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The Data Science Course: Complete Data Science Bootcamp
دوره The Data Science Course: Complete Data Science Bootcamp. علم داده یکی از پردرآمدترین و پرمتقاضاترین شغلهای قرن حاضر است. این شغل به دلیل ماهیت دیجیتال، تحلیلی و مبتنی بر برنامهنویسی، از محبوبیت بالایی برخوردار است. با وجود تقاضای زیاد برای دانشمندان داده، یافتن افراد متخصص در این حوزه دشوار است. دلیل این امر کمبود برنامههای آموزشی جامع و ساختارمند در این زمینه است. دانشگاهها در ارائه برنامههای تخصصی علم داده ناتوان بودهاند و دورههای آنلاین موجود نیز غالباً بر روی یک موضوع خاص تمرکز میکنند و درک کلی از این حوزه را به دانشآموز ارائه نمیدهند. دوره جامع علم داده: بوت کمپ کامل علم داده، راهحلی جامع برای این مشکل ارائه میدهد. این دوره با در نظر گرفتن نیازهای بازار کار و با رویکردی ساختارمند، تمام مهارتهای لازم برای تبدیل شدن به یک دانشمند داده را به شما آموزش میدهد. مزایای این دوره: جامع و ساختارمند: این دوره تمام مباحث مورد نیاز برای تبدیل شدن به یک دانشمند داده را به طور جامع و ساختارمند به شما آموزش میدهد. مبتدیپسند: این دوره از پایه شروع میشود و برای افراد مبتدی نیز مناسب است. تمرکز بر روی کاربرد عملی: در این دوره، علاوه بر آموزش مبانی تئوری، بر روی کاربرد عملی مفاهیم در دنیای واقعی نیز تمرکز میشود. در این دوره چه چیزی را فرا میگیرید: آشنایی با حوزه علم داده و انواع تحلیلهای انجامشده: این بخش به شما دیدگاهی کلی از علم داده و کاربردهای آن در دنیای واقعی ارائه میدهد. ریاضیات و آمار: تسلط بر مبانی ریاضی و آمار، زیربنای علم داده است. در این دوره، مفاهیم کلیدی این دو علم را به طور عمیق فرا خواهید گرفت. برنامهنویسی پایتون: پایتون زبان محبوب علم داده است. در این دوره، نحوه برنامهنویسی در پایتون و استفاده از کتابخانههای قدرتمند آن مانند NumPy، Pandas، Matplotlib و Seaborn را برای تجزیه و تحلیل دادهها فرا خواهید گرفت. تجزیه و تحلیل آماری پیشرفته: این دوره مفاهیم آماری پیشرفته مانند رگرسیون خطی، رگرسیون لجستیک، تحلیل خوشهای و تحلیل عاملی را به شما آموزش میدهد. یادگیری ماشین: یادگیری ماشین یکی از مهمترین شاخههای علم داده است. در این دوره، با الگوریتمهای مختلف یادگیری ماشین مانند درختهای تصمیم، جنگلهای تصادفی و شبکههای عصبی مصنوعی آشنا خواهید شد و نحوه پیادهسازی آنها در پایتون را فرا خواهید گرفت. یادگیری عمیق: یادگیری عمیق زیرشاخهای از یادگیری ماشین است که با استفاده از شبکههای عصبی مصنوعی پیچیده، به حل مسائل پیچیده میپردازد. در این دوره، با مفاهیم کلیدی یادگیری عمیق و چارچوبهای محبوب آن مانند TensorFlow آشنا خواهید شد. این دوره برای چه کسانی است: افرادی که میخواهند دانشمند داده شوند: این دوره برای کسانی که به دنبال ورود به حوزه علم داده هستند و یا در حال حاضر در این حوزه فعالیت میکنند و میخواهند مهارتهای خود را ارتقا دهند، ایدهآل است. افرادی که به دنبال شغلی پردرآمد هستند: علم داده یکی از پردرآمدترین شغلهای حال حاضر است. اگر به دنبال شغلی با ثبات و آیندهدار هستید، این دوره میتواند به شما کمک کند تا به اهدافتان برسید. افراد مبتدی: این دوره از پایه شروع میشود و به تدریج سطح مهارت شما را افزایش میدهد. بنابراین، حتی اگر هیچگونه تجربه یا دانش قبلی در این زمینه ندارید، میتوانید با خیال راحت در این دوره شرکت کنید.
آموزش دهنده
شرکت
Udemy
مدت زمان
31 ساعت و 51 دقیقه
- Read me txt
- Part 1 Introduction
-
The Field of Data Science - The Various Data Science Disciplines
- Data Science and Business Buzzwords Why are there so Many
- What is the difference between Analysis and Analytics
- Business Analytics, Data Analytics, and Data Science An Introduction
- Continuing with BI, ML, and AI
- A Breakdown of our Data Science Infographic
- pdf DataScience-Diagram
- pdf DataScience-Diagram
- png DataScience
- png DataScience
- The Field of Data Science - Connecting the Data Science Disciplines
- The Field of Data Science - The Benefits of Each Discipline
-
The Field of Data Science - Popular Data Science Techniques
- Techniques for Working with Traditional Data
- Real Life Examples of Traditional Data
- Techniques for Working with Big Data
- Real Life Examples of Big Data
- Business Intelligence (BI) Techniques
- Real Life Examples of Business Intelligence (BI)
- Techniques for Working with Traditional Methods
- Real Life Examples of Traditional Methods
- Machine Learning (ML) Techniques
- Types of Machine Learning
- Real Life Examples of Machine Learning (ML)
- The Field of Data Science - Popular Data Science Tools
- The Field of Data Science - Careers in Data Science
- The Field of Data Science - Debunking Common Misconceptions
- Part 2 Probability
-
Probability - Combinatorics
- Fundamentals of Combinatorics
- Permutations and How to Use Them
- Simple Operations with Factorials
- Solving Variations with Repetition
- Solving Variations without Repetition
- Solving Combinations
- Symmetry of Combinations
- Solving Combinations with Separate Sample Spaces
- Combinatorics in Real-Life The Lottery
- A Recap of Combinatorics
- A Practical Example of Combinatorics
- pdf Course-Notes-Combinatorics
- pdf Combinations-With-Repetition
- pdf Symmetry-Explained
- pdf Additional-Exercises-Combinatorics-Solutions
- pdf Additional-Exercises-Combinatorics
-
Probability - Bayesian Inference
- Sets and Events
- Ways Sets Can Interact
- Intersection of Sets
- Union of Sets
- Mutually Exclusive Sets
- Dependence and Independence of Sets
- The Conditional Probability Formula
- The Law of Total Probability
- The Additive Rule
- The Multiplication Law
- Bayes' Law
- A Practical Example of Bayesian Inference
- pdf Course-Notes-Bayesian-Inference
- pdf Bayesian-Homework-Solutions
- pdf Bayesian-Homework
- pdf CDS-2017-2018-Hamilton
-
Probability - Distributions
- Fundamentals of Probability Distributions
- Types of Probability Distributions
- Characteristics of Discrete Distributions
