Python for Business Data Analytics & Intelligence
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.
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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 is business data analytics? Why learn business analytics? What does a business data analyst do?
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 (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 $$$.
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 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.
Using the latest industry techniques, this business data analytics course is focused on efficiency. So you never have to waste your time on confusing, out-of-date, incomplete tutorials anymore.
You'll learn by doing by completing exercises and fun challenges using real-world data. This will help you solidify your skills, push you beyond the basics and ensure that you have a deep understanding of each topic and feel confident using your new skills on any project you encounter.
And unlike other online courses and tutorials, you won't be learning alone.
Because by enrolling today, you’ll also get to join our exclusive live online community classroom to learn alongside thousands of students, alumni, mentors, TAs and Instructors.
Most importantly, you'll be learning from an industry professional (Diogo) that has actual real-world experience as a Business Data Analyst. He teaches you the exact tools and techniques he uses in his role.
Finally, this course will be constantly updated as the landscape changes.
Just as the business data analytics & business intelligence ecosystems evolve, we will ensure this course is constantly updated with new lectures and resources so that you will stay at the top of your field.
This course will be your go-to place to get all the latest analytics best practices anytime in the future.
Watch Online Python for Business Data Analytics & Intelligence
| # | Title | Duration |
|---|---|---|
| 1 | Python for Business Analytics & Intelligence | 02:35 |
| 2 | Introduction | 01:56 |
| 3 | Setting up the Course Material | 07:08 |
| 4 | The Modern Day Business Analyst | 05:01 |
| 5 | Basic Statistics - Game Plan | 01:07 |
| 6 | Arithmetic Mean | 01:57 |
| 7 | CASE STUDY: Moneyball (Briefing) | 00:59 |
| 8 | Python - Directory, Libraries and Data | 08:04 |
| 9 | Python - Mean | 09:17 |
| 10 | EXERCISE: Python - Mean | 02:21 |
| 11 | Median and Mode | 02:42 |
| 12 | Python - Median | 05:02 |
| 13 | EXERCISE: Python - Median | 02:58 |
| 14 | Python - Mode | 03:04 |
| 15 | EXERCISE: Python - Mode | 01:37 |
| 16 | Correlation | 04:17 |
| 17 | Python - Correlation | 08:42 |
| 18 | EXERCISE: Python - Correlation | 03:34 |
| 19 | Standard Deviation | 02:08 |
| 20 | Python - Standard Deviation | 02:24 |
| 21 | EXERCISE: Python - Standard Deviation | 01:05 |
| 22 | CASE STUDY: Moneyball | 03:57 |
| 23 | Intermediary Statistics - Game Plan | 00:47 |
| 24 | Normal Distribution | 03:01 |
| 25 | CASE STUDY: Wine Quality (Briefing) | 02:23 |
| 26 | Python - Preparing Script and Loading Data | 05:01 |
| 27 | Python - Normal Distribution Visualization | 09:29 |
| 28 | EXERCISE: Python - Normal Distribution | 05:42 |
| 29 | P-Value | 05:34 |
