The Data Science Course 2022: Complete Data Science Bootcamp

The Data Science Course 2022: Complete Data Science Bootcamp

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 488 lectures (29h 53m) | 7.79 GB

Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

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 2022.

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 A Breakdown of our Data Science Infographic

The Field of Data Science – Connecting the Data Science Disciplines
9 Applying Traditional Data, Big Data, BI, Traditional Data Science and ML

The Field of Data Science – The Benefits of Each Discipline
10 The Reason Behind These Disciplines

The Field of Data Science – Popular Data Science Techniques
11 Techniques for Working with Traditional Data
12 Real Life Examples of Traditional Data
13 Techniques for Working with Big Data
14 Real Life Examples of Big Data
15 Business Intelligence (BI) Techniques
16 Real Life Examples of Business Intelligence (BI)
17 Techniques for Working with Traditional Methods
18 Real Life Examples of Traditional Methods
19 Machine Learning (ML) Techniques
20 Types of Machine Learning
21 Real Life Examples of Machine Learning (ML)

The Field of Data Science – Popular Data Science Tools
22 Necessary Programming Languages and Software Used in Data Science

The Field of Data Science – Careers in Data Science
23 Finding the Job – What to Expect and What to Look for

The Field of Data Science – Debunking Common Misconceptions
24 Debunking Common Misconceptions

Part 2 Probability
25 The Basic Probability Formula
26 Computing Expected Values
27 Frequency
28 Events and Their Complements

Probability – Combinatorics
29 Fundamentals of Combinatorics
30 Permutations and How to Use Them
31 Simple Operations with Factorials
32 Solving Variations with Repetition
33 Solving Variations without Repetition
34 Solving Combinations
35 Symmetry of Combinations
36 Solving Combinations with Separate Sample Spaces
37 Combinatorics in Real-Life The Lottery
38 A Recap of Combinatorics
39 A Practical Example of Combinatorics

Probability – Bayesian Inference
40 Sets and Events
41 Ways Sets Can Interact
42 Intersection of Sets
43 Union of Sets
44 Mutually Exclusive Sets
45 Dependence and Independence of Sets
46 The Conditional Probability Formula
47 The Law of Total Probability
48 The Additive Rule
49 The Multiplication Law
50 Bayes’ Law
51 A Practical Example of Bayesian Inference

Probability – Distributions
52 Fundamentals of Probability Distributions
53 Types of Probability Distributions
54 Characteristics of Discrete Distributions
55 Discrete Distributions The Uniform Distribution
56 Discrete Distributions The Bernoulli Distribution
57 Discrete Distributions The Binomial Distribution
58 Discrete Distributions The Poisson Distribution
59 Characteristics of Continuous Distributions
60 Continuous Distributions The Normal Distribution
61 Continuous Distributions The Standard Normal Distribution
62 Continuous Distributions The Students’ T Distribution
63 Continuous Distributions The Chi-Squared Distribution
64 Continuous Distributions The Exponential Distribution
65 Continuous Distributions The Logistic Distribution
66 A Practical Example of Probability Distributions

Probability – Probability in Other Fields
67 Probability in Finance
68 Probability in Statistics
69 Probability in Data Science

Part 3 Statistics
70 Population and Sample

Statistics – Descriptive Statistics
71 Types of Data
72 Levels of Measurement
73 Categorical Variables – Visualization Techniques
74 Categorical Variables Exercise
75 Numerical Variables – Frequency Distribution Table
76 Numerical Variables Exercise
77 The Histogram
78 Histogram Exercise
79 Cross Tables and Scatter Plots
80 Cross Tables and Scatter Plots Exercise
81 Mean, median and mode
82 Mean, Median and Mode Exercise
83 Skewness
84 Skewness Exercise
85 Variance
86 Variance Exercise
87 Standard Deviation and Coefficient of Variation
88 Standard Deviation and Coefficient of Variation Exercise
89 Covariance
90 Covariance Exercise
91 Correlation Coefficient
92 Correlation Coefficient Exercise

Statistics – Practical Example Descriptive Statistics
93 Practical Example Descriptive Statistics
94 Practical Example Descriptive Statistics Exercise

Statistics – Inferential Statistics Fundamentals
95 Introduction
96 What is a Distribution
97 The Normal Distribution
98 The Standard Normal Distribution
99 The Standard Normal Distribution Exercise
100 Central Limit Theorem
101 Standard error
102 Estimators and Estimates

