Data Scientist Bootcamp | Udemy

Source: Data Science Training Course: Data Scientist Bootcamp | Udemy

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

Requirements

  • No prior experience is required. We will start from the very basics
  • You’ll need to install Anaconda. We will show you how to do that step by step
  • Microsoft Excel 2003, 2010, 2013, 2016, or 365

Description

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 2023.

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.

Why learn it?

Machine learning is everywhere. Companies like Facebook, Google, and Amazon have been using machines that can learn on their own for years. Now is the time for you to control the machines.

***What you get***

  • A $1250 data science training program
  • Active Q&A support
  • All the knowledge to get hired as a data scientist
  • A community of data science learners
  • A certificate of completion
  • Access to future updates
  • Solve real-life business cases that will get you the job

You will become a data scientist from scratch   We are happy to offer an unconditional 30-day money back in full guarantee. No risk for you. The content of the course is excellent, and this is a no-brainer for us, as we are certain you will love it.

Why wait? Every day is a missed opportunity.

Click the “Buy Now” button and become a part of our data scientist program today.  

 

Who this course is for:

  • You should take this course if you want to become a Data Scientist or if you want to learn about the field
  • This course is for you if you want a great career
  • The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills

Course content

65 sections • 518 lectures • 31h 52m total length

  • Download All Resources and Important FAQ

    10:42

  • Data Science and Business Buzzwords: Why are there so Many?

    Preview05:21

  • Data Science and Business Buzzwords: Why are there so Many?

    1 question

  • What is the difference between Analysis and Analytics

    03:50

  • What is the difference between Analysis and Analytics

    1 question

  • Business Analytics, Data Analytics, and Data Science: An Introduction

    Preview08:26

  • Business Analytics, Data Analytics, and Data Science: An Introduction

    2 questions

  • Continuing with BI, ML, and AI

    09:31

  • Continuing with BI, ML, and AI

    2 questions

  • A Breakdown of our Data Science Infographic

    04:03

  • A Breakdown of our Data Science Infographic

    1 question

  • Applying Traditional Data, Big Data, BI, Traditional Data Science and ML

    07:19

  • The Reason Behind These Disciplines

    04:44

  • The Reason Behind These Disciplines

    1 question

  • Techniques for Working with Traditional Data

    08:13

  • Techniques for Working with Traditional Data

    1 question

  • Real Life Examples of Traditional Data

    01:44

  • Techniques for Working with Big Data

    04:26

  • Techniques for Working with Big Data

    1 question

  • Real Life Examples of Big Data

    01:32

  • Business Intelligence (BI) Techniques

    06:45

  • Business Intelligence (BI) Techniques

    4 questions

  • Real Life Examples of Business Intelligence (BI)

    01:42

  • Techniques for Working with Traditional Methods

    09:08

  • Techniques for Working with Traditional Methods

    4 questions

  • Real Life Examples of Traditional Methods

    02:45

  • Machine Learning (ML) Techniques

    06:55

  • Machine Learning (ML) Techniques

    2 questions

  • Types of Machine Learning

    08:13

  • Types of Machine Learning

    2 questions

  • Real Life Examples of Machine Learning (ML)

    02:11

  • Real Life Examples of Machine Learning (ML)

