The Data Science Course: Complete Data Science Bootcamp 2023

31h 14m 14s
English
Paid

Data science is rapidly becoming one of the most critical fields in the 21st century. With its digital, programming-oriented, and analytical nature, the demand for data scientists is increasing, yet the supply of skilled professionals remains limited. Acquiring the necessary skills to become a data scientist can be challenging, with universities often lagging in developing specialized programs that are both affordable and time-efficient.

Comprehensive Data Science Training

Many online courses focus narrowly on specific topics, making it hard to see the complete data science picture. Our solution is to offer a course that covers all necessary aspects of data science in a structured manner.

Course Content

Data science is a multidisciplinary field covering numerous topics:

  • Understanding the data science field and various analysis types

  • Mathematics

  • Statistics

  • Python

  • Applying advanced statistical techniques in Python

  • Data Visualization

  • Machine Learning

  • Deep Learning

These topics are logically ordered to facilitate gradual learning. For example, understanding mathematics is crucial before delving into machine learning techniques. This course provides the most efficient and structured data science education online, consolidating all essential resources into one program.

Course Structure and Skills

1. Intro to Data and Data Science

Explore key data science concepts such as big data, business intelligence, and artificial intelligence. Understand their meanings and implications in the field.

Why learn it? Gain comprehensive insights into these buzzwords to better approach data-driven problem-solving.

2. Mathematics

Cover essential calculus and linear algebra, the backbone of data science programming and advanced machine learning algorithms.

Why learn it? These mathematical skills are crucial for understanding machine learning algorithms.

3. Statistics

Develop a scientific mindset to frame and test hypotheses, a foundational skill for any scientist.

Why learn it? The course equips you with essential tools and teaches you to apply a scientific approach to data interpretation.

4. Python

Learn Python, a versatile programming language pivotal for machine learning, with capabilities ranging from web development to data science.

Why learn it? Master Python's powerful libraries for data manipulation, visualization, and applying machine and deep learning models.

5. Tableau

Master Tableau for presenting and visualizing data insights in a business-friendly manner.

Why learn it? Convince stakeholders with compelling, easily understandable data visualizations.

6. Advanced Statistics

Delve into regressions, clustering, and factor analysis, crucial for accurate predictive modeling.

Why learn it? Excel in predictive modeling and enhance your expertise with advanced statistical methods.

7. Machine Learning

Explore machine learning and deep learning using TensorFlow, culminating in a solid foundation in the most influential techniques in the field.

Why learn it? Machine learning is integral to modern companies. Learn to build and optimize algorithms that can independently learn and improve.

Course Benefits

  • A $1250 comprehensive data science training program
  • Active Q&A support and a learning community
  • Complete data science skillset to enhance your resume
  • Certificate of completion and ongoing access to updates
  • Practical business case exercises to bolster job prospects

Seize the opportunity to transform your career. Don't wait, every day is a missed opportunity.

Course Requirements

  • No prior experience needed; we start from basics
  • Step-by-step Anaconda installation guidance
  • Microsoft Excel 2003, 2010, 2013, 2016, or 365

Target Audience

  • Aspiring data scientists and individuals interested in the field
  • Professionals seeking a promising career
  • Beginners starting from foundational principles

Learning Outcomes

  • Comprehensive data science toolbox skill set
  • Advanced skills to stand out in interviews
  • Ability to pre-process data and understand machine learning mathematics
  • Hands-on experience with Python for statistical analysis
  • Proficiency in machine learning algorithms and deep neural networks
  • Capability to apply skills to solve real-life business cases

About the Author: udemy

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By connecting students all over the world to the best instructors, Udemy is helping individuals reach their goals and pursue their dreams. Udemy is the leading global marketplace for teaching and learning, connecting millions of students to the skills they need to succeed. Udemy helps organizations of all kinds prepare for the ever-evolving future of work. Our curated collection of top-rated business and technical courses gives companies, governments, and nonprofits the power to develop in-house expertise and satisfy employees’ hunger for learning and development.