| 30 | Shapiro-Wilks Test | 01:52 |
| 31 | Python - Shapiro-Wilks Test | 07:43 |
| 32 | EXERCISE: Python - Shapiro-Wilks | 02:50 |
| 33 | Standard Error of the Mean | 02:37 |
| 34 | Python - Standard Error | 04:25 |
| 35 | EXERCISE: Python - Standard Error | 02:11 |
| 36 | Z-Score | 02:41 |
| 37 | Confidence Interval | 05:49 |
| 38 | Python - Confidence Interval | 06:24 |
| 39 | EXERCISE: Python - Confidence Interval | 02:20 |
| 40 | T-test | 02:18 |
| 41 | CASE STUDY: Remote Work Predictions (Briefing) | 00:40 |
| 42 | Python - T-test | 10:21 |
| 43 | EXERCISE: Python - T-test | 05:23 |
| 44 | Chi-square test | 02:29 |
| 45 | Python - Chi-square test | 07:30 |
| 46 | EXERCISE: Python - Chi-square | 03:15 |
| 47 | Powerposing and p-hacking | 03:21 |
| 48 | Linear Regression - Game Plan | 01:28 |
| 49 | CASE STUDY: Diamonds (Briefing) | 00:58 |
| 50 | Linear Regression | 05:12 |
| 51 | Python - Preparing Script and Loading Data | 04:37 |
| 52 | Python - Isolate X and Y | 01:48 |
| 53 | Python - Adding Constant | 02:44 |
| 54 | Linear Regression Output | 03:37 |
| 55 | Python - Linear Regression Model and Summary | 03:21 |
| 56 | Python - Plotting Regression | 04:24 |
| 57 | Dummy Variable Trap | 03:10 |
| 58 | Python - Dummy Variable | 03:36 |
| 59 | EXERCISE: Python - Linear Regression | 05:52 |
| 60 | Multilinear Regression - Game Plan | 01:35 |
| 61 | The Concept of Multilinear Regression | 01:46 |
| 62 | CASE STUDY: Professors' Salary (Briefing) | 00:46 |
| 63 | Python - Preparing Script and Loading Data | 05:06 |
| 64 | Python - Summary Statistics | 03:00 |
| 65 | Outliers | 02:44 |
| 66 | Python - Plotting Continuous Variables | 04:55 |
| 67 | Python - Correlation Matrix | 02:52 |
| 68 | Python - Categorical Variables | 04:31 |
| 69 | Python - For Loop | 04:44 |
| 70 | Python - Creating Dummy Variables | 03:10 |
| 71 | Python - Isolate X and Y | 03:29 |
| 72 | Python - Adding Constant | 01:27 |
| 73 | Under and Over Fitting | 01:33 |
| 74 | Training and Test Set | 01:04 |
| 75 | Python - Train and Test Split | 02:43 |
| 76 | Python - Multilinear Regression | 05:02 |
| 77 | Accuracy KPIs (Key Performance Indicators) | 03:20 |
| 78 | Python - Model Predictions | 01:32 |
| 79 | Python - Accuracy Assessment | 05:37 |
| 80 | CHALLENGE: Introduction | 05:09 |
| 81 | CHALLENGE: Solutions | 16:00 |
| 82 | Logistic Regression - Game Plan | 01:14 |
| 83 | CASE STUDY: Spam Emails (Briefing) | 01:01 |
| 84 | Logistic Regression | 02:07 |
| 85 | Python - Preparing Script and Loading Data | 04:17 |
| 86 | Python - Summary Statistics | 03:20 |
| 87 | Python - Histogram and Outlier Removal | 07:03 |
| 88 | Python - Correlation Matrix | 02:33 |
| 89 | Python - Transforming Dependent Variable | 02:40 |
| 90 | Python - Prepare X and Y | 02:10 |
| 91 | Python - Training and Test Set | 02:43 |
| 92 | How to Read Logistic Regression Coefficients | 02:41 |
| 93 | Python - Logistic Regression | 02:20 |
| 94 | Python - Function to Read Coefficients | 08:31 |
| 95 | Python - Predictions | 03:07 |
| 96 | Confusion Matrix | 06:18 |
| 97 | Python - Confusion Matrix | 05:26 |
| 98 | Python - Manual Accuracy Assessment | 07:06 |
| 99 | Python - Classification Report | 02:46 |
