENGE: Solutions (Part 3)
237 Forecasting at Uber
238 Thank You!
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