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#1: A Practical Example: What You Will Learn in This Course
All Course Lessons (424)
#Lesson TitleDurationAccess
1
A Practical Example: What You Will Learn in This Course Demo
05:07
2
What Does the Course Cover
03:35
3
Data Science and Business Buzzwords: Why are there so Many?
05:22
4
What is the difference between Analysis and Analytics
03:51
5
Business Analytics, Data Analytics, and Data Science: An Introduction
08:27
6
Continuing with BI, ML, and AI
09:32
7
A Breakdown of our Data Science Infographic
04:04
8
Applying Traditional Data, Big Data, BI, Traditional Data Science and ML
07:20
9
The Reason Behind These Disciplines
04:46
10
Techniques for Working with Traditional Data
08:15
11
Real Life Examples of Traditional Data
01:45
12
Techniques for Working with Big Data
04:27
13
Real Life Examples of Big Data
01:33
14
Business Intelligence (BI) Techniques
06:47
15
Real Life Examples of Business Intelligence (BI)
01:43
16
Techniques for Working with Traditional Methods
09:09
17
Real Life Examples of Traditional Methods
02:47
18
Machine Learning (ML) Techniques
06:56
19
Types of Machine Learning
08:14
20
Real Life Examples of Machine Learning (ML)
02:12
21
Necessary Programming Languages and Software Used in Data Science
05:52
22
Finding the Job - What to Expect and What to Look for
03:30
23
Debunking Common Misconceptions
04:11
24
The Basic Probability Formula
07:10
25
Computing Expected Values
05:30
26
Frequency
05:01
27
Events and Their Complements
05:27
28
Fundamentals of Combinatorics
01:05
29
Permutations and How to Use Them
03:22
30
Simple Operations with Factorials
03:36
31
Solving Variations with Repetition
03:00
32
Solving Variations without Repetition
03:49
33
Solving Combinations
04:52
34
Symmetry of Combinations
03:27
35
Solving Combinations with Separate Sample Spaces
02:53
36
Combinatorics in Real-Life: The Lottery
03:13
37
A Recap of Combinatorics
02:56
38
A Practical Example of Combinatorics
10:54
39
Sets and Events
04:26
40
Ways Sets Can Interact
03:46
41
Intersection of Sets
02:07
42
Union of Sets
04:52
43
Mutually Exclusive Sets
02:10
44
Dependence and Independence of Sets
03:02
45
The Conditional Probability Formula
04:17
46
The Law of Total Probability
03:04
47
The Additive Rule
02:22
48
The Multiplication Law
04:06
49
Bayes' Law
05:45
50
A Practical Example of Bayesian Inference
14:53
51
Fundamentals of Probability Distributions
06:30
52
Types of Probability Distributions
07:33
53
Characteristics of Discrete Distributions
02:01
54
Discrete Distributions: The Uniform Distribution
02:14
55
Discrete Distributions: The Bernoulli Distribution
03:28
56
Discrete Distributions: The Binomial Distribution
07:05
57
Discrete Distributions: The Poisson Distribution
05:28
58
Characteristics of Continuous Distributions
07:13
59
Continuous Distributions: The Normal Distribution
04:09
60
Continuous Distributions: The Standard Normal Distribution
04:26
61
Continuous Distributions: The Students' T Distribution
02:30
62
Continuous Distributions: The Chi-Squared Distribution
02:23
63
Continuous Distributions: The Exponential Distribution
03:16
64
Continuous Distributions: The Logistic Distribution
04:08
65
A Practical Example of Probability Distributions
15:04
66
Probability in Finance
07:47
67
Probability in Statistics
06:19
68
Probability in Data Science
04:48
69
Population and Sample
04:03
70
Types of Data
04:34
71
Levels of Measurement
03:44
72
Categorical Variables - Visualization Techniques
04:53
73
Numerical Variables - Frequency Distribution Table
03:10
74
The Histogram
02:15
75
Cross Tables and Scatter Plots
04:45
76
Mean, median and mode
04:21
77
Skewness
02:38
78
Variance
05:56
79
Standard Deviation and Coefficient of Variation
04:41
80
Covariance
03:24
81
Correlation Coefficient
03:18
82
Practical Example: Descriptive Statistics
16:17
83
Introduction