- Discrete Distributions The Uniform Distribution
- Discrete Distributions The Bernoulli Distribution
- Discrete Distributions The Binomial Distribution
- Discrete Distributions The Poisson Distribution
- Characteristics of Continuous Distributions
- Continuous Distributions The Normal Distribution
- Continuous Distributions The Standard Normal Distribution
- Continuous Distributions The Students' T Distribution
- Continuous Distributions The Chi-Squared Distribution
- Continuous Distributions The Exponential Distribution
- Continuous Distributions The Logistic Distribution
- A Practical Example of Probability Distributions
- pdf Course-Notes-Probability-Distributions
- pdf Poisson-Expected-Value-and-Variance
- pdf Solving-Integrals
- pdf Normal-Distribution-Exp-and-Var
- xlsx Customers-Membership-post
- xlsx Customers-Membership
- xlsx Daily-Views-post
- xlsx Daily-Views
- csv FIFA19-post
- csv FIFA19
- Probability - Probability in Other Fields
- Part 3 Statistics
-
Statistics - Descriptive Statistics
- Types of Data
- Levels of Measurement
- Categorical Variables - Visualization Techniques
- Numerical Variables - Frequency Distribution Table
- The Histogram
- Cross Tables and Scatter Plots
- Mean, median and mode
- Skewness
- Variance
- Standard Deviation and Coefficient of Variation
- Covariance
- Correlation Coefficient
- pdf Course-notes-descriptive-statistics
- xlsx Glossary
- xlsx Categorical-variables.Visualization-techniques-lesson
- xlsx Categorical-variables.Visualization-techniques-exercise-solution
- xlsx Categorical-variables.Visualization-techniques-exercise
- html Categorical Variables Exercise
- pdf Statistics-PDF-with-Excel-Solutions-that-dont-visualize-properly
- xlsx Numerical-variables.Frequency-distribution-table-lesson
- xlsx Numerical-variables.Frequency-distribution-table-exercise-solution
- html Numerical Variables Exercise
- xlsx The-Histogram-lesson
- xlsx The-Histogram-exercise-solution
- xlsx The-Histogram-exercise
- html Histogram Exercise
- pdf Statistics-PDF-with-Excel-Solutions-that-dont-visualize-properly
- xlsx Cross-table-and-scatter-plot
- xlsx Cross-table-and-scatter-plot-exercise-solution
- xlsx Cross-table-and-scatter-plot-exercise
- html Cross Tables and Scatter Plots Exercise
- xlsx Mean-median-and-mode-lesson
- xlsx Mean-median-and-mode-exercise-solution
- xlsx Mean-median-and-mode-exercise
- html Mean, Median and Mode Exercise
- xlsx Skewness-lesson
- xlsx Skewness-exercise-solution
- xlsx Skewness-exercise
- html Skewness Exercise
- xlsx Variance-lesson
- xlsx Variance-exercise-solution
- xlsx Variance-exercise
- html Variance Exercise
- xlsx Standard-deviation-and-coefficient-of-variation-lesson
- xlsx Standard-deviation-and-coefficient-of-variation-exercise-solution
- xlsx Standard-deviation-and-coefficient-of-variation-exercise
- html Standard Deviation and Coefficient of Variation Exercise
- xlsx Covariance-lesson
- xlsx Covariance-exercise-solution
- xlsx Covariance-exercise
- html Covariance Exercise
- xlsx Correlation-exercise-solution
- xlsx Correlation-exercise
- html Correlation Coefficient Exercise
- Statistics - Practical Example Descriptive Statistics
-
Statistics - Inferential Statistics Fundamentals
- Introduction
- What is a Distribution
- The Normal Distribution
- The Standard Normal Distribution
- Central Limit Theorem
- Standard error
- Estimators and Estimates
- pdf Course-notes-inferential-statistics
- xlsx What-is-a-distribution-lesson
- pdf Course-notes-inferential-statistics
- xlsx Standard-normal-distribution-lesson
- xlsx Standard-normal-distribution-exercise-solution
- xlsx Standard-normal-distribution-exercise
- html The Standard Normal Distribution Exercise
-
Statistics - Inferential Statistics Confidence Intervals
- What are Confidence Intervals
- Confidence Intervals; Population Variance Known; Z-score
- Confidence Interval Clarifications
- Student's T Distribution
- Confidence Intervals; Population Variance Unknown; T-score
- Margin of Error
- Confidence intervals. Two means. Dependent samples
- Confidence intervals. Two means. Independent Samples (Part 1)
- Confidence intervals. Two means. Independent Samples (Part 2)
- Confidence intervals. Two means. Independent Samples (Part 3)
- xlsx Population-variance-known-z-score-lesson
- xlsx The-z-table
- xlsx Population-variance-known-z-score-exercise-solution
- xlsx Population-variance-known-z-score-exercise
- xlsx The-z-table
- html Confidence Intervals; Population Variance Known; Z-score; Exercise
- xlsx Population-variance-unknown-t-score-lesson
- xlsx The-t-table
- xlsx Population-variance-unknown-t-score-exercise-solution
- xlsx Population-variance-unknown-t-score-exercise
- xlsx The-t-table
- html Confidence Intervals; Population Variance Unknown; T-score; Exercise
- xlsx Confidence-intervals.Two-means.Dependent-samples-lesson
- xlsx Confidence-intervals.Two-means.Dependent-samples-exercise-solution
- xlsx Confidence-intervals.Two-means.Dependent-samples-exercise
- html Confidence intervals. Two means. Dependent samples Exercise
- xlsx Confidence-intervals.Two-means.Independent-samples-Part-1-lesson
- xlsx Confidence-intervals.Two-means.Independent-samples-Part-1-exercise-solution
- xlsx Confidence-intervals.Two-means.Independent-samples-Part-1-exercise
- html Confidence intervals. Two means. Independent Samples (Part 1). Exercise
- xlsx Confidence-intervals.Two-means.Independent-samples-Part-2-lesson
- xlsx Confidence-intervals.Two-means.Independent-samples-Part-2-exercise-solution