    4 questions

  • Necessary Programming Languages and Software Used in Data Science

    05:51

  • Necessary Programming Languages and Software Used in Data Science

    4 questions

  • Finding the Job – What to Expect and What to Look for

    03:29

  • Finding the Job – What to Expect and What to Look for

    1 question

  • Debunking Common Misconceptions

    04:10

  • Debunking Common Misconceptions

    1 question

  • The Basic Probability Formula

    07:09

  • The Basic Probability Formula

    3 questions

  • Computing Expected Values

    05:29

  • Computing Expected Values

    3 questions

  • Frequency

    05:00

  • Frequency

    3 questions

  • Events and Their Complements

    05:26

  • Events and Their Complements

    3 questions

  • Fundamentals of Combinatorics

    01:04

  • Fundamentals of Combinatorics

    1 question

  • Permutations and How to Use Them

    03:21

  • Permutations and How to Use Them

    2 questions

  • Simple Operations with Factorials

    03:35

  • Simple Operations with Factorials

    3 questions

  • Solving Variations with Repetition

    02:59

  • Solving Variations with Repetition

    3 questions

  • Solving Variations without Repetition

    03:48

  • Solving Variations without Repetition

    3 questions

  • Solving Combinations

    04:51

  • Solving Combinations

    4 questions

  • Symmetry of Combinations

    03:26

  • Symmetry of Combinations

    1 question

  • Solving Combinations with Separate Sample Spaces

    02:52

  • Solving Combinations with Separate Sample Spaces

    1 question

  • Combinatorics in Real-Life: The Lottery

    03:12

  • Combinatorics in Real-Life: The Lottery

    1 question

  • A Recap of Combinatorics

    02:55

  • A Practical Example of Combinatorics

    10:53

  • Sets and Events

    04:25

  • Sets and Events

    3 questions

  • Ways Sets Can Interact

    03:45

  • Ways Sets Can Interact

    2 questions

  • Intersection of Sets

    02:06

  • Intersection of Sets

    3 questions

  • Union of Sets

    04:51

  • Union of Sets

    3 questions

  • Mutually Exclusive Sets

    02:09

  • Mutually Exclusive Sets

    4 questions

  • Dependence and Independence of Sets

    03:01

  • Dependence and Independence of Sets

    3 questions

  • The Conditional Probability Formula

    04:16

  • The Conditional Probability Formula

    3 questions

  • The Law of Total Probability

    03:03

  • The Additive Rule

    02:21

  • The Additive Rule

    2 questions

  • The Multiplication Law

    04:05

  • The Multiplication Law

    2 questions

  • Bayes’ Law

    05:44

  • Bayes’ Law

    2 questions

  • A Practical Example of Bayesian Inference

    14:52

  • Fundamentals of Probability Distributions

    06:29

  • Fundamentals of Probability Distributions

    3 questions

  • Types of Probability Distributions

    07:32

  • Types of Probability Distributions

    2 questions

  • Characteristics of Discrete Distributions

    02:00

  • Characteristics of Discrete Distributions

    2 questions

  • Discrete Distributions: The Uniform Distribution

    02:13

  • Discrete Distributions: The Uniform Distribution

    2 questions

  • Discrete Distributions: The Bernoulli Distribution

    03:26

  • Discrete Distributions: The Bernoulli Distribution

    1 question

  • Discrete Distributions: The Binomial Distribution

    07:04

  • Discrete Distributions: The Binomial Distribution

    1 question

  • Discrete Distributions: The Poisson Distribution

    05:27

  • Discrete Distributions: The Poisson Distribution

    1 question

  • Characteristics of Continuous Distributions

    07:12

  • Characteristics of Continuous Distributions

    1 question

  • Continuous Distributions: The Normal Distribution

    04:08

  • Continuous Distributions: The Normal Distribution

    1 question

  • Continuous Distributions: The Standard Normal Distribution

    04:25

  • Continuous Distributions: The Standard Normal Distribution

    1 question

  • Continuous Distributions: The Students’ T Distribution

    02:29

  • Continuous Distributions: The Students’ T Distribution

    1 question

  • Continuous Distributions: The Chi-Squared Distribution

    02:22

  • Continuous Distributions: The Chi-Squared Distribution

    1 question

  • Continuous Distributions: The Exponential Distribution

    03:15

  • Continuous Distributions: The Exponential Distribution

    1 question

  • Continuous Distributions: The Logistic Distribution

    04:07

  • Continuous Distributions: The Logistic Distribution

    1 question

  • A Practical Example of Probability Distributions

    15:03

  • Probability in Finance

    07:46

  • Probability in Statistics

    06:18

  • Probability in Data Science

    04:47

  • Population and Sample

    04:02

  • Population and Sample

    2 questions

  • Types of Data

    04:33

  • Types of Data

    2 questions

  • Levels of Measurement

    03:43

  • Levels of Measurement

    2 questions

  • Categorical Variables – Visualization Techniques

    1 question

  • Categorical Variables Exercise

    00:03

  • Numerical Variables – Frequency Distribution Table

    03:09

  • Numerical Variables – Frequency Distribution Table

    1 question

  • Numerical Variables Exercise

    00:03

  • The Histogram

    02:14

  • The Histogram

    1 question

  • Histogram Exercise

    00:03

  • Cross Tables and Scatter Plots

    04:44

  • Cross Tables and Scatter Plots

    1 question

  • Cross Tables and Scatter Plots Exercise

    00:03

  • Mean, median and mode

    04:20

  • Mean, Median and Mode Exercise

    00:03

  • Skewness

    02:37

  • Skewness

    1 question

  • Skewness Exercise

    00:03

  • Variance

    05:55

  • Variance Exercise

    00:15

  • Standard Deviation and Coefficient of Variation

    04:40

  • Standard Deviation

    1 question

  • Standard Deviation and Coefficient of Variation Exercise

    00:03

  • Covariance

    03:23

  • Covariance

    1 question

  • Covariance Exercise

    00:03

  • Correlation Coefficient

    03:17

  • Correlation

    2 questions

  • Correlation Coefficient Exercise

    00:03

  • Practical Example: Descriptive Statistics Exercise

    00:03

  • Introduction

    01:00

  • What is a Distribution

    04:33

  • What is a Distribution

    1 question

  • The Normal Distribution

    03:54

  • The Normal Distribution

    1 question

  • The Standard Normal Distribution

    03:30

  • The Standard Normal Distribution

    1 question

  • The Standard Normal Distribution Exercise

    00:03

  • Central Limit Theorem

    04:20

  • Central Limit Theorem

    1 question

  • Standard error

    01:26

  • Standard Error

    1 question

  • Estimators and Estimates

    03:07

  • Estimators and Estimates

    1 question

  • What are Confidence Intervals?

    02:41

  • What are Confidence Intervals?

    1 question

  • Confidence Intervals; Population Variance Known; Z-score

    08:01

  • Confidence Intervals; Population Variance Known; Z-score; Exercise

    00:03

  • Confidence Interval Clarifications

    04:38

  • Student’s T Distribution

    03:22

  • Student’s T Distribution

    1 question

  • Confidence Intervals; Population Variance Unknown; T-score

    04:36

  • Confidence Intervals; Population Variance Unknown; T-score; Exercise

    00:03

  • Margin of Error

    04:52

  • Margin of Error

    1 question

  • Confidence intervals. Two means. Dependent samples

    06:04

  • Confidence intervals. Two means. Dependent samples Exercise

    00:03

  • Confidence intervals. Two means. Independent Samples (Part 1)

    04:31

  • Confidence intervals. Two means. Independent Samples (Part 1). Exercise

    00:03

  • Confidence intervals. Two means. Independent Samples (Part 2)

    03:57

  • Confidence intervals. Two means. Independent Samples (Part 2). Exercise

    00:03

  • Confidence intervals. Two means. Independent Samples (Part 3)