01:02
84
What is a Distribution
04:34
85
The Normal Distribution
03:55
86
The Standard Normal Distribution
03:32
87
Central Limit Theorem
04:21
88
Standard error
01:28
89
Estimators and Estimates
03:08
90
What are Confidence Intervals?
02:42
91
Confidence Intervals; Population Variance Known; Z-score
08:02
92
Confidence Interval Clarifications
04:39
93
Student's T Distribution
03:24
94
Confidence Intervals; Population Variance Unknown; T-score
04:37
95
Margin of Error
04:54
96
Confidence intervals. Two means. Dependent samples
06:05
97
Confidence intervals. Two means. Independent Samples (Part 1)
04:32
98
Confidence intervals. Two means. Independent Samples (Part 2)
03:58
99
Confidence intervals. Two means. Independent Samples (Part 3)
01:28
100
Practical Example: Inferential Statistics
10:07
101
Null vs Alternative Hypothesis
05:53
102
Rejection Region and Significance Level
07:06
103
Type I Error and Type II Error
04:15
104
Test for the Mean. Population Variance Known
06:35
105
p-value
04:14
106
Test for the Mean. Population Variance Unknown
04:50
107
Test for the Mean. Dependent Samples
05:19
108
Test for the mean. Independent Samples (Part 1)
04:23
109
Test for the mean. Independent Samples (Part 2)
04:27
110
Practical Example: Hypothesis Testing
07:17
111
Introduction to Programming
05:05
112
Why Python?
05:12
113
Why Jupyter?
03:30
114
Installing Python and Jupyter
06:50
115
Understanding Jupyter's Interface - the Notebook Dashboard
03:16
116
Prerequisites for Coding in the Jupyter Notebooks
06:16
117
Variables
03:38
118
Numbers and Boolean Values in Python
03:06
119
Python Strings
05:41
120
Using Arithmetic Operators in Python
03:24
121
The Double Equality Sign
01:34
122
How to Reassign Values
01:09
123
Add Comments
01:35
124
Understanding Line Continuation
00:50
125
Indexing Elements
01:19
126
Structuring with Indentation
01:45
127
Comparison Operators
02:11
128
Logical and Identity Operators
05:37
129
The IF Statement
03:02
130
The ELSE Statement
02:46
131
The ELIF Statement
05:35
132
A Note on Boolean Values
02:15
133
Defining a Function in Python
02:03
134
How to Create a Function with a Parameter
03:50
135
Defining a Function in Python - Part II
02:37
136
How to Use a Function within a Function
01:50
137
Conditional Statements and Functions
03:07
138
Functions Containing a Few Arguments
01:18
139
Built-in Functions in Python
03:57
140
Lists
08:19
141
List Slicing
04:32
142
Tuples
06:41
143
Dictionaries
08:28
144
For Loops
05:41
145
While Loops and Incrementing
05:11
146
Lists with the range() Function
06:23
147
Conditional Statements and Loops
06:31
148
Conditional Statements, Functions, and Loops
02:28
149
How to Iterate over Dictionaries
06:22
150
Object Oriented Programming
05:01
151
Modules and Packages
01:07
152
What is the Standard Library?
02:48
153
Importing Modules in Python
04:05
154
Introduction to Regression Analysis
01:28
155
The Linear Regression Model
05:51
156
Correlation vs Regression
01:45
157
Geometrical Representation of the Linear Regression Model
01:26
158
Python Packages Installation
04:40
159
First Regression in Python
07:12
160
Using Seaborn for Graphs
01:22
161
How to Interpret the Regression Table
05:48
162
Decomposition of Variability
03:39
163
What is the OLS?
03:14
164
R-Squared
05:31
165
Multiple Linear Regression
02:57
166
Adjusted R-Squared
06:01
167
Test for Significance of the Model (F-Test)
02:02
168
OLS Assumptions
02:22
169
A1: Linearity
01:51
170
A2: No Endogeneity
04:10
171
A3: Normality and Homoscedasticity
05:48
172
A4: No Autocorrelation
03:32
173
A5: No Multicollinearity
03:27
174
Dealing with Categorical Data - Dummy Variables
06:44
175
Making Predictions with the Linear Regression
03:30
176
What is sklearn and How is it Different from Other Packages
02:15
177
How are we Going to Approach this Section?
01:57
178
Simple Linear Regression with sklearn