- xlsx Confidence-intervals.Two-means.Independent-samples-Part-2-exercise
- html Confidence intervals. Two means. Independent Samples (Part 2). Exercise
- Statistics - Practical Example Inferential Statistics
-
Statistics - Hypothesis Testing
- Null vs Alternative Hypothesis
- Rejection Region and Significance Level
- Type I Error and Type II Error
- Test for the Mean. Population Variance Known
- p-value
- Test for the Mean. Population Variance Unknown
- Test for the Mean. Dependent Samples
- Test for the mean. Independent Samples (Part 1)
- Test for the mean. Independent Samples (Part 2)
- pdf Course-notes-hypothesis-testing
- html Further Reading on Null and Alternative Hypothesis
- pdf Course-notes-hypothesis-testing
- xlsx Test-for-the-mean.Population-variance-known-lesson
- xlsx Test-for-the-mean.Population-variance-known-exercise-solution
- xlsx Test-for-the-mean.Population-variance-known-exercise
- html Test for the Mean. Population Variance Known Exercise
- pdf Online-p-value-calculator
- xlsx Test-for-the-mean.Population-variance-unknown-lesson
- xlsx Test-for-the-mean.Population-variance-unknown-exercise-solution
- xlsx Test-for-the-mean.Population-variance-unknown-exercise
- html Test for the Mean. Population Variance Unknown Exercise
- xlsx Test-for-the-mean.Dependent-samples-lesson
- xlsx Test-for-the-mean.Dependent-samples-exercise-solution
- xlsx Test-for-the-mean.Dependent-samples-exercise
- html Test for the Mean. Dependent Samples Exercise
- xlsx Test-for-the-mean.Independent-samples-Part-1-lesson
- xlsx Test-for-the-mean.Independent-samples-Part-1-exercise-solution
- xlsx Test-for-the-mean.Independent-samples-Part-1-exercise
- html Test for the mean. Independent Samples (Part 1). Exercise
- xlsx Test-for-the-mean.Independent-samples-Part-2-lesson
- xlsx Test-for-the-mean.Independent-samples-Part-2-exercise-2-solution
- xlsx Test-for-the-mean.Independent-samples-Part-2-exercise-2
- html Test for the mean. Independent Samples (Part 2). Exercise
- Statistics - Practical Example Hypothesis Testing
- Part 4 Introduction to Python
-
Python - Variables and Data Types
- Variables
- Numbers and Boolean Values in Python
- Python Strings
- pdf Introduction-to-Python-Course-Notes
- ipynb Variables-Exercise-Py3
- ipynb Variables-Lecture-Py3
- ipynb Variables-Solution-Py3
- ipynb Numbers-and-Boolean-Values-Exercise-Py3
- ipynb Numbers-and-Boolean-Values-Lecture-Py3
- ipynb Strings-Exercise-Py3
- ipynb Strings-Lecture-Py3
- ipynb Strings-Solution-Py3
-
Python - Basic Python Syntax
- Using Arithmetic Operators in Python
- The Double Equality Sign
- How to Reassign Values
- Add Comments
- Understanding Line Continuation
- Indexing Elements
- Structuring with Indentation
- ipynb Arithmetic-Operators-Exercise-Py3
- ipynb Arithmetic-Operators-Lecture-Py3
- ipynb Arithmetic-Operators-Solution-Py3
- ipynb The-Double-Equality-Sign-Exercise-Py3
- ipynb The-Double-Equality-Sign-Lecture-Py3
- ipynb The-Double-Equality-Sign-Solution-Py3
- ipynb Reassign-Values-Exercise-Py3
- ipynb Reassign-Values-Lecture-Py3
- ipynb Reassign-Values-Solution-Py3
- ipynb Add-Comments-Lecture-Py3
- ipynb Line-Continuation-Exercise-Py3
- ipynb Line-Continuation-Lecture-Py3
- ipynb Line-Continuation-Solution-Py3
- ipynb Indexing-Elements-Exercise-Py3
- ipynb Indexing-Elements-Lecture-Py3
- ipynb Indexing-Elements-Solution-Py3
- ipynb Structure-Your-Code-with-Indentation-Exercise-Py3
- ipynb Structure-Your-Code-with-Indentation-Lecture-Py3
- ipynb Structure-Your-Code-with-Indentation-Solution-Py3
- Python - Other Python Operators
-
Python - Conditional Statements
- The IF Statement
- The ELSE Statement
- The ELIF Statement
- A Note on Boolean Values
- ipynb Introduction-to-the-If-Statement-Exercise-Py3
- ipynb Introduction-to-the-If-Statement-Lecture-Py3
- ipynb Introduction-to-the-If-Statement-Solution-Py3
- ipynb Add-an-Else-Statement-Exercise-Py3
- ipynb Add-an-Else-Statement-Lecture-Py3
- ipynb Add-an-Else-Statement-Solution-Py3
- ipynb Else-If-for-Brief-Elif-Exercise-Py3
- ipynb Else-If-for-Brief-Elif-Lecture-Py3
- ipynb Else-If-for-Brief-Elif-Solution-Py3
- ipynb A-Note-on-Boolean-Values-Lecture-Py3
-
Python - Python Functions
- Defining a Function in Python
- How to Create a Function with a Parameter
- Defining a Function in Python - Part II
- How to Use a Function within a Function
- Conditional Statements and Functions
- Functions Containing a Few Arguments
- Built-in Functions in Python
- ipynb Defining-a-Function-in-Python-Lecture-Py3
- ipynb Creating-a-Function-with-a-Parameter-Exercise-Py3
- ipynb Creating-a-Function-with-a-Parameter-Lecture-Py3
- ipynb Creating-a-Function-with-a-Parameter-Solution-Py3
- ipynb Another-Way-to-Define-a-Function-Exercise-Py3
- ipynb Another-Way-to-Define-a-Function-Lecture-Py3
- ipynb Another-Way-to-Define-a-Function-Solution-Py3
- ipynb Using-a-Function-in-another-Function-Exercise-Py3
- ipynb Using-a-Function-in-another-Function-Lecture-Py3
- ipynb Using-a-Function-in-another-Function-Solution-Py3
- ipynb Combining-Conditional-Statements-and-Functions-Exercise-Py3
- ipynb Combining-Conditional-Statements-and-Functions-Lecture-Py3
- ipynb Combining-Conditional-Statements-and-Functions-Solution-Py3
- ipynb Creating-Functions-Containing-a-Few-Arguments-Lecture-Py3
- ipynb Notable-Built-In-Functions-in-Python-Exercise-Py3
- ipynb Notable-Built-In-Functions-in-Python-Lecture-Py3
- ipynb Notable-Built-In-Functions-in-Python-Solution-Py3
-
Python - Sequences
- Lists
- Using Methods
- List Slicing
- Tuples
- Dictionaries