    01:27

  • Practical Example: Inferential Statistics

    10:05

  • Practical Example: Inferential Statistics Exercise

    00:03

  • Further Reading on Null and Alternative Hypothesis

    01:16

  • Null vs Alternative Hypothesis

    2 questions

  • Rejection Region and Significance Level

    07:05

  • Rejection Region and Significance Level

    2 questions

  • Type I Error and Type II Error

    04:14

  • Type I Error and Type II Error

    4 questions

  • Test for the Mean. Population Variance Known

    06:34

  • Test for the Mean. Population Variance Known Exercise

    00:03

  • p-value

    04:13

  • p-value

    4 questions

  • Test for the Mean. Population Variance Unknown

    04:48

  • Test for the Mean. Population Variance Unknown Exercise

    00:03

  • Test for the Mean. Dependent Samples

    05:18

  • Test for the Mean. Dependent Samples Exercise

    00:03

  • Test for the mean. Independent Samples (Part 1)

    04:22

  • Test for the mean. Independent Samples (Part 1). Exercise

    00:03

  • Test for the mean. Independent Samples (Part 2)

    04:26

  • Test for the mean. Independent Samples (Part 2)

    1 question

  • Test for the mean. Independent Samples (Part 2). Exercise

    00:03

  • Practical Example: Hypothesis Testing

    07:16

  • Practical Example: Hypothesis Testing Exercise

    00:03

  • Introduction to Programming

    05:04

  • Introduction to Programming

    2 questions

  • Why Python?

    05:11

  • Why Python?

    2 questions

  • Why Jupyter?

    03:29

  • Why Jupyter?

