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**

370 READ ME

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

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