- ipynb Lists-Exercise-Py3
- ipynb Lists-Lecture-Py3
- ipynb Lists-Solution-Py3
- ipynb Help-Yourself-with-Methods-Exercise-Py3
- ipynb Help-Yourself-with-Methods-Lecture-Py3
- ipynb Help-Yourself-with-Methods-Solution-Py3
- ipynb List-Slicing-Exercise-Py3
- ipynb List-Slicing-Lecture-Py3
- ipynb List-Slicing-Solution-Py3
- ipynb Tuples-Exercise-Py3
- ipynb Tuples-Lecture-Py3
- ipynb Tuples-Solution-Py3
- ipynb Dictionaries-Exercise-Py3
- ipynb Dictionaries-Lecture-Py3
- ipynb Dictionaries-Solution-Py3
-
Python - Iterations
- For Loops
- While Loops and Incrementing
- Lists with the range() Function
- Conditional Statements and Loops
- Conditional Statements, Functions, and Loops
- How to Iterate over Dictionaries
- ipynb For-Loops-Exercise-Py3
- ipynb For-Loops-Lecture-Py3
- ipynb For-Loops-Solution-Py3
- ipynb While-Loops-and-Incrementing-Exercise-Py3
- ipynb While-Loops-and-Incrementing-Lecture-Py3
- ipynb While-Loops-and-Incrementing-Solution-Py3
- ipynb Create-Lists-with-the-range-Function-Exercise-Py3
- ipynb Create-Lists-with-the-range-Function-Lecture-Py3
- ipynb Create-Lists-with-the-range-Function-Solution-Py3
- ipynb Use-Conditional-Statements-and-Loops-Together-Exercise-Py3
- ipynb Use-Conditional-Statements-and-Loops-Together-Lecture-Py3
- ipynb Use-Conditional-Statements-and-Loops-Together-Solution-Py3
- ipynb All-In-Exercise-Py3
- ipynb All-In-Lecture-Py3
- ipynb All-In-Solution-Py3
- ipynb Iterating-over-Dictionaries-Exercise-Py3
- ipynb Iterating-over-Dictionaries-Lecture-Py3
- ipynb Iterating-over-Dictionaries-Solution-Py3
- Python - Advanced Python Tools
- Part 5 Advanced Statistical Methods in Python
-
Advanced Statistical Methods - Linear Regression with StatsModels
- The Linear Regression Model
- Correlation vs Regression
- Geometrical Representation of the Linear Regression Model_en
- Python Packages Installation
- First Regression in Python
- Using Seaborn for Graphs
- How to Interpret the Regression Table
- Decomposition of Variability
- What is the OLS
- R-Squared
- pdf Course-notes-regression-analysis
- csv Simple-linear-regression
- ipynb Simple-linear-regression-with-comments
- ipynb Simple-linear-regression
- html First Regression in Python Exercise
- ipynb Simple-Linear-Regression-Exercise-Solution
- ipynb Simple-Linear-Regression-Exercise
- csv real-estate-price-size
-
Advanced Statistical Methods - Multiple Linear Regression with StatsModels
- Multiple Linear Regression
- Adjusted R-Squared
- Test for Significance of the Model (F-Test)
- OLS Assumptions
- A1 Linearity
- A2 No Endogeneity
- A3 Normality and Homoscedasticity
- A4 No Autocorrelation
- A5 No Multicollinearity
- Dealing with Categorical Data - Dummy Variables_en
- Making Predictions with the Linear Regression_en
- csv Multiple-linear-regression
- ipynb Multiple-linear-regression-and-Adjusted-R-squared-with-comments
- ipynb Multiple-linear-regression-and-Adjusted-R-squared
- html Multiple Linear Regression Exercise
- ipynb Multiple-Linear-Regression-Exercise-Solution
- ipynb Multiple-Linear-Regression-Exercise
- csv real-estate-price-size-year
- csv Dummies
- ipynb Dummy-Variables
- ipynb Dummy-variables-with-comments
- html Dealing with Categorical Data - Dummy Variables
- ipynb Multiple-Linear-Regression-with-Dummies-Exercise-Solution
- ipynb Multiple-Linear-Regression-with-Dummies-Exercise
- csv real-estate-price-size-year-view
- part Making Predictions with the Linear Regression.encrypted.m4a
- urls Making Predictions with the Linear Regression.encrypted.m4a.part.frag
- part Making Predictions with the Linear Regression.encrypted.mp4
- urls Making Predictions with the Linear Regression.encrypted.mp4.part.frag
- ipynb Making-predictions-with-comments
- ipynb Making-predictions
-
Advanced Statistical Methods - Linear Regression with sklearn
- What is sklearn and How is it Different from Other Packages_en
- How are we Going to Approach this Section
- Simple Linear Regression with sklearn
- Simple Linear Regression with sklearn - A StatsModels-like Summary Table
- Multiple Linear Regression with sklearn
- Calculating the Adjusted R-Squared in sklearn
- Feature Selection (F-regression)
- Creating a Summary Table with P-values
- Feature Scaling (Standardization)
- Feature Selection through Standardization of Weights
- Predicting with the Standardized Coefficients
- Underfitting and Overfitting
- Train - Test Split Explained
- part What is sklearn and How is it Different from Other Packages.encrypted.m4a
- urls What is sklearn and How is it Different from Other Packages.encrypted.m4a.part.frag
- part What is sklearn and How is it Different from Other Packages.encrypted.mp4
- urls What is sklearn and How is it Different from Other Packages.encrypted.mp4.part.frag
- csv Simple-linear-regression
- ipynb sklearn-Simple-Linear-Regression-with-comments
- ipynb sklearn-Simple-Linear-Regression
- csv Simple-linear-regression
- ipynb sklearn-Simple-Linear-Regression-with-comments
- ipynb sklearn-Simple-Linear-Regression
- html A Note on Normalization
- html Simple Linear Regression with sklearn - Exercise
- ipynb Simple-Linear-Regression-with-sklearn-Exercise-Solution
- ipynb Simple-Linear-Regression-with-sklearn-Exercise
- csv real-estate-price-size
- csv Multiple-linear-regression
- ipynb sklearn-Multiple-Linear-Regression-with-comments
- ipynb sklearn-Multiple-Linear-Regression
- csv Multiple-linear-regression
- ipynb sklearn-Multiple-Linear-Regression-and-Adjusted-R-squared-with-comments
- ipynb sklearn-Multiple-Linear-Regression-and-Adjusted-R-squared