    2 questions

  • Installing Python and Jupyter

    06:49

  • Understanding Jupyter’s Interface – the Notebook Dashboard

    03:15

  • Prerequisites for Coding in the Jupyter Notebooks

    06:15

  • Jupyter’s Interface

    3 questions

  • Variables

    03:37

  • Python Variables – Exercise #1

    1 question

  • Python Variables – Exercise #2

    1 question

  • Python Variables – Exercise #3

    1 question

  • Python Variables – Exercise #4

    1 question

  • Variables

    1 question

  • Numbers and Boolean Values in Python

    03:05

  • Numbers and Boolean Values – Exercise #1

    1 question

  • Numbers and Boolean Values – Exercise #2

    1 question

  • Numbers and Boolean Values – Exercise #3

    1 question

  • Numbers and Boolean Values – Exercise #4

    1 question

  • Numbers and Boolean Values – Exercise #5

    1 question

  • Numbers and Boolean Values in Python

    1 question

  • Python Strings

    05:40

  • Python Strings – Exercise #1

    1 question

  • Python Strings – Exercise #2

    1 question

  • Python Strings – Exercise #3

    1 question

  • Python Strings – Exercise #4

    1 question

  • Python Strings – Exercise #5

    1 question

  • Python Strings

    3 questions

  • Using Arithmetic Operators in Python

    03:23

  • Using Arithmetic Operators in Python – Exercise #1

    1 question

  • Using Arithmetic Operators in Python – Exercise #2

    1 question

  • Using Arithmetic Operators in Python – Exercise #3

    1 question

  • Using Arithmetic Operators in Python – Exercise #4

    1 question

  • Using Arithmetic Operators in Python – Exercise #5

    1 question

  • Using Arithmetic Operators in Python – Exercise #6

    1 question

  • Using Arithmetic Operators in Python – Exercise #7

    1 question

  • Using Arithmetic Operators in Python – Exercise #8

    1 question

  • Using Arithmetic Operators in Python

    1 question

  • The Double Equality Sign

    01:33

  • The Double Equality Sign – Exercise #1

    1 question

  • The Double Equality Sign

    1 question

  • How to Reassign Values

    01:08

  • How to Reassign Values – Exercise #1

    1 question

  • How to Reassign Values – Exercise #2

    1 question

  • How to Reassign Values – Exercise #3

    1 question

  • How to Reassign Values – Exercise #4

    1 question

  • How to Reassign Values

    1 question

  • Add Comments

    01:34

  • Add Comments

    1 question

  • Understanding Line Continuation

    00:49

  • Understanding Line Continuation – Exercise #1

    1 question

  • Indexing Elements

    01:18

  • Indexing Elements – Exercise #1

    1 question

  • Indexing Elements – Exercise #2

    1 question

  • Indexing Elements

    1 question

  • Structuring with Indentation

    01:44

  • Structuring with Indentation – Exercise #1

    1 question

  • Structuring with Indentation

    1 question

  • Comparison Operators

    02:10

  • Comparison Operators – Exercise #1

    1 question

  • Comparison Operators – Exercise #2

    1 question

  • Comparison Operators – Exercise #3

    1 question

  • Comparison Operators – Exercise #4

    1 question

  • Comparison Operators

    2 questions

  • Logical and Identity Operators

    05:35

  • Logical and Identity Operators – Exercise #1

    1 question

  • Logical and Identity Operators – Exercise #2

    1 question

  • Logical and Identity Operators – Exercise #3

    1 question

  • Logical and Identity Operators – Exercise #4

    1 question

  • Logical and Identity Operators – Exercise #5

    1 question

  • Logical and Identity Operators – Exercise #6

    1 question

  • Logical and Identity Operators

    2 questions

  • The IF Statement

    03:01

  • The IF Statement – Exercise #1

    1 question

  • The IF Statement – Exercise #2

    1 question

  • The IF Statement

    1 question

  • The ELSE Statement

    02:45

  • The ELSE Statement – Exercise #1

    1 question

  • The ELIF Statement

    05:34

  • The ELIF Statement – Exercise #1

    1 question

  • The ELIF Statement – Exercise #2

    1 question

  • A Note on Boolean Values

    02:13

  • A Note on Boolean Values

    1 question

  • Defining a Function in Python

    02:02

  • How to Create a Function with a Parameter

    03:49

  • How to Create a Function with a Parameter – Exercise #1

    1 question

  • How to Create a Function with a Parameter – Exercise #2

    1 question

  • Defining a Function in Python – Part II

    02:36

  • Defining a Function in Python – Exercise #1

    1 question

  • How to Use a Function within a Function

    01:49

  • How to Use a Function within a Function – Exercise #1

    1 question

  • Conditional Statements and Functions

    03:06

  • Conditional Statements and Functions – Exercise #1

    1 question

  • Functions Containing a Few Arguments

    01:16

  • Built-in Functions in Python

    03:56

  • Built-in Functions in Python – Exercise #1

    1 question

  • Built-in Functions in Python – Exercise #2

    1 question

  • Built-in Functions in Python – Exercise #3

    1 question

  • Built-in Functions in Python – Exercise #4

    1 question

  • Built-in Functions in Python – Exercise #5

    1 question

  • Built-in Functions in Python – Exercise #6

    1 question

  • Built-in Functions in Python – Exercise #7

    1 question

  • Built-in Functions in Python – Exercise #8

    1 question

  • Built-in Functions in Python – Exercise #9

    1 question

  • Python Functions

    2 questions

  • Lists

    08:18

  • Lists – Exercise #1

    1 question

  • Lists – Exercise #2

    1 question

  • Lists – Exercise #3

    1 question

  • Lists – Exercise #4

    1 question

  • Lists – Exercise #5

    1 question

  • Lists

    1 question

  • Using Methods

    06:54

  • Using Methods – Exercise #1

    1 question

  • Using Methods – Exercise #2

    1 question

  • Using Methods – Exercise #3

    1 question

  • Using Methods – Exercise #4

    1 question

  • Using Methods

    1 question

  • List Slicing

    04:30

  • List Slicing – Exercise #1

    1 question

  • List Slicing – Exercise #2

    1 question

  • List Slicing – Exercise #3

    1 question

  • List Slicing – Exercise #4

    1 question

  • List Slicing – Exercise #5

    1 question

  • List Slicing – Exercise #6

    1 question

  • List Slicing – Exercise #7

    1 question

  • Tuples

    06:40

  • Tuples – Exercise #1

    1 question

  • Tuples – Exercise #2

    1 question

  • Tuples – Exercise #3

    1 question

  • Tuples – Exercise #4

    1 question

  • Dictionaries

    08:27

  • Dictionaries – Exercise #1

    1 question

  • Dictionaries – Exercise #2

    1 question

  • Dictionaries – Exercise #3

    1 question

  • Dictionaries – Exercise #4

    1 question

  • Dictionaries – Exercise #5

    1 question

  • Dictionaries – Exercise #6

    1 question

  • Dictionaries

    1 question

  • For Loops – Exercise #1

    1 question

  • For Loops – Exercise #2

    1 question

  • For Loops

    1 question

  • While Loops and Incrementing

    05:10

  • While Loops and Incrementing – Exercise #1

    1 question

  • Lists with the range() Function

    06:22

  • Lists with the range() Function – Exercise #1

    1 question

  • Lists with the range() Function – Exercise #2

    1 question

  • Lists with the range() Function – Exercise #3

    1 question

  • Lists with the range() Function

    1 question

  • Conditional Statements and Loops

    06:30

  • Conditional Statements and Loops – Exercise #1

    1 question

  • Conditional Statements and Loops – Exercise #2

    1 question

  • Conditional Statements and Loops – Exercise #3

    1 question

  • Conditional Statements, Functions, and Loops

    02:27

  • Conditional Statements, Functions, and Loops – Exercise #1

    1 question

  • How to Iterate over Dictionaries

    06:21

  • How to Iterate over Dictionaries – Exercise #1

    1 question

  • How to Iterate over Dictionaries – Exercise #2

    1 question

  • Object Oriented Programming

    05:00

  • Object Oriented Programming

    2 questions

  • Modules and Packages

    01:05

  • Modules and Packages

    2 questions

  • What is the Standard Library?

    02:47

  • What is the Standard Library?

    1 question

  • Importing Modules in Python

    04:04

  • Importing Modules in Python

    2 questions

  • Introduction to Regression Analysis

    01:27

  • Introduction to Regression Analysis

    1 question

  • The Linear Regression Model

    05:50

  • The Linear Regression Model

    2 questions

  • Correlation vs Regression

    01:43

  • Correlation vs Regression

    1 question

  • Geometrical Representation of the Linear Regression Model

    01:25

  • Geometrical Representation of the Linear Regression Model

    1 question

  • Python Packages Installation

    04:39

  • First Regression in Python

    07:11

  • First Regression in Python Exercise

    00:39

  • Using Seaborn for Graphs

    01:21

  • How to Interpret the Regression Table

    05:47

  • How to Interpret the Regression Table

    3 questions

  • Decomposition of Variability

    03:37

  • Decomposition of Variability

    1 question

  • What is the OLS?