Statistics – Inferential Statistics Confidence Intervals
103 What are Confidence Intervals
104 Confidence Intervals; Population Variance Known; Z-score
105 Confidence Intervals; Population Variance Known; Z-score; Exercise
106 Confidence Interval Clarifications
107 Student’s T Distribution
108 Confidence Intervals; Population Variance Unknown; T-score
109 Confidence Intervals; Population Variance Unknown; T-score; Exercise
110 Margin of Error
111 Confidence intervals. Two means. Dependent samples
112 Confidence intervals. Two means. Dependent samples Exercise
113 Confidence intervals. Two means. Independent Samples (Part 1)
114 Confidence intervals. Two means. Independent Samples (Part 1). Exercise
115 Confidence intervals. Two means. Independent Samples (Part 2)
116 Confidence intervals. Two means. Independent Samples (Part 2). Exercise
117 Confidence intervals. Two means. Independent Samples (Part 3)

Statistics – Practical Example Inferential Statistics
118 Practical Example Inferential Statistics
119 Practical Example Inferential Statistics Exercise

Statistics – Hypothesis Testing
120 Null vs Alternative Hypothesis
121 Further Reading on Null and Alternative Hypothesis
122 Rejection Region and Significance Level
123 Type I Error and Type II Error
124 Test for the Mean. Population Variance Known
125 Test for the Mean. Population Variance Known Exercise
126 p-value
127 Test for the Mean. Population Variance Unknown
128 Test for the Mean. Population Variance Unknown Exercise
129 Test for the Mean. Dependent Samples
130 Test for the Mean. Dependent Samples Exercise
131 Test for the mean. Independent Samples (Part 1)
132 Test for the mean. Independent Samples (Part 1). Exercise
133 Test for the mean. Independent Samples (Part 2)
134 Test for the mean. Independent Samples (Part 2). Exercise

Statistics – Practical Example Hypothesis Testing
135 Practical Example Hypothesis Testing
136 Practical Example Hypothesis Testing Exercise

Part 4 Introduction to Python
137 Introduction to Programming
138 Why Python
139 Why Jupyter
140 Installing Python and Jupyter
141 Understanding Jupyter’s Interface – the Notebook Dashboard
142 Prerequisites for Coding in the Jupyter Notebooks

Python – Variables and Data Types
143 Variables
144 Numbers and Boolean Values in Python
145 Python Strings

Python – Basic Python Syntax
146 Using Arithmetic Operators in Python
147 The Double Equality Sign
148 How to Reassign Values
149 Add Comments
150 Understanding Line Continuation
151 Indexing Elements
152 Structuring with Indentation

Python – Other Python Operators
153 Comparison Operators
154 Logical and Identity Operators

Python – Conditional Statements
155 The IF Statement
156 The ELSE Statement
157 The ELIF Statement
158 A Note on Boolean Values

Python – Python Functions
159 Defining a Function in Python
160 How to Create a Function with a Parameter
161 Defining a Function in Python – Part II
162 How to Use a Function within a Function
163 Conditional Statements and Functions
164 Functions Containing a Few Arguments
165 Built-in Functions in Python

Python – Sequences
166 Lists
167 Using Methods
168 List Slicing
169 Tuples
170 Dictionaries

Python – Iterations
171 For Loops
172 While Loops and Incrementing
173 Lists with the range() Function
174 Conditional Statements and Loops
175 Conditional Statements, Functions, and Loops
176 How to Iterate over Dictionaries

Python – Advanced Python Tools
177 Object Oriented Programming
178 Modules and Packages
179 What is the Standard Library
180 Importing Modules in Python

Part 5 Advanced Statistical Methods in Python
181 Introduction to Regression Analysis

Advanced Statistical Methods – Linear Regression with StatsModels
182 The Linear Regression Model
183 Correlation vs Regression
184 Geometrical Representation of the Linear Regression Model
185 Python Packages Installation
186 First Regression in Python
187 First Regression in Python Exercise
188 Using Seaborn for Graphs
189 How to Interpret the Regression Table
190 Decomposition of Variability
191 What is the OLS
192 R-Squared

Advanced Statistical Methods – Multiple Linear Regression with StatsModels
193 Multiple Linear Regression
194 Adjusted R-Squared
195 Multiple Linear Regression Exercise
196 Test for Significance of the Model (F-Test)
197 OLS Assumptions
198 A1 Linearity
199 A2 No Endogeneity
200 A3 Normality and Homoscedasticity
201 A4 No Autocorrelation
202 A5 No Multicollinearity
203 Dealing with Categorical Data – Dummy Variables
204 Dealing with Categorical Data – Dummy Variables
205 Making Predictions with the Linear Regression