- csv Multiple-linear-regression
- html Calculating the Adjusted R-Squared in sklearn - Exercise
- ipynb sklearn-Multiple-Linear-Regression-and-Adjusted-R-squared-Exercise-Solution
- ipynb sklearn-Multiple-Linear-Regression-and-Adjusted-R-squared-Exercise
- csv Multiple-linear-regression
- ipynb sklearn-Feature-Selection-with-F-regression-with-comments
- ipynb sklearn-Feature-Selection-with-F-regression
- csv Multiple-linear-regression
- html A Note on Calculation of P-values with sklearn
- ipynb sklearn-How-to-properly-include-p-values
- csv Multiple-linear-regression
- ipynb sklearn-Multiple-Linear-Regression-Summary-Table-with-comments
- ipynb sklearn-Multiple-Linear-Regression-Summary-Table
- html Multiple Linear Regression - Exercise
- csv real-estate-price-size-year
- ipynb sklearn-Multiple-Linear-Regression-Exercise-Solution
- ipynb sklearn-Multiple-Linear-Regression-Exercise
- csv Multiple-linear-regression
- IPY SKLEAR-1
- ipynb sklearn-Feature-Selection-through-Feature-Scaling-Standardization-Part-1
- csv Multiple-linear-regression
- IPY SKLEAR-1
- ipynb sklearn-Feature-Selection-through-Feature-Scaling-Standardization-Part-2
- csv Multiple-linear-regression
- ipynb sklearn-Making-Predictions-with-the-Standardized-Coefficients-with-comments
- ipynb sklearn-Making-Predictions-with-the-Standardized-Coefficients
- html Feature Scaling (Standardization) - Exercise
- csv real-estate-price-size-year
- ipynb sklearn-Feature-Scaling-Exercise-Solution
- ipynb sklearn-Feature-Scaling-Exercise
- ipynb sklearn-Train-Test-Split-with-comments
- ipynb sklearn-Train-Test-Split
-
Advanced Statistical Methods - Practical Example Linear Regression
- Practical Example Linear Regression (Part 1)
- Practical Example Linear Regression (Part 2)
- Practical Example Linear Regression (Part 3)
- Practical Example Linear Regression (Part 4)
- Practical Example Linear Regression (Part 5)
- csv Real-life-example
- ipynb sklearn-Linear-Regression-Practical-Example-Part-1-with-comments
- ipynb sklearn-Linear-Regression-Practical-Example-Part-1
- csv Real-life-example
- ipynb sklearn-Linear-Regression-Practical-Example-Part-2-with-comments
- ipynb sklearn-Linear-Regression-Practical-Example-Part-2
- html A Note on Multicollinearity
- ipynb sklearn-Linear-Regression-Practical-Example-Part-3-with-comments
- ipynb sklearn-Linear-Regression-Practical-Example-Part-3
- csv Real-life-example
- html Dummies and Variance Inflation Factor - Exercise
- ipynb sklearn-Dummies-and-VIF-Exercise-Solution
- ipynb sklearn-Dummies-and-VIF-Exercise
- csv Real-life-example
- ipynb sklearn-Linear-Regression-Practical-Example-Part-4-with-comments
- ipynb sklearn-Linear-Regression-Practical-Example-Part-4
- html Dummy Variables - Exercise
- csv Real-life-example
- ipynb sklearn-Linear-Regression-Practical-Example-Part-5-with-comments
- ipynb sklearn-Linear-Regression-Practical-Example-Part-5
- html Linear Regression - Exercise
- txt external-links
-
Advanced Statistical Methods - Logistic Regression
- Introduction to Logistic Regression
- A Simple Example in Python
- Logistic vs Logit Function
- Building a Logistic Regression
- An Invaluable Coding Tip
- Understanding Logistic Regression Tables
- What do the Odds Actually Mean
- Binary Predictors in a Logistic Regression
- Calculating the Accuracy of the Model
- Underfitting and Overfitting
- Testing the Model
- pdf Course-Notes-Logistic-Regression
- csv Admittance
- ipynb Admittance-with-comments
- ipynb Admittance
- pdf Course-Notes-Logistic-Regression
- ipynb Admittance-regression-summary-error
- ipynb Admittance-regression-tables-fixed-error
- ipynb Admittance-regression
- html Building a Logistic Regression - Exercise
- ipynb Building-a-Logistic-Regression-Exercise
- ipynb Building-a-Logistic-Regression-Solution
- csv Example-bank-data
- csv Bank-data
- html Understanding Logistic Regression Tables - Exercise
- ipynb Understanding-Logistic-Regression-Tables-Exercise
- ipynb Understanding-Logistic-Regression-Tables-Solution
- csv Binary-predictors
- ipynb Binary-predictors
- csv Bank-data
- html Binary Predictors in a Logistic Regression - Exercise
- ipynb Binary-Predictors-in-a-Logistic-Regression-Exercise
- ipynb Binary-Predictors-in-a-Logistic-Regression-Solution
- ipynb Accuracy-with-comments
- ipynb Accuracy
- csv Bank-data
- html Calculating the Accuracy of the Model
- ipynb Calculating-the-Accuracy-of-the-Model-Exercise
- ipynb Calculating-the-Accuracy-of-the-Model-Solution
- csv Test-dataset
- ipynb Testing-the-model-with-comments
- ipynb Testing-the-model
- csv Bank-data-testing
- csv Bank-data
- html Testing the Model - Exercise
- ipynb Testing-the-Model-Exercise
- ipynb Testing-the-Model-Solution
- Advanced Statistical Methods - Cluster Analysis
-
Advanced Statistical Methods - K-Means Clustering
- K-Means Clustering
- A Simple Example of Clustering
- Clustering Categorical Data
- How to Choose the Number of Clusters
- Pros and Cons of K-Means Clustering
- To Standardize or not to Standardize
- Relationship between Clustering and Regression
- Market Segmentation with Cluster Analysis (Part 1)
- Market Segmentation with Cluster Analysis (Part 2)
- How is Clustering Useful
- csv Country-clusters
- ipynb Country-clusters-with-comments
- ipynb Country-clusters
- html A Simple Example of Clustering - Exercise
- ipynb A-Simple-Example-of-Clustering-Exercise
- ipynb A-Simple-Example-of-Clustering-Solution
- csv Countries-exercise
- ipynb Categorical-data-with-comments
- ipynb Categorical-data
- csv Categorical
