English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 523 lectures (31h 57m) | 15.88 GB
Complete Data Science Training: Math, Statistics, Python, Advanced Statistics in Python, Machine and Deep Learning
Update 2025: Intro to Data Science module updated for recent AI developments
The Problem
Data scientist is one of the best suited professions to thrive this century. It is digital, programming-oriented, and analytical. Therefore, it comes as no surprise that the demand for data scientists has been surging in the job marketplace.
However, supply has been very limited. It is difficult to acquire the skills necessary to be hired as a data scientist.
And how can you do that?
Universities have been slow at creating specialized data science programs. (not to mention that the ones that exist are very expensive and time consuming)
Most online courses focus on a specific topic and it is difficult to understand how the skill they teach fit in the complete picture
The Solution
Data science is a multidisciplinary field. It encompasses a wide range of topics.
- Understanding of the data science field and the type of analysis carried out
- Mathematics
- Statistics
- Python
- Applying advanced statistical techniques in Python
- Data Visualization
- Machine Learning
- Deep Learning
Each of these topics builds on the previous ones. And you risk getting lost along the way if you don’t acquire these skills in the right order. For example, one would struggle in the application of Machine Learning techniques before understanding the underlying Mathematics. Or, it can be overwhelming to study regression analysis in Python before knowing what a regression is.
So, in an effort to create the most effective, time-efficient, and structured data science training available online, we created The Data Science Course 2025.
We believe this is the first training program that solves the biggest challenge to entering the data science field – having all the necessary resources in one place.
Moreover, our focus is to teach topics that flow smoothly and complement each other. The course teaches you everything you need to know to become a data scientist at a fraction of the cost of traditional programs (not to mention the amount of time you will save).
The Skills
1. Intro to Data and Data Science
Big data, business intelligence, business analytics, machine learning and artificial intelligence. We know these buzzwords belong to the field of data science but what do they all mean?
Why learn it? As a candidate data scientist, you must understand the ins and outs of each of these areas and recognise the appropriate approach to solving a problem. This ‘Intro to data and data science’ will give you a comprehensive look at all these buzzwords and where they fit in the realm of data science.
2. Mathematics
Learning the tools is the first step to doing data science. You must first see the big picture to then examine the parts in detail.
We take a detailed look specifically at calculus and linear algebra as they are the subfields data science relies on.
Why learn it?
Calculus and linear algebra are essential for programming in data science. If you want to understand advanced machine learning algorithms, then you need these skills in your arsenal.
3. Statistics
You need to think like a scientist before you can become a scientist. Statistics trains your mind to frame problems as hypotheses and gives you techniques to test these hypotheses, just like a scientist.
Why learn it?
This course doesn’t just give you the tools you need but teaches you how to use them. Statistics trains you to think like a scientist.
4. Python
Python is a relatively new programming language and, unlike R, it is a general-purpose programming language. You can do anything with it! Web applications, computer games and data science are among many of its capabilities. That’s why, in a short space of time, it has managed to disrupt many disciplines. Extremely powerful libraries have been developed to enable data manipulation, transformation, and visualisation. Where Python really shines however, is when it deals with machine and deep learning.
Why learn it?
When it comes to developing, implementing, and deploying machine learning models through powerful frameworks such as scikit-learn, TensorFlow, etc, Python is a must have programming language.
5. Tableau
Data scientists don’t just need to deal with data and solve data driven problems. They also need to convince company executives of the right decisions to make. These executives may not be well versed in data science, so the data scientist must but be able to present and visualise the data’s story in a way they will understand. That’s where Tableau comes in – and we will help you become an expert story teller using the leading visualisation software in business intelligence and data science.
Why learn it?
A data scientist relies on business intelligence tools like Tableau to communicate complex results to non-technical decision makers.
6. Advanced Statistics
Regressions, clustering, and factor analysis are all disciplines that were invented before machine learning. However, now these statistical methods are all performed through machine learning to provide predictions with unparalleled accuracy. This section will look at these techniques in detail.
Why learn it?
Data science is all about predictive modelling and you can become an expert in these methods through this ‘advance statistics’ section.