| 100 | CHALLENGE: Introduction | 04:50 |
| 101 | CHALLENGE: Solutions | 13:40 |
| 102 | Why Econometrics and Causal Inference | 04:21 |
| 103 | Google Causal Impact - Game Plan | 01:21 |
| 104 | Time Series Data | 01:31 |
| 105 | CASE STUDY: Bitcoin Pricing (Briefing) | 02:29 |
| 106 | Difference-in-Differences Framework | 02:22 |
| 107 | Causal Impact Step-by-Step | 02:21 |
| 108 | Python - Installing and Importing Libraries | 03:55 |
| 109 | Python - Defining Dates | 03:35 |
| 110 | Python - Bitcoin Price loading | 05:13 |
| 111 | Assumptions | 02:55 |
| 112 | Python - Load Control Groups | 04:00 |
| 113 | Python - Preparing DataFrame | 06:01 |
| 114 | Python - Preparing for Correlation Matrix | 02:43 |
| 115 | Correlation Recap and Stationarity | 04:17 |
| 116 | Python - Stationarity | 07:07 |
| 117 | Python - Correlation | 03:23 |
| 118 | Python - Google Causal Impact Setup | 02:42 |
| 119 | Python - Google Causal Impact | 03:24 |
| 120 | Interpretation of Results | 04:18 |
| 121 | Python - Impact Results | 05:05 |
| 122 | CHALLENGE: Introduction | 07:15 |
| 123 | CHALLENGE: Solutions | 13:14 |
| 124 | Matching - Game Plan | 02:51 |
| 125 | Matching | 02:52 |
| 126 | CASE STUDY: Catholic Schools & Standardized Tests (Briefing) | 01:01 |
| 127 | Python - Directory and Libraries | 02:54 |
| 128 | Python - Loading Data | 02:25 |
| 129 | Unconfoundedness | 02:17 |
| 130 | Python - Comparing Means | 02:43 |
| 131 | Python - T-Test | 04:10 |
| 132 | Python - T-Test Loop | 04:39 |
| 133 | Python - Chi-square Test | 03:28 |
| 134 | Python - Chi-square Loop | 04:27 |
| 135 | Python - Other Variables | 01:50 |
| 136 | The Curse of Dimensionality | 01:41 |
| 137 | Python - Race Variable Transformation | 07:00 |
| 138 | Python - Education Variables | 05:31 |
| 139 | Python - Cleaning and Preparing Dataset | 03:32 |
| 140 | Common Support Region | 04:05 |
| 141 | Python - Logistic Regression and Debugging | 07:23 |
| 142 | Python - Preparing for Common Support Region | 05:40 |
| 143 | Python - Common Support Region Visualization | 01:42 |
| 144 | Python - Matching | 04:52 |
| 145 | Robustness Checks | 02:14 |
| 146 | Python - Robustness Check - Repeated experiments | 07:01 |
| 147 | Python - Outcome Visualization | 01:56 |
| 148 | Python - Robustness Check - Removing 1 confounder | 03:39 |
| 149 | CHALLENGE: Introduction | 05:26 |
| 150 | CHALLENGE: Solutions | 14:04 |
| 151 | My Experience with Matching | 02:42 |
| 152 | RFM - Game Plan | 00:46 |
| 153 | Value Based Segmentation | 02:53 |
| 154 | RFM Model | 04:54 |
| 155 | CASE STUDY: Online Shopping (Briefing) | 00:54 |
| 156 | Python - Directory and Libraries | 02:18 |
| 157 | Python - Loading Data | 02:30 |
| 158 | Python - Creating Sales Variable | 01:46 |
| 159 | Python - Date Variable | 03:34 |
| 160 | Python - Customer Level Aggregation | 03:50 |
| 161 | Python - Monetary Variable | 01:24 |
| 162 | Python - Tidying up Dataframe | 02:53 |
| 163 | Python - Quartiles | 06:35 |
| 164 | Python - RFM Score | 01:52 |
| 165 | Python - RFM Function | 04:42 |
| 166 | Python - Applying RFM Function | 02:10 |
| 167 | Python - Results Summary | 04:30 |
| 168 | CHALLENGE: Introduction | 03:32 |