- html Clustering Categorical Data - Exercise
- ipynb Clustering-Categorical-Data-Exercise
- ipynb Clustering-Categorical-Data-Solution
- ipynb Selecting-the-number-of-clusters-with-comments
- ipynb Selecting-the-number-of-clusters
- csv Countries-exercise
- html How to Choose the Number of Clusters - Exercise
- ipynb How-to-Choose-the-Number-of-Clusters-Exercise
- ipynb How-to-Choose-the-Number-of-Clusters-Solution
- csv Example
- ipynb Market-segmentation-example-with-comments
- ipynb Market-segmentation-example
- ipynb Market-segmentation-example-Part2-with-comments
- ipynb Market-segmentation-example-Part2
- html EXERCISE Species Segmentation with Cluster Analysis (Part 1)
- ipynb Species-Segmentation-with-Cluster-Analysis-Part-1-Exercise
- ipynb Species-Segmentation-with-Cluster-Analysis-Part-1-Solution
- csv iris-dataset
- html EXERCISE Species Segmentation with Cluster Analysis (Part 2)
- ipynb Species-Segmentation-with-Cluster-Analysis-Part-2-Exercise
- ipynb Species-Segmentation-with-Cluster-Analysis-Part-2-Solution
- csv iris-dataset
- csv iris-with-answers
- Advanced Statistical Methods - Other Types of Clustering
-
Part 6 Mathematics
- What is a Matrix
- Scalars and Vectors
- Linear Algebra and Geometry
- Arrays in Python - A Convenient Way To Represent Matrices
- What is a Tensor
- Addition and Subtraction of Matrices
- Errors when Adding Matrices
- Transpose of a Matrix
- Dot Product
- Dot Product of Matrices
- Why is Linear Algebra Useful
- ipynb Scalars-Vectors-and-Matrices
- ipynb Tensors
- ipynb Adding-and-subtracting-matrices
- ipynb Errors-when-adding-scalars-vectors-and-matrices-in-Python
- ipynb Tranpose-of-a-matrix
- ipynb Dot-product
- ipynb Dot-product-Part-2
- Part 7 Deep Learning
-
Deep Learning - Introduction to Neural Networks
- Introduction to Neural Networks
- Training the Model
- Types of Machine Learning
- The Linear Model (Linear Algebraic Version)
- The Linear Model with Multiple Inputs
- The Linear model with Multiple Inputs and Multiple Outputs
- Graphical Representation of Simple Neural Networks
- What is the Objective Function
- Common Objective Functions L2-norm Loss
- Common Objective Functions Cross-Entropy Loss
- Optimization Algorithm 1-Parameter Gradient Descent
- Optimization Algorithm n-Parameter Gradient Descent
- pdf Course-Notes-Section-2
- pdf Course-Notes-Section-2
- xlsx GD-function-example
-
Deep Learning - How to Build a Neural Network from Scratch with NumPy
- Basic NN Example (Part 1)
- Basic NN Example (Part 2)
- Basic NN Example (Part 3)
- Basic NN Example (Part 4)
- ipynb Minimal-example-Part-1
- pdf Shortcuts-for-Jupyter
- ipynb Minimal-example-Part-2
- ipynb Minimal-example-Part-3
- ipynb Minimal-example-Part-4-Complete
- html Basic NN Example Exercises
- ipynb Minimal-example-All-Exercises
- ipynb Minimal-example-Exercise-1-Solution
- ipynb Minimal-example-Exercise-2-Solution
- ipynb Minimal-example-Exercise-3.a.Solution
- ipynb Minimal-example-Exercise-3.b.Solution
- ipynb Minimal-example-Exercise-3.c.Solution
- ipynb Minimal-example-Exercise-3.d.Solution
- ipynb Minimal-example-Exercise-4-Solution
- ipynb Minimal-example-Exercise-5-Solution
- ipynb Minimal-example-Exercise-6-Solution
- ipynb Minimal-example-Exercise-6
-
Deep Learning - TensorFlow 2.0 Introduction
- How to Install TensorFlow 2.0
- TensorFlow Outline and Comparison with Other Libraries
- TensorFlow 1 vs TensorFlow 2
- A Note on TensorFlow 2 Syntax
- Types of File Formats Supporting TensorFlow
- Outlining the Model with TensorFlow 2
- Interpreting the Result and Extracting the Weights and Bias
- Customizing a TensorFlow 2 Model
- pdf Shortcuts-for-Jupyter
- ipynb TensorFlow-Minimal-example-Part1
- ipynb TensorFlow-Minimal-example-Part2
- ipynb TensorFlow-Minimal-example-Part3
- ipynb TensorFlow-Minimal-example-complete-with-comments
- ipynb TensorFlow-Minimal-example-complete
- html Basic NN with TensorFlow Exercises
- ipynb TensorFlow-Minimal-Example-Exercise-2-1-Solution
- ipynb TensorFlow-Minimal-Example-Exercise-2-2-Solution
- ipynb TensorFlow-Minimal-Example-Exercise-3-Solution
- ipynb TensorFlow-Minimal-example-All-exercises
- ipynb TensorFlow-Minimal-example-Exercise-1-Solution
-
Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks
- What is a Layer
- What is a Deep Net
- Digging into a Deep Net
- Non-Linearities and their Purpose
- Activation Functions
- Activation Functions Softmax Activation
- Backpropagation
- Backpropagation Picture
- pdf Course-Notes-Section-6
- pdf Course-Notes-Section-6
- html Backpropagation - A Peek into the Mathematics of Optimization
- pdf Backpropagation-a-peek-into-the-Mathematics-of-Optimization
- Deep Learning - Overfitting
- Deep Learning - Initialization
- Deep Learning - Digging into Gradient Descent and Learning Rate Schedules
- Deep Learning - Preprocessing
-
Deep Learning - Classifying on the MNIST Dataset
- MNIST The Dataset
- MNIST How to Tackle the MNIST
- MNIST Importing the Relevant Packages and Loading the Data
- MNIST Preprocess the Data - Create a Validation Set and Scale It
- MNIST Preprocess the Data - Shuffle and Batch
- MNIST Outline the Model
- MNIST Select the Loss and the Optimizer
- MNIST Learning
- MNIST Testing the Model
- ipynb TensorFlow-MNIST-Part1-with-comments
- html MNIST Preprocess the Data - Scale the Test Data - Exercise
- ipynb TensorFlow-MNIST-Part2-with-comments
- html MNIST Preprocess the Data - Shuffle and Batch - Exercise
- ipynb TensorFlow-MNIST-Part3-with-comments
- ipynb TensorFlow-MNIST-Part4-with-comments
- ipynb TensorFlow-MNIST-Part5-with-comments