7. Machine Learning
The final part of the program and what every section has been leading up to is deep learning. Being able to employ machine and deep learning in their work is what often separates a data scientist from a data analyst. This section covers all common machine learning techniques and deep learning methods with TensorFlow.
What you’ll learn
- The course provides the entire toolbox you need to become a data scientist
- Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau,
- Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
- Impress interviewers by showing an understanding of the data science field
- Learn how to pre-process data
- Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
- Start coding in Python and learn how to use it for statistical analysis
- Perform linear and logistic regressions in Python
- Carry out cluster and factor analysis
- Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
- Apply your skills to real-life business cases
- Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
- Unfold the power of deep neural networks
- Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
- Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
Table of Contents
Part 1 Introduction
1 A Practical Example What You Will Learn in This Course
2 What Does the Course Cover
3 Download All Resources and Important FAQ
The Field of Data Science The Various Data Science Disciplines
4 Data Science and Business Buzzwords Why are there so Many
5 What is the difference between Analysis and Analytics
6 Business Analytics Data Analytics and Data Science An Introduction
7 Continuing with BI ML and AI
8 Traditional AI vs Generative AI
9 More Examples of Generative AI
10 A Breakdown of our Data Science Infographic
The Field of Data Science Connecting the Data Science Disciplines
11 Applying Traditional Data Big Data BI Traditional Data Science and ML
The Field of Data Science The Benefits of Each Discipline
12 The Reason Behind These Disciplines
The Field of Data Science Popular Data Science Techniques
13 Techniques for Working with Traditional Data
14 Real Life Examples of Traditional Data
15 Techniques for Working with Big Data
16 Real Life Examples of Big Data
17 Business Intelligence BI Techniques
18 Real Life Examples of Business Intelligence BI
19 Techniques for Working with Traditional Methods
20 Real Life Examples of Traditional Methods
21 Machine Learning ML Techniques
22 Types of Machine Learning
23 Evolution and Latest Trends of Machine Learning ML
24 Real Life Examples of Machine Learning ML
The Field of Data Science Popular Data Science Tools
25 Necessary Programming Languages and Software Used in Data Science
The Field of Data Science Careers in Data Science
26 Finding the Job What to Expect and What to Look for
The Field of Data Science Debunking Common Misconceptions
27 Debunking Common Misconceptions
Part 2 Probability
28 The Basic Probability Formula
29 Computing Expected Values
30 Frequency
31 Events and Their Complements
Probability Combinatorics
32 Fundamentals of Combinatorics
33 Permutations and How to Use Them
34 Simple Operations with Factorials
35 Solving Variations with Repetition
36 Solving Variations without Repetition
37 Solving Combinations
38 Symmetry of Combinations
39 Solving Combinations with Separate Sample Spaces
40 Combinatorics in RealLife The Lottery
41 A Recap of Combinatorics
42 A Practical Example of Combinatorics
Probability Bayesian Inference
43 Sets and Events
44 Ways Sets Can Interact
45 Intersection of Sets
46 Union of Sets
47 Mutually Exclusive Sets
48 Dependence and Independence of Sets
49 The Conditional Probability Formula
50 The Law of Total Probability
51 The Additive Rule
52 The Multiplication Law
53 Bayes Law
54 A Practical Example of Bayesian Inference
Probability Distributions
55 Fundamentals of Probability Distributions
56 Types of Probability Distributions
57 Characteristics of Discrete Distributions
58 Discrete Distributions The Uniform Distribution
59 Discrete Distributions The Bernoulli Distribution
60 Discrete Distributions The Binomial Distribution
61 Discrete Distributions The Poisson Distribution
62 Characteristics of Continuous Distributions