    03:13

  • What is the OLS

    1 question

  • R-Squared

    05:30

  • R-Squared

    2 questions

  • Multiple Linear Regression

    02:55

  • Multiple Linear Regression

    1 question

  • Adjusted R-Squared

    06:00

  • Adjusted R-Squared

    3 questions

  • Multiple Linear Regression Exercise

    00:03

  • Test for Significance of the Model (F-Test)

    02:01

  • OLS Assumptions

    02:21

  • OLS Assumptions

    1 question

  • A1: Linearity

    01:50

  • A1: Linearity

    2 questions

  • A2: No Endogeneity

    04:09

  • A2: No Endogeneity

    1 question

  • A3: Normality and Homoscedasticity

    05:47

  • A4: No Autocorrelation

    03:31

  • A4: No autocorrelation

    2 questions

  • A5: No Multicollinearity

    03:26

  • A5: No Multicollinearity

    1 question

  • Dealing with Categorical Data – Dummy Variables

    06:43

  • Dealing with Categorical Data – Dummy Variables

    00:03

  • Making Predictions with the Linear Regression

    03:29

  • What is sklearn and How is it Different from Other Packages

    02:14

  • How are we Going to Approach this Section?

    01:55

  • A Note on Normalization

    00:09

  • Simple Linear Regression with sklearn – Exercise

    00:03

  • Multiple Linear Regression with sklearn

    03:10

  • Calculating the Adjusted R-Squared in sklearn

    04:45

  • Calculating the Adjusted R-Squared in sklearn – Exercise

    00:03

  • Feature Selection (F-regression)

    04:41

  • A Note on Calculation of P-values with sklearn

    00:13

  • Creating a Summary Table with P-values

    02:10

  • Multiple Linear Regression – Exercise

    00:03

  • Feature Scaling (Standardization)

    05:38

  • Feature Selection through Standardization of Weights

    05:22

  • Predicting with the Standardized Coefficients

    03:53

  • Feature Scaling (Standardization) – Exercise

    00:03

  • Underfitting and Overfitting

    02:42

  • Train – Test Split Explained

    06:54

  • Practical Example: Linear Regression (Part 1)

    11:59

  • Practical Example: Linear Regression (Part 2)

    06:12

  • A Note on Multicollinearity

    00:14

  • Practical Example: Linear Regression (Part 3)

    03:15

  • Dummies and Variance Inflation Factor – Exercise

    00:03

  • Practical Example: Linear Regression (Part 4)

    08:09

  • Dummy Variables – Exercise

    00:14

  • Practical Example: Linear Regression (Part 5)

    07:34

  • Linear Regression – Exercise

    00:16

  • Introduction to Logistic Regression

    01:19

  • A Simple Example in Python

    04:42

  • Logistic vs Logit Function

    04:00

  • Building a Logistic Regression

    02:48

  • Building a Logistic Regression – Exercise

    00:03

  • An Invaluable Coding Tip

    02:26

  • Understanding Logistic Regression Tables

    04:06

  • Understanding Logistic Regression Tables – Exercise

    00:03

  • What do the Odds Actually Mean

    04:30

  • Binary Predictors in a Logistic Regression

    04:32

  • Binary Predictors in a Logistic Regression – Exercise

    00:03

  • Calculating the Accuracy of the Model

    03:21

  • Calculating the Accuracy of the Model

    00:03

  • Underfitting and Overfitting

    03:43

  • Testing the Model

    05:05

  • Testing the Model – Exercise

    00:03

  • Introduction to Cluster Analysis

    03:41

  • Some Examples of Clusters

    04:31

  • Difference between Classification and Clustering

    02:32

  • Math Prerequisites

    03:19

  • K-Means Clustering

    04:41

  • A Simple Example of Clustering

    07:48

  • A Simple Example of Clustering – Exercise

    00:03

  • Clustering Categorical Data

    02:50

  • Clustering Categorical Data – Exercise

    00:03

  • How to Choose the Number of Clusters

    06:11

  • How to Choose the Number of Clusters – Exercise

    00:03

  • Pros and Cons of K-Means Clustering

    03:23

  • To Standardize or not to Standardize

    04:32

  • Relationship between Clustering and Regression

    01:31

  • Market Segmentation with Cluster Analysis (Part 1)

    06:03

  • Market Segmentation with Cluster Analysis (Part 2)

    06:58

  • How is Clustering Useful?

    04:47

  • EXERCISE: Species Segmentation with Cluster Analysis (Part 1)

    00:03

  • EXERCISE: Species Segmentation with Cluster Analysis (Part 2)

    00:03

  • Types of Clustering

    03:39

  • Dendrogram

    05:21

  • What is a Matrix?

    03:37

  • What is a Matrix?

    6 questions

  • Scalars and Vectors

    02:58

  • Scalars and Vectors

    5 questions

  • Linear Algebra and Geometry

    03:06

  • Linear Algebra and Geometry

    3 questions

  • Arrays in Python – A Convenient Way To Represent Matrices

    05:09

  • What is a Tensor?

    03:00

  • What is a Tensor?

    2 questions

  • Addition and Subtraction of Matrices

    03:36

  • Addition and Subtraction of Matrices

    3 questions

  • Errors when Adding Matrices

    02:01

  • Transpose of a Matrix

    05:13

  • Dot Product

    03:48

  • Dot Product of Matrices

    08:23

  • Why is Linear Algebra Useful?

    10:10

  • What to Expect from this Part?

    03:07

  • Introduction to Neural Networks

    04:09

  • Introduction to Neural Networks

    1 question

  • Training the Model

    02:54

  • Training the Model

    3 questions

  • Types of Machine Learning

    03:43

  • Types of Machine Learning

    4 questions

  • The Linear Model (Linear Algebraic Version)

    03:08

  • The Linear Model

    2 questions

  • The Linear Model with Multiple Inputs

    02:25

  • The Linear Model with Multiple Inputs

    2 questions

  • The Linear model with Multiple Inputs and Multiple Outputs

    04:25

  • The Linear model with Multiple Inputs and Multiple Outputs

    3 questions

  • Graphical Representation of Simple Neural Networks

    01:47

  • Graphical Representation of Simple Neural Networks

    1 question

  • What is the Objective Function?