- ipynb TensorFlow-MNIST-Part6-with-comments
- ipynb TensorFlow-MNIST-Width-Solution
- ipynb TensorFlow-MNIST-Depth-Solution
- ipynb TensorFlow-MNIST-Width-and-Depth-Solution
- ipynb TensorFlow-MNIST-Activation-functions-Part-1-Solution
- ipynb TensorFlow-MNIST-Activation-functions-Part-2-Solution
- ipynb TensorFlow-MNIST-Batch-size-Part-1-Solution
- ipynb TensorFlow-MNIST-Batch-size-Part-2-Solution
- ipynb TensorFlow-MNIST-Learning-rate-Part-1-Solution
- ipynb TensorFlow-MNIST-Learning-rate-Part-2-Solution
- html MNIST - Exercises
- ipynb TensorFlow-MNIST-All-Exercises
- ipynb TensorFlow-MNIST-around-98-percent-accuracy
- ipynb TensorFlow-MNIST-complete-with-comments
- ipynb TensorFlow-MNIST-complete
-
Deep Learning - Business Case Example
- Business Case Exploring the Dataset and Identifying Predictors
- Business Case Outlining the Solution
- Business Case Balancing the Dataset
- Business Case Preprocessing the Data
- Business Case Load the Preprocessed Data
- Business Case Learning and Interpreting the Result
- Business Case Setting an Early Stopping Mechanism
- Business Case Testing the Model
- csv Audiobooks-data
- ipynb TensorFlow-Audiobooks-Preprocessing-with-comments
- ipynb TensorFlow-Audiobooks-Preprocessing
- html Business Case Preprocessing the Data - Exercise
- ipynb TensorFlow-Audiobooks-Preprocessing-Exercise-Solution
- ipynb TensorFlow-Audiobooks-Preprocessing-Exercise
- html Business Case Load the Preprocessed Data - Exercise
- ipynb TensorFlow-Audiobooks-Machine-Learning-Part1-with-comments
- ipynb TensorFlow-Audiobooks-Machine-Learning-Part2-with-comments
- ipynb TensorFlow-Audiobooks-Machine-Learning-Part3-with-comments
- html Setting an Early Stopping Mechanism - Exercise
- ipynb TensorFlow-Audiobooks-Machine-Learning-with-comments
- html Business Case Final Exercise
- ipynb TensorFlow-Audiobooks-Machine-Learning-with-comments
- Deep Learning - Conclusion
-
Appendix Deep Learning - TensorFlow 1 Introduction
- How to Install TensorFlow 1
- TensorFlow Intro
- Actual Introduction to TensorFlow
- Types of File Formats, supporting Tensors
- Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases
- Basic NN Example with TF Loss Function and Gradient Descent
- Basic NN Example with TF Model Output
- html READ ME!!!!
- html A Note on Installing Packages in Anaconda
- pdf Shortcuts-for-Jupyter
- ipynb TensorFlow-Minimal-example-Part-1
- ipynb TensorFlow-Minimal-example-Part-2
- ipynb TensorFlow-Minimal-example-Part-3
- ipynb TensorFlow-Minimal-example-complete
- html Basic NN Example with TF Exercises
- ipynb TensorFlow-Minimal-Example-All-Exercises
- ipynb TensorFlow-Minimal-Example-Exercise-1-Solution
- ipynb TensorFlow-Minimal-Example-Exercise-2-1-Solution
- ipynb TensorFlow-Minimal-Example-Exercise-2-2-Solution
- ipynb TensorFlow-Minimal-Example-Exercise-2-3-Solution
- ipynb TensorFlow-Minimal-Example-Exercise-2-4-Solution
- ipynb TensorFlow-Minimal-Example-Exercise-3-Solution
- ipynb TensorFlow-Minimal-Example-Exercise-4-Solution
-
Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset
- MNIST What is the MNIST Dataset
- MNIST How to Tackle the MNIST
- MNIST Relevant Packages
- MNIST Model Outline
- MNIST Loss and Optimization Algorithm
- Calculating the Accuracy of the Model
- MNIST Batching and Early Stopping
- MNIST Learning
- MNIST Results and Testing
- ipynb TensorFlow-MNIST-with-comments-Part-1
- ipynb TensorFlow-MNIST-with-comments-Part-2
- ipynb TensorFlow-MNIST-with-comments-Part-3
- ipynb TensorFlow-MNIST-with-comments-Part-4
- ipynb TensorFlow-MNIST-with-comments-Part-5
- ipynb TensorFlow-MNIST-with-comments-Part-6
- ipynb TensorFlow-MNIST-with-comments
- html MNIST Exercises
- ipynb TensorFlow-MNIST-Exercises-All
- ipynb TensorFlow-MNIST-take-note-of-time-Solution
- ipynb TensorFlow-MNIST-Width-Solution
- ipynb TensorFlow-MNIST-Depth-Solution
- ipynb TensorFlow-MNIST-Width-and-Depth-Solution
- ipynb TensorFlow-MNIST-Activation-functions-Part-1-Solution
- ipynb TensorFlow-MNIST-Activation-functions-Part-2-Solution
- ipynb TensorFlow-MNIST-Batch-size-Part-1-Solution
- ipynb TensorFlow-MNIST-Batch-size-Part-2-Solution
- ipynb TensorFlow-MNIST-Learning-rate-Part-1-Solution
- ipynb TensorFlow-MNIST-Learning-rate-Part-2-Solution
- html MNIST Solutions
- ipynb TensorFlow-MNIST-around-98-percent-accuracy
-
Appendix Deep Learning - TensorFlow 1 Business Case
- Business Case Getting Acquainted with the Dataset
- Business Case Outlining the Solution
- The Importance of Working with a Balanced Dataset
- Business Case Preprocessing
- Creating a Data Provider
- Business Case Model Outline
- Business Case Optimization
- Business Case Interpretation
- Business Case Testing the Model
- Business Case A Comment on the Homework
- csv Audiobooks-data
- csv Audiobooks-data
- csv Audiobooks-data
- ipynb TensorFlow-Audiobooks-Preprocessing-with-comments
- ipynb TensorFlow-Audiobooks-Preprocessing
- csv Audiobooks-data
- html Business Case Preprocessing Exercise
- ipynb TensorFlow-Audiobooks-Preprocessing-Exercise-Solution
- ipynb TensorFlow-Audiobooks-Preprocessing-Exercise
- ipynb TensorFlow-Audiobooks-Outlining-the-model-with-comments
- ipynb TensorFlow-Audiobooks-Outlining-the-model
- ipynb TensorFlow-Audiobooks-optimizing-the-algorithm-with-comments
- ipynb TensorFlow-Audiobooks-optimizing-the-algorithm
- ipynb TensorFlow-Audiobooks-optimizing-the-algorithm-with-comments
- ipynb TensorFlow-Audiobooks-optimizing-the-algorithm
- csv Audiobooks-data
- ipynb TensorFlow-Audiobooks-Machine-learning-Homework