63 Continuous Distributions The Normal Distribution
64 Continuous Distributions The Standard Normal Distribution
65 Continuous Distributions The Students T Distribution
66 Continuous Distributions The ChiSquared Distribution
67 Continuous Distributions The Exponential Distribution
68 Continuous Distributions The Logistic Distribution
69 A Practical Example of Probability Distributions
Probability Probability in Other Fields
70 Probability in Finance
71 Probability in Statistics
72 Probability in Data Science
Part 3 Statistics
73 Population and Sample
Statistics Descriptive Statistics
74 Types of Data
75 Levels of Measurement
76 Categorical Variables Visualization Techniques
77 Categorical Variables Exercise
78 Numerical Variables Frequency Distribution Table
79 Numerical Variables Exercise
80 The Histogram
81 Histogram Exercise
82 Cross Tables and Scatter Plots
83 Cross Tables and Scatter Plots Exercise
84 Mean median and mode
85 Mean Median and Mode Exercise
86 Skewness
87 Skewness Exercise
88 Variance
89 Variance Exercise
90 Standard Deviation and Coefficient of Variation
91 Standard Deviation and Coefficient of Variation Exercise
92 Covariance
93 Covariance Exercise
94 Correlation Coefficient
95 Correlation Coefficient Exercise
Statistics Practical Example Descriptive Statistics
96 Practical Example Descriptive Statistics
97 Practical Example Descriptive Statistics Exercise
Statistics Inferential Statistics Fundamentals
98 Introduction
99 What is a Distribution
100 The Normal Distribution
101 The Standard Normal Distribution
102 The Standard Normal Distribution Exercise
103 Central Limit Theorem
104 Standard error
105 Estimators and Estimates
Statistics Inferential Statistics Confidence Intervals
106 What are Confidence Intervals
107 Confidence Intervals Population Variance Known Zscore
108 Confidence Intervals Population Variance Known Zscore Exercise
109 Confidence Interval Clarifications
110 Students T Distribution
111 Confidence Intervals Population Variance Unknown Tscore
112 Confidence Intervals Population Variance Unknown Tscore Exercise
113 Margin of Error
114 Confidence intervals Two means Dependent samples
115 Confidence intervals Two means Dependent samples Exercise
116 Confidence intervals Two means Independent Samples Part 1
117 Confidence intervals Two means Independent Samples Part 1 Exercise
118 Confidence intervals Two means Independent Samples Part 2
119 Confidence intervals Two means Independent Samples Part 2 Exercise
120 Confidence intervals Two means Independent Samples Part 3
Statistics Practical Example Inferential Statistics
121 Practical Example Inferential Statistics
122 Practical Example Inferential Statistics Exercise
Statistics Hypothesis Testing
123 Null vs Alternative Hypothesis
124 Further Reading on Null and Alternative Hypothesis
125 Rejection Region and Significance Level
126 Type I Error and Type II Error
127 Test for the Mean Population Variance Known
128 Test for the Mean Population Variance Known Exercise
129 pvalue
130 Test for the Mean Population Variance Unknown
131 Test for the Mean Population Variance Unknown Exercise
132 Test for the Mean Dependent Samples
133 Test for the Mean Dependent Samples Exercise
134 Test for the mean Independent Samples Part 1
135 Test for the mean Independent Samples Part 1 Exercise
136 Test for the mean Independent Samples Part 2
137 Test for the mean Independent Samples Part 2 Exercise
Statistics Practical Example Hypothesis Testing
138 Practical Example Hypothesis Testing
139 Practical Example Hypothesis Testing Exercise
Part 4 Introduction to Python
140 Introduction to Programming
141 Why Python
142 Why Jupyter
143 Installing Python and Jupyter
144 Understanding Jupyters Interface the Notebook Dashboard
145 Prerequisites for Coding in the Jupyter Notebooks
Python Variables and Data Types
146 Variables
147 Numbers and Boolean Values in Python
148 Python Strings
Python Basic Python Syntax
149 Using Arithmetic Operators in Python
150 The Double Equality Sign
151 How to Reassign Values
152 Add Comments
153 Understanding Line Continuation
154 Indexing Elements
155 Structuring with Indentation