05:39
179
Simple Linear Regression with sklearn - A StatsModels-like Summary Table
04:50
180
Multiple Linear Regression with sklearn
03:11
181
Calculating the Adjusted R-Squared in sklearn
04:47
182
Feature Selection (F-regression)
04:42
183
Creating a Summary Table with P-values
02:11
184
Feature Scaling (Standardization)
05:39
185
Feature Selection through Standardization of Weights
05:23
186
Predicting with the Standardized Coefficients
03:54
187
Underfitting and Overfitting
02:43
188
Train - Test Split Explained
06:55
189
Practical Example: Linear Regression (Part 1)
12:00
190
Practical Example: Linear Regression (Part 2)
06:13
191
Practical Example: Linear Regression (Part 3)
03:17
192
Practical Example: Linear Regression (Part 4)
08:11
193
Practical Example: Linear Regression (Part 5)
07:35
194
Introduction to Logistic Regression
01:20
195
A Simple Example in Python
04:43
196
Logistic vs Logit Function
04:01
197
Building a Logistic Regression
02:49
198
An Invaluable Coding Tip
02:27
199
Understanding Logistic Regression Tables
04:07
200
What do the Odds Actually Mean
04:31
201
Binary Predictors in a Logistic Regression
04:33
202
Calculating the Accuracy of the Model
03:22
203
Underfitting and Overfitting
03:44
204
Testing the Model
05:06
205
Introduction to Cluster Analysis
03:42
206
Some Examples of Clusters
04:32
207
Difference between Classification and Clustering
02:33
208
Math Prerequisites
03:21
209
K-Means Clustering
04:42
210
A Simple Example of Clustering
07:49
211
Clustering Categorical Data
02:51
212
How to Choose the Number of Clusters
06:12
213
Pros and Cons of K-Means Clustering
03:24
214
To Standardize or not to Standardize
04:34
215
Relationship between Clustering and Regression
01:32
216
Market Segmentation with Cluster Analysis (Part 1)
06:05
217
Market Segmentation with Cluster Analysis (Part 2)
06:59
218
How is Clustering Useful?
04:49
219
Types of Clustering
03:40
220
Dendrogram
05:22
221
Heatmaps
04:35
222
What is a Matrix?
03:38
223
Scalars and Vectors
02:59
224
Linear Algebra and Geometry
03:07
225
Arrays in Python - A Convenient Way To Represent Matrices
05:10
226
What is a Tensor?
03:01
227
Addition and Subtraction of Matrices
03:37
228
Errors when Adding Matrices
02:02
229
Transpose of a Matrix
05:14
230
Dot Product
03:49
231
Dot Product of Matrices
08:24
232
Why is Linear Algebra Useful?
10:11
233
What to Expect from this Part?
03:09
234
Introduction to Neural Networks
04:10
235
Training the Model
02:55
236
Types of Machine Learning
03:44
237
The Linear Model (Linear Algebraic Version)
03:09
238
The Linear Model with Multiple Inputs
02:26
239
The Linear model with Multiple Inputs and Multiple Outputs
04:27
240
Graphical Representation of Simple Neural Networks
01:48
241
What is the Objective Function?
01:28
242
Common Objective Functions: L2-norm Loss
02:05
243
Common Objective Functions: Cross-Entropy Loss
03:56
244
Optimization Algorithm: 1-Parameter Gradient Descent
06:34
245
Optimization Algorithm: n-Parameter Gradient Descent
06:09
246
Basic NN Example (Part 1)
03:07
247
Basic NN Example (Part 2)
05:00
248
Basic NN Example (Part 3)
03:26
249
Basic NN Example (Part 4)
08:16
250
How to Install TensorFlow 2.0
05:03
251
TensorFlow Outline and Comparison with Other Libraries
03:29
252
TensorFlow 1 vs TensorFlow 2
02:34
253
A Note on TensorFlow 2 Syntax
00:59
254
Types of File Formats Supporting TensorFlow
02:35
255
Outlining the Model with TensorFlow 2
05:49
256
Interpreting the Result and Extracting the Weights and Bias
04:10
257
Customizing a TensorFlow 2 Model
02:52
258
What is a Layer?
01:54
259
What is a Deep Net?
02:19
260
Digging into a Deep Net
04:59
261
Non-Linearities and their Purpose
03:00
262
Activation Functions
03:38
263
Activation Functions: Softmax Activation
03:25
264
Backpropagation
03:13
265
Backpropagation Picture
03:03
266
What is Overfitting?
03:52
267
