CF

Python for Business Data Analytics & Intelligence

15h 25m 6s
English
Paid

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

This is the most up-to-date and comprehensive course for 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?

Business data analytics involves using data to drive business decisions and optimize outcomes. As a business data analyst, you will act like a detective, using tools such as Python, Facebook Prophet, and Google Causal Impact to investigate data, understand historical trends, and predict future outcomes.

Why Learn Business Analytics?

In today's data-driven economy, companies are racing to make informed decisions. Business Data Analysts are essential because they translate data into actionable insights, turning potential data into profits. Possessing these skills makes you highly valuable, and companies are ready to offer competitive salaries to attract such talent.

Learning Approach

Our course focuses on efficiency using the latest industry techniques, saving you from confusing or outdated tutorials. You'll engage in hands-on exercises and challenges using real-world data, ensuring you understand each topic deeply.

Learn Together

Unlike other online courses, by enrolling today, you'll join our exclusive online community classroom. Learn alongside thousands of students, alumni, mentors, TAs, and instructors, enhancing your learning experience.

Expert Instruction

Receive guidance from an industry professional, Diogo, who brings real-world experience as a Business Data Analyst, teaching you the exact tools and techniques he uses in his roles.

Constant Updates

The business data analytics and intelligence landscape continually evolves. We pledge to keep this course updated with new lectures and resources, ensuring that you remain at the forefront of your field. This course will be your go-to source for the latest analytics best practices anytime you need them.

About the Author: Zero To Mastery

Zero To Mastery thumbnail

Zero To Mastery (ZTM) is a Toronto-based online coding academy founded by Andrei Neagoie, originally a senior developer at large Canadian tech firms before turning to teaching full-time. The academy's signature is the cohort-based bootcamp track combined with a deep self-paced course library, all aimed at career-changers and self-taught developers preparing to land software-engineering roles at top companies.

The instructor roster has grown well beyond Andrei to include other senior practitioners: Daniel Bourke (machine learning), Aleksa Tešić (DevOps), Jacinto Wong, and others. Courses cover the full software-engineering career path: web development with React and Next.js, Python, machine learning and deep learning, DevOps and cloud, system design, mobile, and the algorithm / data-structure interview prep that gates engineering jobs.

The CourseFlix listing under this source carries over 120 ZTM courses spanning that full range. Material is paid; ZTM itself runs on a monthly / annual membership model. The teaching style favours long-form, project-based courses where students build complete portfolio-quality applications rather than disconnected feature tutorials.

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#1: Python for Business Analytics & Intelligence
All Course Lessons (238)
#Lesson TitleDurationAccess
1
Python for Business Analytics & Intelligence Demo
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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Frequently asked questions

What are the prerequisites for enrolling in this course?
There are no formal prerequisites for enrolling in this course. It's designed to take you from a complete beginner to a proficient business data analyst. Basic familiarity with Python can be helpful, but the course will guide you through necessary Python concepts, starting with setting up the course material and directory and libraries management.
What projects or case studies are included in the course?
The course includes several real-world case studies to apply what you learn. Notable examples are the 'Moneyball' case study, which covers statistical analysis, and the 'Wine Quality' case study, focusing on normal distribution visualization. Additionally, there's a 'Remote Work Predictions' case study that involves learning about T-tests and a 'Diamonds' case study exploring linear regression.
Who is the target audience for this course?
The course is aimed at anyone interested in becoming a business data analyst, regardless of their current skill level. It's particularly suitable for individuals looking to enter the data analytics field, enhance their decision-making capabilities with data, or those wanting to learn Python for business applications.
How does the depth of this course compare to other data analytics courses?
This course offers a comprehensive and up-to-date approach to learning business data analytics, covering 238 lessons. It not only provides foundational knowledge in Python and statistics but also delves into advanced topics like multilinear regression and various statistical tests such as Chi-square and T-tests, using real-world examples to ensure practical applicability.
What specific tools or platforms does this course teach?
The course focuses on using Python extensively for data analysis. It includes lessons on specific Python libraries and functionalities, such as mean, median, correlation, and linear regression. Additionally, tools like Facebook Prophet and Google Causal Impact are introduced for predictive analytics and understanding historical trends.
How much time should I expect to commit to this course?
While the course consists of 238 lessons, the total runtime is not specified. However, given the depth and breadth of topics covered, students should be prepared to dedicate several hours per week over a few months to complete the course. This timeframe includes engaging with hands-on exercises and case studies.
What topics are not covered in this course?
The course does not cover topics outside the realm of business data analytics and Python. For instance, it doesn't delve into other programming languages, non-Python data analysis tools, or areas like data engineering or machine learning in depth. The focus remains on business intelligence, statistical analysis, and Python-centric data analytics.