Python Other Python Operators
156 Comparison Operators
157 Logical and Identity Operators
Python Conditional Statements
158 The IF Statement
159 The ELSE Statement
160 The ELIF Statement
161 A Note on Boolean Values
Python Python Functions
162 Defining a Function in Python
163 How to Create a Function with a Parameter
164 Defining a Function in Python Part II
165 How to Use a Function within a Function
166 Conditional Statements and Functions
167 Functions Containing a Few Arguments
168 Builtin Functions in Python
Python Sequences
169 Lists
170 Using Methods
171 List Slicing
172 Tuples
173 Dictionaries
Python Iterations
174 For Loops
175 While Loops and Incrementing
176 Lists with the range Function
177 Conditional Statements and Loops
178 Conditional Statements Functions and Loops
179 How to Iterate over Dictionaries
Python Advanced Python Tools
180 Object Oriented Programming
181 Modules and Packages
182 What is the Standard Library
183 Importing Modules in Python
Part 5 Advanced Statistical Methods in Python
184 Introduction to Regression Analysis
Advanced Statistical Methods Linear Regression with StatsModels
185 The Linear Regression Model
186 Correlation vs Regression
187 Geometrical Representation of the Linear Regression Model
188 Python Packages Installation
189 First Regression in Python
190 First Regression in Python Exercise
191 Using Seaborn for Graphs
192 How to Interpret the Regression Table
193 Decomposition of Variability
194 What is the OLS
195 RSquared
Advanced Statistical Methods Multiple Linear Regression with StatsModels
196 Multiple Linear Regression
197 Adjusted RSquared
198 Multiple Linear Regression Exercise
199 Test for Significance of the Model FTest
200 OLS Assumptions
201 A1 Linearity
202 A2 No Endogeneity
203 A3 Normality and Homoscedasticity
204 A4 No Autocorrelation
205 A5 No Multicollinearity
206 Dealing with Categorical Data Dummy Variables
207 Dealing with Categorical Data Dummy Variables
208 Making Predictions with the Linear Regression
Advanced Statistical Methods Linear Regression with sklearn
209 What is sklearn and How is it Different from Other Packages
210 How are we Going to Approach this Section
211 Simple Linear Regression with sklearn
212 Simple Linear Regression with sklearn A StatsModelslike Summary Table
213 A Note on Normalization
214 Simple Linear Regression with sklearn Exercise
215 Multiple Linear Regression with sklearn
216 Calculating the Adjusted RSquared in sklearn
217 Calculating the Adjusted RSquared in sklearn Exercise
218 Feature Selection Fregression
219 A Note on Calculation of Pvalues with sklearn
220 Creating a Summary Table with Pvalues
221 Multiple Linear Regression Exercise
222 Feature Scaling Standardization
223 Feature Selection through Standardization of Weights
224 Predicting with the Standardized Coefficients
225 Feature Scaling Standardization Exercise
226 Underfitting and Overfitting
227 Train Test Split Explained
Advanced Statistical Methods Practical Example Linear Regression
228 Practical Example Linear Regression Part 1
229 Practical Example Linear Regression Part 2
230 A Note on Multicollinearity
231 Practical Example Linear Regression Part 3
232 Dummies and Variance Inflation Factor Exercise
233 Practical Example Linear Regression Part 4
234 Dummy Variables Exercise
235 Practical Example Linear Regression Part 5
236 Linear Regression Exercise
Advanced Statistical Methods Logistic Regression
237 Introduction to Logistic Regression
238 A Simple Example in Python
239 Logistic vs Logit Function
240 Building a Logistic Regression
241 Building a Logistic Regression Exercise
242 An Invaluable Coding Tip
243 Understanding Logistic Regression Tables
244 Understanding Logistic Regression Tables Exercise
245 What do the Odds Actually Mean
246 Binary Predictors in a Logistic Regression
247 Binary Predictors in a Logistic Regression Exercise
248 Calculating the Accuracy of the Model
249 Calculating the Accuracy of the Model
250 Underfitting and Overfitting
251 Testing the Model
252 Testing the Model Exercise
Advanced Statistical Methods Cluster Analysis
253 Introduction to Cluster Analysis
254 Some Examples of Clusters