Underfitting and Overfitting for Classification
01:53
268
What is Validation?
03:23
269
Training, Validation, and Test Datasets
02:31
270
N-Fold Cross Validation
03:08
271
Early Stopping or When to Stop Training
04:55
272
What is Initialization?
02:33
273
Types of Simple Initializations
02:48
274
State-of-the-Art Method - (Xavier) Glorot Initialization
02:46
275
Stochastic Gradient Descent
03:25
276
Problems with Gradient Descent
02:03
277
Momentum
02:31
278
Learning Rate Schedules, or How to Choose the Optimal Learning Rate
04:26
279
Learning Rate Schedules Visualized
01:33
280
Adaptive Learning Rate Schedules (AdaGrad and RMSprop )
04:09
281
Adam (Adaptive Moment Estimation)
02:40
282
Preprocessing Introduction
02:52
283
Types of Basic Preprocessing
01:18
284
Standardization
04:32
285
Preprocessing Categorical Data
02:16
286
Binary and One-Hot Encoding
03:40
287
MNIST: The Dataset
02:26
288
MNIST: How to Tackle the MNIST
02:45
289
MNIST: Importing the Relevant Packages and Loading the Data
02:12
290
MNIST: Preprocess the Data - Create a Validation Set and Scale It
04:44
291
MNIST: Preprocess the Data - Shuffle and Batch
06:31
292
MNIST: Outline the Model
04:55
293
MNIST: Select the Loss and the Optimizer
02:06
294
MNIST: Learning
05:39
295
MNIST: Testing the Model
03:57
296
Business Case: Exploring the Dataset and Identifying Predictors
07:55
297
Business Case: Outlining the Solution
01:32
298
Business Case: Balancing the Dataset
03:40
299
Business Case: Preprocessing the Data
11:33
300
Business Case: Load the Preprocessed Data
03:24
301
Business Case: Learning and Interpreting the Result
04:16
302
Business Case: Setting an Early Stopping Mechanism
05:02
303
Business Case: Testing the Model
01:24
304
Summary on What You've Learned
03:42
305
What's Further out there in terms of Machine Learning
01:48
306
An overview of CNNs
04:57
307
An Overview of RNNs
02:51
308
An Overview of non-NN Approaches
03:53
309
How to Install TensorFlow 1
02:21
310
TensorFlow Intro
03:47
311
Actual Introduction to TensorFlow
01:41
312
Types of File Formats, supporting Tensors
02:39
313
Basic NN Example with TF: Inputs, Outputs, Targets, Weights, Biases
06:06
314
Basic NN Example with TF: Loss Function and Gradient Descent
03:42
315
Basic NN Example with TF: Model Output
06:06
316
MNIST: What is the MNIST Dataset?
02:27
317
MNIST: How to Tackle the MNIST
02:49
318
MNIST: Relevant Packages
01:36
319
MNIST: Model Outline
06:52
320
MNIST: Loss and Optimization Algorithm
02:40
321
Calculating the Accuracy of the Model
04:19
322
MNIST: Batching and Early Stopping
02:09
323
MNIST: Learning
07:36
324
MNIST: Results and Testing
06:12
325
Business Case: Getting Acquainted with the Dataset
07:56
326
Business Case: Outlining the Solution
01:58
327
The Importance of Working with a Balanced Dataset
03:40
328
Business Case: Preprocessing
11:36
329
Creating a Data Provider
06:38
330
Business Case: Model Outline
05:36
331
Business Case: Optimization
05:11
332
Business Case: Interpretation
02:06
333
Business Case: Testing the Model
02:05
334
Business Case: A Comment on the Homework
03:52
335
What are Data, Servers, Clients, Requests, and Responses
04:45
336
What are Data Connectivity, APIs, and Endpoints?
07:06
337
Taking a Closer Look at APIs
08:06
338
Communication between Software Products through Text Files
04:22
339
Software Integration - Explained
05:26
340
Game Plan for this Python, SQL, and Tableau Business Exercise
04:09
341
The Business Task
02:49
342
Introducing the Data Set
03:19
343
Importing the Absenteeism Data in Python
03:24
344
Checking the Content of the Data Set
05:54
345
Introduction to Terms with Multiple Meanings
03:29
346
Using a Statistical Approach towards the Solution to the Exercise
02:18
347
Dropping a Column from a DataFrame in Python
06:28
348
Analyzing the Reasons for Absence
05:05
349
Obtaining Dummies from a Single Feature