Advanced Statistical Methods – Linear Regression with sklearn
206 What is sklearn and How is it Different from Other Packages
207 How are we Going to Approach this Section
208 Simple Linear Regression with sklearn
209 Simple Linear Regression with sklearn – A StatsModels-like Summary Table
210 A Note on Normalization
211 Simple Linear Regression with sklearn – Exercise
212 Multiple Linear Regression with sklearn
213 Calculating the Adjusted R-Squared in sklearn
214 Calculating the Adjusted R-Squared in sklearn – Exercise
215 Feature Selection (F-regression)
216 A Note on Calculation of P-values with sklearn
217 Creating a Summary Table with P-values
218 Multiple Linear Regression – Exercise
219 Feature Scaling (Standardization)
220 Feature Selection through Standardization of Weights
221 Predicting with the Standardized Coefficients
222 Feature Scaling (Standardization) – Exercise
223 Underfitting and Overfitting
224 Train – Test Split Explained

Advanced Statistical Methods – Practical Example Linear Regression
225 Practical Example Linear Regression (Part 1)
226 Practical Example Linear Regression (Part 2)
227 A Note on Multicollinearity
228 Practical Example Linear Regression (Part 3)
229 Dummies and Variance Inflation Factor – Exercise
230 Practical Example Linear Regression (Part 4)
231 Dummy Variables – Exercise
232 Practical Example Linear Regression (Part 5)
233 Linear Regression – Exercise

Advanced Statistical Methods – Logistic Regression
234 Introduction to Logistic Regression
235 A Simple Example in Python
236 Logistic vs Logit Function
237 Building a Logistic Regression
238 Building a Logistic Regression – Exercise
239 An Invaluable Coding Tip
240 Understanding Logistic Regression Tables
241 Understanding Logistic Regression Tables – Exercise
242 What do the Odds Actually Mean
243 Binary Predictors in a Logistic Regression
244 Binary Predictors in a Logistic Regression – Exercise
245 Calculating the Accuracy of the Model
246 Calculating the Accuracy of the Model
247 Underfitting and Overfitting
248 Testing the Model
249 Testing the Model – Exercise

Advanced Statistical Methods – Cluster Analysis
250 Introduction to Cluster Analysis
251 Some Examples of Clusters
252 Difference between Classification and Clustering
253 Math Prerequisites

Advanced Statistical Methods – K-Means Clustering
254 K-Means Clustering
255 A Simple Example of Clustering
256 A Simple Example of Clustering – Exercise
257 Clustering Categorical Data
258 Clustering Categorical Data – Exercise
259 How to Choose the Number of Clusters
260 How to Choose the Number of Clusters – Exercise
261 Pros and Cons of K-Means Clustering
262 To Standardize or not to Standardize
263 Relationship between Clustering and Regression
264 Market Segmentation with Cluster Analysis (Part 1)
265 Market Segmentation with Cluster Analysis (Part 2)
266 How is Clustering Useful
267 EXERCISE Species Segmentation with Cluster Analysis (Part 1)
268 EXERCISE Species Segmentation with Cluster Analysis (Part 2)

Advanced Statistical Methods – Other Types of Clustering
269 Types of Clustering
270 Dendrogram
271 Heatmaps

Part 6 Mathematics
272 What is a Matrix
273 Scalars and Vectors
274 Linear Algebra and Geometry
275 Arrays in Python – A Convenient Way To Represent Matrices
276 What is a Tensor
277 Addition and Subtraction of Matrices
278 Errors when Adding Matrices
279 Transpose of a Matrix
280 Dot Product
281 Dot Product of Matrices
282 Why is Linear Algebra Useful

Part 7 Deep Learning
283 What to Expect from this Part

Deep Learning – Introduction to Neural Networks
284 Introduction to Neural Networks
285 Training the Model
286 Types of Machine Learning
287 The Linear Model (Linear Algebraic Version)
288 The Linear Model with Multiple Inputs
289 The Linear model with Multiple Inputs and Multiple Outputs
290 Graphical Representation of Simple Neural Networks
291 What is the Objective Function
292 Common Objective Functions L2-norm Loss
293 Common Objective Functions Cross-Entropy Loss
294 Optimization Algorithm 1-Parameter Gradient Descent
295 Optimization Algorithm n-Parameter Gradient Descent

Deep Learning – How to Build a Neural Network from Scratch with NumPy
296 Basic NN Example (Part 1)
297 Basic NN Example (Part 2)
298 Basic NN Example (Part 3)
299 Basic NN Example (Part 4)
300 Basic NN Example Exercises