255 Difference between Classification and Clustering
256 Math Prerequisites
Advanced Statistical Methods KMeans Clustering
257 KMeans Clustering
258 A Simple Example of Clustering
259 A Simple Example of Clustering Exercise
260 Clustering Categorical Data
261 Clustering Categorical Data Exercise
262 How to Choose the Number of Clusters
263 How to Choose the Number of Clusters Exercise
264 Pros and Cons of KMeans Clustering
265 To Standardize or not to Standardize
266 Relationship between Clustering and Regression
267 Market Segmentation with Cluster Analysis Part 1
268 Market Segmentation with Cluster Analysis Part 2
269 How is Clustering Useful
270 EXERCISE Species Segmentation with Cluster Analysis Part 1
271 EXERCISE Species Segmentation with Cluster Analysis Part 2
Advanced Statistical Methods Other Types of Clustering
272 Types of Clustering
273 Dendrogram
274 Heatmaps
ChatGPT for Data Science
275 Traditional data science methods and the role of ChatGPT
276 How to install ChatGPT
277 How ChatGPT can boost your productivity
278 Data Preprocessing with ChatGPT
279 First attempt at machine learning with ChatGPT
280 Analyzing a client database with ChatGPT in Python
281 Analyzing a client database with ChatGPT in Python analyzing top products
282 Analyzing a client database with ChatGPT in Python analyzing top clients RFM
283 Exploratory data analysis EDA with ChatGPT histogram and scatter plot
284 Exploratory data analysis EDA with ChatGPT correlation matrix outlier detec
285 Assignment 1
286 Hypothesis testing with ChatGPT
287 Marvels comic book database Intro to Regular Expressions RegEx
288 Decoding comic book data Python Regular Expressions and ChatGPT
289 Assignment 2
290 Algorithm recommendation Movie Database Analysis with ChatGPT
291 Algorithm recommendation recommendation engine for movies with ChatGPT
292 Ethical principles in data and AI utilization
293 Using ChatGPT for ethical considerations
Case Study Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis
294 Intro to the Case Study
295 The Naive Bayes Algorithm
296 Tokenization and Vectorization
297 Imbalanced Data Sets
298 Overcome Imbalanced Data in Machine Learning
299 Loading the Dataset and Preprocessing
300 Optimizing User Reviews Data Preprocessing EDA
301 Reg Ex for Analyzing Text Review Data
302 Understanding Differences between Multinomial and Bernouilli Naive Bayes
303 Machine Learning with Naive Bayes First Attempt
304 Machine Learning with Naive Bayes converting the problem to a binary one
305 Testing the Model on New Data
Part 6 Mathematics
306 What is a Matrix
307 Scalars and Vectors
308 Linear Algebra and Geometry
309 Arrays in Python A Convenient Way To Represent Matrices
310 What is a Tensor
311 Addition and Subtraction of Matrices
312 Errors when Adding Matrices
313 Transpose of a Matrix
314 Dot Product
315 Dot Product of Matrices
316 Why is Linear Algebra Useful
Part 7 Deep Learning
317 What to Expect from this Part
Deep Learning Introduction to Neural Networks
318 Introduction to Neural Networks
319 Training the Model
320 Types of Machine Learning
321 The Linear Model Linear Algebraic Version
322 The Linear Model with Multiple Inputs
323 The Linear model with Multiple Inputs and Multiple Outputs
324 Graphical Representation of Simple Neural Networks
325 What is the Objective Function
326 Common Objective Functions L2norm Loss
327 Common Objective Functions CrossEntropy Loss
328 Optimization Algorithm 1Parameter Gradient Descent
329 Optimization Algorithm nParameter Gradient Descent
Deep Learning How to Build a Neural Network from Scratch with NumPy
330 Basic NN Example Part 1
331 Basic NN Example Part 2
332 Basic NN Example Part 3
333 Basic NN Example Part 4
334 Basic NN Example Exercises
Deep Learning TensorFlow 20 Introduction
335 How to Install TensorFlow 20
336 TensorFlow Outline and Comparison with Other Libraries
337 TensorFlow 1 vs TensorFlow 2
338 A Note on TensorFlow 2 Syntax
339 Types of File Formats Supporting TensorFlow
340 Outlining the Model with TensorFlow 2
341 Interpreting the Result and Extracting the Weights and Bias
342 Customizing a TensorFlow 2 Model
343 Basic NN with TensorFlow Exercises