08:38
350
More on Dummy Variables: A Statistical Perspective
01:29
351
Classifying the Various Reasons for Absence
08:36
352
Using .concat() in Python
04:36
353
Reordering Columns in a Pandas DataFrame in Python
01:44
354
Creating Checkpoints while Coding in Jupyter
02:53
355
Analyzing the Dates from the Initial Data Set
07:50
356
Extracting the Month Value from the "Date" Column
07:01
357
Extracting the Day of the Week from the "Date" Column
03:37
358
Analyzing Several "Straightforward" Columns for this Exercise
03:18
359
Working on "Education", "Children", and "Pets"
04:39
360
Final Remarks of this Section
02:00
361
Exploring the Problem with a Machine Learning Mindset
03:21
362
Creating the Targets for the Logistic Regression
06:33
363
Selecting the Inputs for the Logistic Regression
02:43
364
Standardizing the Data
03:27
365
Splitting the Data for Training and Testing
06:14
366
Fitting the Model and Assessing its Accuracy
05:40
367
Creating a Summary Table with the Coefficients and Intercept
05:17
368
Interpreting the Coefficients for Our Problem
06:15
369
Standardizing only the Numerical Variables (Creating a Custom Scaler)
04:13
370
Interpreting the Coefficients of the Logistic Regression
05:12
371
Backward Elimination or How to Simplify Your Model
04:03
372
Testing the Model We Created
04:44
373
Saving the Model and Preparing it for Deployment
04:07
374
Preparing the Deployment of the Model through a Module
04:05
375
Deploying the 'absenteeism_module' - Part I
03:51
376
Deploying the 'absenteeism_module' - Part II
06:25
377
Analyzing Age vs Probability in Tableau
08:50
378
Analyzing Reasons vs Probability in Tableau
07:50
379
Analyzing Transportation Expense vs Probability in Tableau
06:02
380
Using the .format() Method
09:04
381
Iterating Over Range Objects
04:18
382
Introduction to Nested For Loops
06:00
383
Triple Nested For Loops
05:38
384
List Comprehensions
08:31
385
Anonymous (Lambda) Functions
07:01
386
Introduction to pandas Series
08:34
387
Working with Methods in Python - Part I
04:50
388
Working with Methods in Python - Part II
02:33
389
Parameters and Arguments in pandas
04:10
390
Using .unique() and .nunique()
03:50
391
Using .sort_values()
03:59
392
Introduction to pandas DataFrames - Part I
04:42
393
Introduction to pandas DataFrames - Part II
05:06
394
pandas DataFrames - Common Attributes
04:16
395
Data Selection in pandas DataFrames
06:56
396
pandas DataFrames - Indexing with .iloc[]
05:57
397
pandas DataFrames - Indexing with .loc[]
03:53
398
An Introduction to Working with Files in Python
03:47
399
File vs File Object, Reading vs Parsing Data
02:53
400
Structured, Semi-Structured and Unstructured Data
03:11
401
Text Files and Data Connectivity
03:07
402
Importing Data in Python - Principles
04:51
403
Plain Text Files, Flat Files and More
04:34
404
Text Files of Fixed Width
01:27
405
Common Naming Conventions
03:50
406
Importing Text Files - open()
09:01
407
Importing Text Files - with open()
04:54
408
Importing *.csv Files - Part I
05:36
409
Importing *.csv Files - Part II
02:38
410
Importing *.csv Files - Part III
05:58
411
Importing Data with index_col
02:36
412
Importing Data with .loadtxt() and .genfromtxt()
10:45
413
Importing Data - Partial Cleaning While Importing Data
07:22
414
Importing Data from *.json Files
05:16
415
An Introduction to Working with Excel Files in Python
03:41
416
Working with Excel (*.xlsx) Data
01:56
417
Importing Data in Python - an Important Exercise
05:45
418
Importing Data with the .squeeze() Method
03:24
419
Importing Files in Jupyter
03:11
420
Saving Your Data with pandas
03:12
421
Saving Your Data with NumPy - Part I - *.npy
05:24
422
Saving Your Data with NumPy - Part II - *.npz
05:13
423
Saving Your Data with NumPy - Part III - *.csv
03:59
424
Working with Text Files in Python - Conclusion
00:43
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