    01:27

  • What is the Objective Function?

    2 questions

  • Common Objective Functions: L2-norm Loss

    02:04

  • Common Objective Functions: L2-norm Loss

    3 questions

  • Common Objective Functions: Cross-Entropy Loss

    03:55

  • Common Objective Functions: Cross-Entropy Loss

    4 questions

  • Optimization Algorithm: 1-Parameter Gradient Descent

    06:33

  • Optimization Algorithm: 1-Parameter Gradient Descent

    4 questions

  • Optimization Algorithm: n-Parameter Gradient Descent

    06:08

  • Optimization Algorithm: n-Parameter Gradient Descent

    3 questions

  • Basic NN Example (Part 1)

    03:06

  • Basic NN Example (Part 2)

    04:58

  • Basic NN Example (Part 3)

    03:25

  • Basic NN Example (Part 4)

    08:15

  • Basic NN Example Exercises

    00:51

  • How to Install TensorFlow 2.0

    05:02

  • TensorFlow Outline and Comparison with Other Libraries

    03:28

  • TensorFlow 1 vs TensorFlow 2

    02:32

  • A Note on TensorFlow 2 Syntax

    00:58

  • Types of File Formats Supporting TensorFlow

    02:34

  • Outlining the Model with TensorFlow 2

    05:48

  • Interpreting the Result and Extracting the Weights and Bias

    04:09

  • Customizing a TensorFlow 2 Model

    02:51

  • Basic NN with TensorFlow: Exercises

    00:47

  • What is a Layer?

    01:53

  • What is a Deep Net?

    02:18

  • Digging into a Deep Net

    04:58

  • Non-Linearities and their Purpose

    02:59

  • Activation Functions

    03:37

  • Activation Functions: Softmax Activation

    03:24

  • Backpropagation

    03:12

  • Backpropagation Picture

    03:02

  • Backpropagation – A Peek into the Mathematics of Optimization

    00:21

  • What is Overfitting?

    03:51

  • Underfitting and Overfitting for Classification

    01:52

  • What is Validation?

    03:22

  • Training, Validation, and Test Datasets

    02:30

  • N-Fold Cross Validation

    03:07

  • Early Stopping or When to Stop Training

    04:54

  • What is Initialization?

    02:32

  • Types of Simple Initializations

    02:47

  • State-of-the-Art Method – (Xavier) Glorot Initialization

    02:45

  • Stochastic Gradient Descent

    03:24

  • Problems with Gradient Descent

    02:02

  • Momentum

    02:30

  • Learning Rate Schedules, or How to Choose the Optimal Learning Rate

    04:25

  • Learning Rate Schedules Visualized

    01:32

  • Adaptive Learning Rate Schedules (AdaGrad and RMSprop )

    04:08

  • Adam (Adaptive Moment Estimation)

    02:39

  • Preprocessing Introduction

    02:51

  • Types of Basic Preprocessing

    01:17

  • Standardization

    04:31

  • Preprocessing Categorical Data

    02:15

  • Binary and One-Hot Encoding

    03:39

  • MNIST: The Dataset

    02:25

  • MNIST: How to Tackle the MNIST

    02:44

  • MNIST: Importing the Relevant Packages and Loading the Data

    02:11

  • MNIST: Preprocess the Data – Create a Validation Set and Scale It

    04:43

  • MNIST: Preprocess the Data – Scale the Test Data – Exercise

    00:03

  • MNIST: Preprocess the Data – Shuffle and Batch

    06:30

  • MNIST: Preprocess the Data – Shuffle and Batch – Exercise

    00:03

  • MNIST: Outline the Model

    04:54

  • MNIST: Select the Loss and the Optimizer

    02:05

  • MNIST: Learning

    05:38

  • MNIST – Exercises

    01:21

  • MNIST: Testing the Model

    03:56

  • Business Case: Exploring the Dataset and Identifying Predictors

    07:54

  • Business Case: Outlining the Solution

    01:31

  • Business Case: Balancing the Dataset

    03:39

  • Business Case: Preprocessing the Data

    11:32

  • Business Case: Preprocessing the Data – Exercise

    00:12

  • Business Case: Load the Preprocessed Data

    03:23

  • Business Case: Load the Preprocessed Data – Exercise

    00:03

  • Business Case: Learning and Interpreting the Result

    04:15

  • Business Case: Setting an Early Stopping Mechanism

    05:01

  • Setting an Early Stopping Mechanism – Exercise

    00:08

  • Business Case: Testing the Model

    01:23

  • Business Case: Final Exercise

    00:16

  • Summary on What You’ve Learned

    03:41

  • What’s Further out there in terms of Machine Learning

    01:47

  • DeepMind and Deep Learning

    00:21

  • An overview of CNNs

    04:55

  • An Overview of RNNs

    02:50

  • An Overview of non-NN Approaches

    03:52

  • READ ME!!!!

    00:21

  • How to Install TensorFlow 1

    02:20

  • A Note on Installing Packages in Anaconda

    01:14

  • TensorFlow Intro

    03:46

  • Actual Introduction to TensorFlow

    01:40

  • Types of File Formats, supporting Tensors

    02:38

  • Basic NN Example with TF: Inputs, Outputs, Targets, Weights, Biases

    06:05

  • Basic NN Example with TF: Loss Function and Gradient Descent

    03:41

  • Basic NN Example with TF: Model Output

    06:05

  • Basic NN Example with TF Exercises

    01:01

  • MNIST: What is the MNIST Dataset?