| 169 | CHALLENGE: Solutions | 12:17 |
| 170 | Gaussian Mixture - Game Plan | 01:11 |
| 171 | Clustering | 02:10 |
| 172 | Gaussian Mixture Model | 03:58 |
| 173 | CASE STUDY: Credit Cards #1 (Briefing) | 00:54 |
| 174 | Python - Directory and Data | 02:12 |
| 175 | Python - Load Data | 01:51 |
| 176 | Python - Transform Character variables | 01:22 |
| 177 | AIC and BIC | 02:16 |
| 178 | Python - Optimal Number of Clusters | 06:25 |
| 179 | Python - Gaussian Mixture Model | 01:12 |
| 180 | Python - Cluster Prediction and Assignment | 02:51 |
| 181 | Python - Interpretation | 07:47 |
| 182 | CHALLENGE: Introduction | 04:36 |
| 183 | CHALLENGE: Solutions | 18:05 |
| 184 | My Experience with Segmentation | 03:16 |
| 185 | Random Forest - Game Plan | 01:06 |
| 186 | Ensemble Learning and Random Forest | 02:17 |
| 187 | How Decision Trees Work | 04:20 |
| 188 | CASE STUDY: Credit Cards #2 (Briefing) | 00:38 |
| 189 | Python - Directory and Libraries | 02:03 |
| 190 | Python - Loading Data | 01:51 |
| 191 | Python - Transform Object into Numerical Variables | 01:44 |
| 192 | Python - Summary Statistics | 02:22 |
| 193 | Random Forest Quirks | 02:31 |
| 194 | Python - Isolate X and Y | 01:33 |
| 195 | Python - Training and Test Set | 03:41 |
| 196 | Python - Random Forest Model | 03:00 |
| 197 | Python - Predictions | 01:19 |
| 198 | Python - Classification Report and F1 score | 03:45 |
| 199 | Python - Feature Importance | 04:23 |
| 200 | Parameter Tuning | 02:46 |
| 201 | Python - Parameter Grid | 03:15 |
| 202 | Python - Parameter Tuning | 07:11 |
| 203 | CHALLENGE: Introduction | 04:25 |
| 204 | CHALLENGE: Solutions (Part 1) | 08:30 |
| 205 | CHALLENGE: Solutions (Part 2) | 09:41 |
| 206 | Facebook Prophet - Game Plan | 01:21 |
| 207 | Structural Time Series | 02:26 |
| 208 | Facebook Prophet | 03:38 |
| 209 | CASE STUDY: Wikipedia (Briefing) | 00:52 |
| 210 | Python - Directory and Libraries | 02:06 |
| 211 | Python - Loading Data | 02:35 |
| 212 | Python - Transforming Date Variable | 02:49 |
| 213 | Python - Renaming Variables | 01:32 |
| 214 | Dynamic Holidays | 02:11 |
| 215 | Python - Easter Holidays | 05:17 |
| 216 | Python - Black Friday | 02:51 |
| 217 | Python - Combining Events and Preparing Dataframe | 02:34 |
| 218 | Training and Test Set | 02:13 |
| 219 | Python - Training and Test Set | 03:18 |
| 220 | Facebook Prophet Parameters | 02:14 |
| 221 | Additive vs. Multiplicative Seasonality | 02:38 |
| 222 | Facebook Prophet Model | 04:46 |
| 223 | Python - Regressor Coefficients | 01:50 |
| 224 | Python - Future Dataframe | 04:38 |
| 225 | Python - Forecasting | 02:20 |
| 226 | Python - Accuracy Assessment | 03:42 |
| 227 | Python - Visualization | 05:41 |
| 228 | Cross-validation | 01:08 |
| 229 | Python - Cross-validation | 08:00 |
| 230 | Parameters to tune | 01:23 |
| 231 | Python - Parameter Grid | 04:04 |
| 232 | Python - Parameter Tuning | 07:29 |
| 233 | CHALLENGE: Introduction | 04:48 |
| 234 | CHALLENGE: Solutions (Part 1) | 09:18 |
| 235 | CHALLENGE: Solutions (Part 2) | 11:08 |
| 236 | CHALLENGE: Solutions (Part 3) | 08:09 |
| 237 | Forecasting at Uber | 04:39 |
| 238 | Thank You! | 01:18 |
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