Deep Learning – TensorFlow 2.0 Introduction
301 How to Install TensorFlow 2.0
302 TensorFlow Outline and Comparison with Other Libraries
303 TensorFlow 1 vs TensorFlow 2
304 A Note on TensorFlow 2 Syntax
305 Types of File Formats Supporting TensorFlow
306 Outlining the Model with TensorFlow 2
307 Interpreting the Result and Extracting the Weights and Bias
308 Customizing a TensorFlow 2 Model
309 Basic NN with TensorFlow Exercises

Deep Learning – Digging Deeper into NNs Introducing Deep Neural Networks
310 What is a Layer
311 What is a Deep Net
312 Digging into a Deep Net
313 Non-Linearities and their Purpose
314 Activation Functions
315 Activation Functions Softmax Activation
316 Backpropagation
317 Backpropagation Picture
318 Backpropagation – A Peek into the Mathematics of Optimization

Deep Learning – Overfitting
319 What is Overfitting
320 Underfitting and Overfitting for Classification
321 What is Validation
322 Training, Validation, and Test Datasets
323 N-Fold Cross Validation
324 Early Stopping or When to Stop Training

Deep Learning – Initialization
325 What is Initialization
326 Types of Simple Initializations
327 State-of-the-Art Method – (Xavier) Glorot Initialization

Deep Learning – Digging into Gradient Descent and Learning Rate Schedules
328 Stochastic Gradient Descent
329 Problems with Gradient Descent
330 Momentum
331 Learning Rate Schedules, or How to Choose the Optimal Learning Rate
332 Learning Rate Schedules Visualized
333 Adaptive Learning Rate Schedules (AdaGrad and RMSprop )
334 Adam (Adaptive Moment Estimation)

Deep Learning – Preprocessing
335 Preprocessing Introduction
336 Types of Basic Preprocessing
337 Standardization
338 Preprocessing Categorical Data
339 Binary and One-Hot Encoding

Deep Learning – Classifying on the MNIST Dataset
340 MNIST The Dataset
341 MNIST How to Tackle the MNIST
342 MNIST Importing the Relevant Packages and Loading the Data
343 MNIST Preprocess the Data – Create a Validation Set and Scale It
344 MNIST Preprocess the Data – Scale the Test Data – Exercise
345 MNIST Preprocess the Data – Shuffle and Batch
346 MNIST Preprocess the Data – Shuffle and Batch – Exercise
347 MNIST Outline the Model
348 MNIST Select the Loss and the Optimizer
349 MNIST Learning
350 MNIST – Exercises
351 MNIST Testing the Model

Deep Learning – Business Case Example
352 Business Case Exploring the Dataset and Identifying Predictors
353 Business Case Outlining the Solution
354 Business Case Balancing the Dataset
355 Business Case Preprocessing the Data
356 Business Case Preprocessing the Data – Exercise
357 Business Case Load the Preprocessed Data
358 Business Case Load the Preprocessed Data – Exercise
359 Business Case Learning and Interpreting the Result
360 Business Case Setting an Early Stopping Mechanism
361 Setting an Early Stopping Mechanism – Exercise
362 Business Case Testing the Model
363 Business Case Final Exercise

Deep Learning – Conclusion
364 Summary on What You’ve Learned
365 What’s Further out there in terms of Machine Learning
366 DeepMind and Deep Learning
367 An overview of CNNs
368 An Overview of RNNs
369 An Overview of non-NN Approaches

Appendix Deep Learning – TensorFlow 1 Introduction
371 How to Install TensorFlow 1
372 A Note on Installing Packages in Anaconda
373 TensorFlow Intro
374 Actual Introduction to TensorFlow
375 Types of File Formats, supporting Tensors
376 Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases
377 Basic NN Example with TF Loss Function and Gradient Descent
378 Basic NN Example with TF Model Output
379 Basic NN Example with TF Exercises

Appendix Deep Learning – TensorFlow 1 Classifying on the MNIST Dataset
380 MNIST What is the MNIST Dataset
381 MNIST How to Tackle the MNIST
382 MNIST Relevant Packages
383 MNIST Model Outline
384 MNIST Loss and Optimization Algorithm
385 Calculating the Accuracy of the Model
386 MNIST Batching and Early Stopping
387 MNIST Learning
388 MNIST Results and Testing
389 MNIST Exercises
390 MNIST Solutions

Appendix Deep Learning – TensorFlow 1 Business Case
391 Business Case Getting Acquainted with the Dataset
392 Business Case Outlining the Solution
393 The Importance of Working with a Balanced Dataset
394 Business Case Preprocessing
395 Business Case Preprocessing Exercise
396 Creating a Data Provider
397 Business Case Model Outline
398 Business Case Optimization
399 Business Case Interpretation
400 Business Case Testing the Model
401 Business Case A Comment on the Homework
402 Business Case Final Exercise