    02:26

  • MNIST: How to Tackle the MNIST

    02:48

  • MNIST: Relevant Packages

    01:34

  • MNIST: Model Outline

    06:51

  • MNIST: Loss and Optimization Algorithm

    02:39

  • Calculating the Accuracy of the Model

    04:18

  • MNIST: Batching and Early Stopping

    02:08

  • MNIST: Learning

    07:35

  • MNIST: Results and Testing

    06:11

  • MNIST: Exercises

    01:29

  • MNIST: Solutions

    01:31

  • Business Case: Getting Acquainted with the Dataset

    07:55

  • Business Case: Outlining the Solution

    01:57

  • The Importance of Working with a Balanced Dataset

    03:39

  • Business Case: Preprocessing

    11:35

  • Business Case: Preprocessing Exercise

    00:13

  • Creating a Data Provider

    06:37

  • Business Case: Model Outline

    05:34

  • Business Case: Optimization

    05:10

  • Business Case: Interpretation

    02:05

  • Business Case: Testing the Model

    02:04

  • Business Case: A Comment on the Homework

    03:51

  • Business Case: Final Exercise

    00:17

  • What are Data, Servers, Clients, Requests, and Responses

    04:43

  • What are Data, Servers, Clients, Requests, and Responses

    2 questions

  • What are Data Connectivity, APIs, and Endpoints?

    07:05

  • What are Data Connectivity, APIs, and Endpoints?

    2 questions

  • Taking a Closer Look at APIs

    08:05

  • Taking a Closer Look at APIs

    2 questions

  • Communication between Software Products through Text Files

    04:20

  • Communication between Software Products through Text Files

    1 question

  • Software Integration – Explained

    05:25

  • Software Integration – Explained

    2 questions

  • Game Plan for this Python, SQL, and Tableau Business Exercise

    04:08

  • The Business Task

    02:48

  • Introducing the Data Set

    03:18

  • Introducing the Data Set

    1 question

  • What to Expect from the Following Sections?

    01:28

  • Importing the Absenteeism Data in Python

    03:23

  • Checking the Content of the Data Set

    05:53

  • Introduction to Terms with Multiple Meanings

    03:27

  • What’s Regression Analysis – a Quick Refresher

    01:50

  • Using a Statistical Approach towards the Solution to the Exercise

    02:17

  • Dropping a Column from a DataFrame in Python

    06:27

  • EXERCISE – Dropping a Column from a DataFrame in Python

    00:26

  • SOLUTION – Dropping a Column from a DataFrame in Python

    00:01

  • Analyzing the Reasons for Absence

    05:04

  • Obtaining Dummies from a Single Feature

    08:37

  • EXERCISE – Obtaining Dummies from a Single Feature

    00:04

  • SOLUTION – Obtaining Dummies from a Single Feature

    00:00

  • Dropping a Dummy Variable from the Data Set

    01:32

  • More on Dummy Variables: A Statistical Perspective

    01:28

  • Classifying the Various Reasons for Absence

    08:35

  • Using .concat() in Python

    04:35

  • EXERCISE – Using .concat() in Python

    00:04

  • SOLUTION – Using .concat() in Python

    00:01

  • Reordering Columns in a Pandas DataFrame in Python

    01:43

  • EXERCISE – Reordering Columns in a Pandas DataFrame in Python

    00:06

  • SOLUTION – Reordering Columns in a Pandas DataFrame in Python

    00:12

  • Creating Checkpoints while Coding in Jupyter

    02:52

  • EXERCISE – Creating Checkpoints while Coding in Jupyter

    00:04

  • SOLUTION – Creating Checkpoints while Coding in Jupyter

    00:00

  • Analyzing the Dates from the Initial Data Set

    07:48

  • Extracting the Month Value from the “Date” Column

    07:00

  • Extracting the Day of the Week from the “Date” Column

    03:36

  • EXERCISE – Removing the “Date” Column

    00:37

  • Analyzing Several “Straightforward” Columns for this Exercise

    03:17

  • Working on “Education”, “Children”, and “Pets”

    04:38

  • Final Remarks of this Section

    01:59

  • A Note on Exporting Your Data as a *.csv File

    00:26

  • Exploring the Problem with a Machine Learning Mindset

    03:20

  • Creating the Targets for the Logistic Regression

    06:32

  • Selecting the Inputs for the Logistic Regression

    02:41

  • Standardizing the Data

    03:26

  • Splitting the Data for Training and Testing

    06:12

  • Fitting the Model and Assessing its Accuracy

    05:39

  • Creating a Summary Table with the Coefficients and Intercept

    05:16

  • Interpreting the Coefficients for Our Problem

    06:14

  • Standardizing only the Numerical Variables (Creating a Custom Scaler)

    04:12

  • Interpreting the Coefficients of the Logistic Regression

    05:10

  • Backward Elimination or How to Simplify Your Model

    04:02

  • Testing the Model We Created

    04:43

  • Saving the Model and Preparing it for Deployment

    04:06

  • ARTICLE – A Note on ‘pickling’

    01:15

  • EXERCISE – Saving the Model (and Scaler)

    00:13

  • Preparing the Deployment of the Model through a Module

    04:04

  • Are You Sure You’re All Set?