Deep Learning Digging Deeper into NNs Introducing Deep Neural Networks
344 What is a Layer
345 What is a Deep Net
346 Digging into a Deep Net
347 NonLinearities and their Purpose
348 Activation Functions
349 Activation Functions Softmax Activation
350 Backpropagation
351 Backpropagation Picture
352 Backpropagation A Peek into the Mathematics of Optimization
Deep Learning Overfitting
353 What is Overfitting
354 Underfitting and Overfitting for Classification
355 What is Validation
356 Training Validation and Test Datasets
357 NFold Cross Validation
358 Early Stopping or When to Stop Training
Deep Learning Initialization
359 What is Initialization
360 Types of Simple Initializations
361 StateoftheArt Method Xavier Glorot Initialization
Deep Learning Digging into Gradient Descent and Learning Rate Schedules
362 Stochastic Gradient Descent
363 Problems with Gradient Descent
364 Momentum
365 Learning Rate Schedules or How to Choose the Optimal Learning Rate
366 Learning Rate Schedules Visualized
367 Adaptive Learning Rate Schedules AdaGrad and RMSprop
368 Adam Adaptive Moment Estimation
Deep Learning Preprocessing
369 Preprocessing Introduction
370 Types of Basic Preprocessing
371 Standardization
372 Preprocessing Categorical Data
373 Binary and OneHot Encoding
Deep Learning Classifying on the MNIST Dataset
374 MNIST The Dataset
375 MNIST How to Tackle the MNIST
376 MNIST Importing the Relevant Packages and Loading the Data
377 MNIST Preprocess the Data Create a Validation Set and Scale It
378 MNIST Preprocess the Data Scale the Test Data Exercise
379 MNIST Preprocess the Data Shuffle and Batch
380 MNIST Preprocess the Data Shuffle and Batch Exercise
381 MNIST Outline the Model
382 MNIST Select the Loss and the Optimizer
383 MNIST Learning
384 MNIST Exercises
385 MNIST Testing the Model
Deep Learning Business Case Example
386 Business Case Exploring the Dataset and Identifying Predictors
387 Business Case Outlining the Solution
388 Business Case Balancing the Dataset
389 Business Case Preprocessing the Data
390 Business Case Preprocessing the Data Exercise
391 Business Case Load the Preprocessed Data
392 Business Case Load the Preprocessed Data Exercise
393 Business Case Learning and Interpreting the Result
394 Business Case Setting an Early Stopping Mechanism
395 Setting an Early Stopping Mechanism Exercise
396 Business Case Testing the Model
397 Business Case Final Exercise
Deep Learning Conclusion
398 Summary on What Youve Learned
399 Whats Further out there in terms of Machine Learning
400 DeepMind and Deep Learning
401 An overview of CNNs
402 An Overview of RNNs
403 An Overview of nonNN Approaches
Appendix Deep Learning TensorFlow 1 Introduction
404 READ ME
405 How to Install TensorFlow 1
406 A Note on Installing Packages in Anaconda
407 TensorFlow Intro
408 Actual Introduction to TensorFlow
409 Types of File Formats supporting Tensors
410 Basic NN Example with TF Inputs Outputs Targets Weights Biases
411 Basic NN Example with TF Loss Function and Gradient Descent
412 Basic NN Example with TF Model Output
413 Basic NN Example with TF Exercises
Appendix Deep Learning TensorFlow 1 Classifying on the MNIST Dataset
414 MNIST What is the MNIST Dataset
415 MNIST How to Tackle the MNIST
416 MNIST Relevant Packages
417 MNIST Model Outline
418 MNIST Loss and Optimization Algorithm
419 Calculating the Accuracy of the Model
420 MNIST Batching and Early Stopping
421 MNIST Learning
422 MNIST Results and Testing
423 MNIST Exercises
424 MNIST Solutions
Appendix Deep Learning TensorFlow 1 Business Case
425 Business Case Getting Acquainted with the Dataset
426 Business Case Outlining the Solution
427 The Importance of Working with a Balanced Dataset
428 Business Case Preprocessing
429 Business Case Preprocessing Exercise
430 Creating a Data Provider
431 Business Case Model Outline
432 Business Case Optimization
433 Business Case Interpretation
434 Business Case Testing the Model
435 Business Case A Comment on the Homework
436 Business Case Final Exercise
Software Integration
437 What are Data Servers Clients Requests and Responses
438 What are Data Connectivity APIs and Endpoints
439 Taking a Closer Look at APIs