Software Integration
403 What are Data, Servers, Clients, Requests, and Responses
404 What are Data Connectivity, APIs, and Endpoints
405 Taking a Closer Look at APIs
406 Communication between Software Products through Text Files
407 Software Integration – Explained

Case Study – What’s Next in the Course
408 Game Plan for this Python, SQL, and Tableau Business Exercise
409 The Business Task
410 Introducing the Data Set

Case Study – Preprocessing the ‘Absenteeism data’
411 What to Expect from the Following Sections
412 Importing the Absenteeism Data in Python
413 Checking the Content of the Data Set
414 Introduction to Terms with Multiple Meanings
415 What’s Regression Analysis – a Quick Refresher
416 Using a Statistical Approach towards the Solution to the Exercise
417 Dropping a Column from a DataFrame in Python
418 EXERCISE – Dropping a Column from a DataFrame in Python
419 SOLUTION – Dropping a Column from a DataFrame in Python
420 Analyzing the Reasons for Absence
421 Obtaining Dummies from a Single Feature
422 EXERCISE – Obtaining Dummies from a Single Feature
423 SOLUTION – Obtaining Dummies from a Single Feature
424 Dropping a Dummy Variable from the Data Set
425 More on Dummy Variables A Statistical Perspective
426 Classifying the Various Reasons for Absence
427 Using .concat() in Python
428 EXERCISE – Using .concat() in Python
429 SOLUTION – Using .concat() in Python
430 Reordering Columns in a Pandas DataFrame in Python
431 EXERCISE – Reordering Columns in a Pandas DataFrame in Python
432 SOLUTION – Reordering Columns in a Pandas DataFrame in Python
433 Creating Checkpoints while Coding in Jupyter
434 EXERCISE – Creating Checkpoints while Coding in Jupyter
435 SOLUTION – Creating Checkpoints while Coding in Jupyter
436 Analyzing the Dates from the Initial Data Set
437 Extracting the Month Value from the Date Column
438 Extracting the Day of the Week from the Date Column
439 EXERCISE – Removing the Date Column
440 Analyzing Several Straightforward Columns for this Exercise
441 Working on Education , Children , and Pets
442 Final Remarks of this Section
443 A Note on Exporting Your Data as a .csv File

Case Study – Applying Machine Learning to Create the ‘absenteeism module’
444 Exploring the Problem with a Machine Learning Mindset
445 Creating the Targets for the Logistic Regression
446 Selecting the Inputs for the Logistic Regression
447 Standardizing the Data
448 Splitting the Data for Training and Testing
449 Fitting the Model and Assessing its Accuracy
450 Creating a Summary Table with the Coefficients and Intercept
451 Interpreting the Coefficients for Our Problem
452 Standardizing only the Numerical Variables (Creating a Custom Scaler)
453 Interpreting the Coefficients of the Logistic Regression
454 Backward Elimination or How to Simplify Your Model
455 Testing the Model We Created
456 Saving the Model and Preparing it for Deployment
457 ARTICLE – A Note on ‘pickling’
458 EXERCISE – Saving the Model (and Scaler)
459 Preparing the Deployment of the Model through a Module

Case Study – Loading the ‘absenteeism module’
460 Are You Sure You’re All Set
461 Deploying the ‘absenteeism module’ – Part I
462 Deploying the ‘absenteeism module’ – Part II
463 Exporting the Obtained Data Set as a .csv

Case Study – Analyzing the Predicted Outputs in Tableau
464 EXERCISE – Age vs Probability
465 Analyzing Age vs Probability in Tableau
466 EXERCISE – Reasons vs Probability
467 Analyzing Reasons vs Probability in Tableau
468 EXERCISE – Transportation Expense vs Probability
469 Analyzing Transportation Expense vs Probability in Tableau

Appendix – Additional Python Tools
470 Using the .format() Method
471 Iterating Over Range Objects
472 Introduction to Nested For Loops
473 Triple Nested For Loops
474 List Comprehensions
475 Anonymous (Lambda) Functions

Appendix – pandas Fundamentals
476 Introduction to pandas Series
477 Working with Methods in Python – Part I
478 Working with Methods in Python – Part II
479 Parameters and Arguments in pandas
480 Using .unique() and .nunique()
481 Using .sort values()
482 Introduction to pandas DataFrames – Part I
483 Introduction to pandas DataFrames – Part II
484 pandas DataFrames – Common Attributes
485 Data Selection in pandas DataFrames
486 pandas DataFrames – Indexing with .iloc[]
487 pandas DataFrames – Indexing with .loc[]

Bonus Lecture
488 Bonus Lecture Next Steps