    00:14

  • Deploying the ‘absenteeism_module’ – Part I

    03:50

  • Deploying the ‘absenteeism_module’ – Part II

    06:23

  • Exporting the Obtained Data Set as a *.csv

    00:31

  • EXERCISE – Age vs Probability

    00:14

  • Analyzing Age vs Probability in Tableau

    08:49

  • EXERCISE – Reasons vs Probability

    00:15

  • Analyzing Reasons vs Probability in Tableau

    07:49

  • EXERCISE – Transportation Expense vs Probability

    00:22

  • Analyzing Transportation Expense vs Probability in Tableau

    06:00

  • Using the .format() Method

    09:02

  • Using .format() – Exercise #1

    1 question

  • Using .format() – Exercise #2

    1 question

  • Using .format() – Exercise #3

    1 question

  • Using .format() – Exercise #4

    1 question

  • Using .format() – Exercise #5

    1 question

  • Iterating Over Range Objects

    04:17

  • Introduction to Nested For Loops

    05:59

  • Triple Nested For Loops

    05:37

  • Triple Nested For Loops – Exercise #1

    1 question

  • Triple Nested For Loops – Exercise #2

    1 question

  • Triple Nested For Loops – Exercise #3

    1 question

  • Triple Nested For Loops – Exercise #4

    1 question

  • Triple Nested For Loops – Exercise #5

    1 question

  • Triple Nested For Loops – Exercise #6

    1 question

  • Triple Nested For Loops – Exercise #7

    1 question

  • List Comprehensions

    08:30

  • List Comprehensions – Exercise #1

    1 question

  • List Comprehensions – Exercise #2

    1 question

  • List Comprehensions – Exercise #3

    1 question

  • List Comprehensions – Exercise #4

    1 question

  • List Comprehensions – Exercise #5

    1 question

  • Anonymous (Lambda) Functions

    07:00

  • Anonymous Functions – Exercise #1

    1 question

  • Anonymous Functions – Exercise #2

    1 question

  • Anonymous Functions – Exercise #3

    1 question

  • Anonymous Functions – Exercise #4

    1 question

  • Introduction to pandas Series

    08:33

  • A Note on Completing the Upcoming Coding Exercises

    01:22

  • Introduction to pandas Series – Exercise #1

    1 question

  • Introduction to pandas Series – Exercise #2

    1 question

  • Introduction to pandas Series – Exercise #3

    1 question

  • Introduction to pandas Series – Exercise #4

    1 question

  • Introduction to pandas Series – Exercise #5

    1 question

  • Introduction to pandas Series – Exercise #6

    1 question

  • Introduction to pandas Series – Exercise #7

    1 question

  • Introduction to pandas Series – Exercise #8

    1 question

  • Introduction to pandas Series – Exercise #9

    1 question

  • Introduction to pandas Series – Exercise #10

    1 question

  • Working with Methods in Python – Part I

    04:49

  • Working with Methods in Python – Part II

    02:32

  • Working with Methods in Python – Exercise #1

    1 question

  • Working with Methods in Python – Exercise #2

    1 question

  • Parameters and Arguments in pandas

    04:09

  • Parameters and Arguments in pandas – Exercise #1

    1 question

  • Parameters and Arguments in pandas – Exercise #2

    1 question

  • Using .unique() and .nunique()

    03:49

  • Using .sort_values()

    03:58

  • Introduction to pandas DataFrames – Part I

    04:41

  • Introduction to pandas DataFrames – Exercise #1

    1 question

  • Introduction to pandas DataFrames – Exercise #2

    1 question

  • Introduction to pandas DataFrames – Part II

    05:05

  • Introduction to pandas DataFrames – Exercise #3

    1 question

  • Introduction to pandas DataFrames – Exercise #4

    1 question

  • Introduction to pandas DataFrames – Exercise #5

    1 question

  • pandas DataFrames – Common Attributes

    04:15

  • Data Selection in pandas DataFrames

    06:55

  • pandas DataFrames – Indexing with .iloc[]

    05:56

  • pandas DataFrames – Indexing with .loc[]

    03:51

  • An Introduction to Working with Files in Python

    03:46

  • File vs File Object, Reading vs Parsing Data

    02:52

  • Structured, Semi-Structured and Unstructured Data

    03:10

  • Text Files and Data Connectivity

    03:06

  • Importing Data in Python – Principles

    04:50

  • Plain Text Files, Flat Files and More

    04:33

  • Text Files of Fixed Width

    01:26

  • Common Naming Conventions

    03:49

  • Importing Text Files – open()

    09:00

  • Importing Text Files – with open()

    04:53

  • Importing *.csv Files – Part I

    05:35

  • Importing *.csv Files – Part II

    02:37

  • Importing *.csv Files – Part III

    05:57

  • Importing Data with index_col

    02:35

  • Importing Data with .loadtxt() and .genfromtxt()

    10:43

  • Importing Data – Partial Cleaning While Importing Data

    07:21

  • Importing Data with NumPy – Exercise

    00:10

  • Importing Data from *.json Files

    05:14

  • An Introduction to Working with Excel Files in Python

    03:40

  • Working with Excel (*.xlsx) Data

    01:55

  • Importing Data in Python – an Important Exercise

    05:44

  • Importing Data with the .squeeze() Method

    03:23

  • Importing Files in Jupyter

    03:10

  • Saving Your Data with pandas

    03:11

  • Saving Your Data with NumPy – Part I – *.npy

    05:23

  • Saving Your Data with NumPy – Part II – *.npz

    05:12

  • Saving Your Data with NumPy – Part III – *.csv

    03:58

  • Saving Data with Numpy – Exercise

    00:08

  • Working with Text Files in Python – Conclusion

    00:42

  • Bonus Lecture: Next Steps

    01:07

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