440 Communication between Software Products through Text Files
441 Software Integration Explained
Case Study Whats Next in the Course
442 Game Plan for this Python SQL and Tableau Business Exercise
443 The Business Task
444 Introducing the Data Set
Case Study Preprocessing the Absenteeismdata
445 What to Expect from the Following Sections
446 Importing the Absenteeism Data in Python
447 Checking the Content of the Data Set
448 Introduction to Terms with Multiple Meanings
449 Whats Regression Analysis a Quick Refresher
450 Using a Statistical Approach towards the Solution to the Exercise
451 Dropping a Column from a DataFrame in Python
452 EXERCISE Dropping a Column from a DataFrame in Python
453 SOLUTION Dropping a Column from a DataFrame in Python
454 Analyzing the Reasons for Absence
455 Obtaining Dummies from a Single Feature
456 EXERCISE Obtaining Dummies from a Single Feature
457 SOLUTION Obtaining Dummies from a Single Feature
458 Dropping a Dummy Variable from the Data Set
459 More on Dummy Variables A Statistical Perspective
460 Classifying the Various Reasons for Absence
461 Using concat in Python
462 EXERCISE Using concat in Python
463 SOLUTION Using concat in Python
464 Reordering Columns in a Pandas DataFrame in Python
465 EXERCISE Reordering Columns in a Pandas DataFrame in Python
466 SOLUTION Reordering Columns in a Pandas DataFrame in Python
467 Creating Checkpoints while Coding in Jupyter
468 EXERCISE Creating Checkpoints while Coding in Jupyter
469 SOLUTION Creating Checkpoints while Coding in Jupyter
470 Analyzing the Dates from the Initial Data Set
471 Extracting the Month Value from the Date Column
472 Extracting the Day of the Week from the Date Column
473 EXERCISE Removing the Date Column
474 Analyzing Several Straightforward Columns for this Exercise
475 Working on Education Children and Pets
476 Final Remarks of this Section
477 A Note on Exporting Your Data as a csv File
Case Study Applying Machine Learning to Create the absenteeismmodule
478 Exploring the Problem with a Machine Learning Mindset
479 Creating the Targets for the Logistic Regression
480 Selecting the Inputs for the Logistic Regression
481 Standardizing the Data
482 Splitting the Data for Training and Testing
483 Fitting the Model and Assessing its Accuracy
484 Creating a Summary Table with the Coefficients and Intercept
485 Interpreting the Coefficients for Our Problem
486 Standardizing only the Numerical Variables Creating a Custom Scaler
487 Interpreting the Coefficients of the Logistic Regression
488 Backward Elimination or How to Simplify Your Model
489 Testing the Model We Created
490 Saving the Model and Preparing it for Deployment
491 ARTICLE A Note on pickling
492 EXERCISE Saving the Model and Scaler
493 Preparing the Deployment of the Model through a Module
Case Study Loading the absenteeismmodule
494 Are You Sure Youre All Set
495 Deploying the absenteeismmodule Part I
496 Deploying the absenteeismmodule Part II
497 Exporting the Obtained Data Set as a csv
Case Study Analyzing the Predicted Outputs in Tableau
498 EXERCISE Age vs Probability
499 Analyzing Age vs Probability in Tableau
500 EXERCISE Reasons vs Probability
501 Analyzing Reasons vs Probability in Tableau
502 EXERCISE Transportation Expense vs Probability
503 Analyzing Transportation Expense vs Probability in Tableau
Appendix Additional Python Tools
504 Using the format Method
505 Iterating Over Range Objects
506 Introduction to Nested For Loops
507 Triple Nested For Loops
508 List Comprehensions
509 Anonymous Lambda Functions
Appendix pandas Fundamentals
510 Introduction to pandas Series
511 A Note on Completing the Upcoming Coding Exercises
512 Working with Methods in Python Part I
513 Working with Methods in Python Part II
514 Parameters and Arguments in pandas
515 Using unique and nunique
516 Using sortvalues
517 Introduction to pandas DataFrames Part I
518 Introduction to pandas DataFrames Part II
519 pandas DataFrames Common Attributes
520 Data Selection in pandas DataFrames
521 pandas DataFrames Indexing with iloc
522 pandas DataFrames Indexing with loc
Bonus Lecture
523 Bonus Lecture Next Steps
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