- ipynb TensorFlow-Audiobooks-Preprocessing-with-comments
- csv Audiobooks-data
- html Business Case Final Exercise
- ipynb TensorFlow-Audiobooks-Machine-learning-Homework
- ipynb TensorFlow-Audiobooks-Preprocessing-with-comments
- Software Integration
- Case Study - What's Next in the Course
-
Case Study - Preprocessing the 'Absenteeism_data'
- Importing the Absenteeism Data in Python
- Checking the Content of the Data Set
- Introduction to Terms with Multiple Meanings
- Using a Statistical Approach towards the Solution to the Exercise
- Dropping a Column from a DataFrame in Python
- Analyzing the Reasons for Absence
- Obtaining Dummies from a Single Feature
- More on Dummy Variables A Statistical Perspective
- Classifying the Various Reasons for Absence
- Using .concat() in Python
- Reordering Columns in a Pandas DataFrame in Python
- Creating Checkpoints while Coding in Jupyter
- Analyzing the Dates from the Initial Data Set
- Extracting the Month Value from the Date Column
- Extracting the Day of the Week from the Date Column
- Analyzing Several Straightforward Columns for this Exercise
- Working on Education, Children, and Pets
- Final Remarks of this Section
- csv Absenteeism-data
- html What to Expect from the Following Sections
- pdf data-preprocessing-homework
- csv df-preprocessed
- html What's Regression Analysis - a Quick Refresher
- html EXERCISE - Dropping a Column from a DataFrame in Python
- html SOLUTION - Dropping a Column from a DataFrame in Python
- html EXERCISE - Obtaining Dummies from a Single Feature
- html SOLUTION - Obtaining Dummies from a Single Feature
- html Dropping a Dummy Variable from the Data Set
- html EXERCISE - Using .concat() in Python
- html SOLUTION - Using .concat() in Python
- html EXERCISE - Reordering Columns in a Pandas DataFrame in Python
- html SOLUTION - Reordering Columns in a Pandas DataFrame in Python
- ipynb Absenteeism-Exercise-Preprocessing-df-reason-mod
- html EXERCISE - Creating Checkpoints while Coding in Jupyter
- html SOLUTION - Creating Checkpoints while Coding in Jupyter
- ipynb Absenteeism-Exercise-Preprocessing-ChP-df-date-reason-mod
- ipynb Absenteeism-Exercise-Preprocessing-LECTURES
- ipynb Absenteeism-Exercise-Removing-the-Date-Column-SOLUTION
- html EXERCISE - Removing the Date Column
- ipynb Absenteeism-Exercise-EXERCISES-and-SOLUTIONS
- ipynb Absenteeism-Exercise-Preprocessing-df-preprocessed
- html A Note on Exporting Your Data as a .csv File
-
Case Study - Applying Machine Learning to Create the 'absenteeism_module'
- Exploring the Problem with a Machine Learning Mindset_en
- Creating the Targets for the Logistic Regression_en
- Selecting the Inputs for the Logistic Regression_en
- Standardizing the Data
- Splitting the Data for Training and Testing
- Fitting the Model and Assessing its Accuracy
- Creating a Summary Table with the Coefficients and Intercept_en
- Interpreting the Coefficients for Our Problem
- Standardizing only the Numerical Variables (Creating a Custom Scaler)_en
- Interpreting the Coefficients of the Logistic Regression_en
- Backward Elimination or How to Simplify Your Model_en
- Testing the Model We Created_en
- Saving the Model and Preparing it for Deployment_en
- Preparing the Deployment of the Model through a Module_en
- csv Absenteeism-preprocessed
- html ARTICLE - A Note on 'pickling'
- html EXERCISE - Saving the Model (and Scaler)
- txt external-links
-
Case Study - Loading the 'absenteeism_module'
- Deploying the 'absenteeism_module' - Part I
- Deploying the 'absenteeism_module' - Part II
- ipynb Absenteeism-Exercise-Integration
- csv Absenteeism-new-data
- html Are You Sure You're All Set
- py absenteeism-module
- 001 model model
- 001 scaler scaler
- ipynb Absenteeism-Exercise-Deploying-the-absenteeism-module
- html Exporting the Obtained Data Set as a .csv
-
Case Study - Analyzing the Predicted Outputs in Tableau
- Analyzing Age vs Probability in Tableau
- Analyzing Reasons vs Probability in Tableau
- Analyzing Transportation Expense vs Probability in Tableau
- csv Absenteeism-predictions
- html EXERCISE - Age vs Probability
- csv Absenteeism-predictions
- html EXERCISE - Reasons vs Probability
- html EXERCISE - Transportation Expense vs Probability
-
Appendix - Additional Python Tools
- Using the .format() Method
- Iterating Over Range Objects
- Introduction to Nested For Loops
- Triple Nested For Loops
- List Comprehensions
- Anonymous (Lambda) Functions
- ipynb Additional-Python-Tools-Exercises
- ipynb Additional-Python-Tools-Lectures
- ipynb Additional-Python-Tools-Solutions
- ipynb Additional-Python-Tools-Exercises
- ipynb Additional-Python-Tools-Lectures
- ipynb Additional-Python-Tools-Solutions
-
Appendix - pandas Fundamentals
- Introduction to pandas Series
- Working with Methods in Python - Part I
- Working with Methods in Python - Part II
- Parameters and Arguments in pandas
- Using .unique() and .nunique()
- Using .sort_values()
- Introduction to pandas DataFrames - Part I
- Introduction to pandas DataFrames - Part II
- pandas DataFrames - Common Attributes
- Data Selection in pandas DataFrames
- pandas DataFrames - Indexing with .iloc[]
- pandas DataFrames - Indexing with .loc[]
- csv Lending-company
- csv Location
- csv Region
- csv Sales-products
- ipynb pandas-Fundamentals-Exercises
- ipynb pandas-Fundamentals-Lectures
- ipynb pandas-Fundamentals-Solutions
- csv Lending-company
- csv Location
- csv Region
- csv Sales-products
- ipynb pandas-Fundamentals-Exercises
- ipynb pandas-Fundamentals-Lectures
- ipynb pandas-Fundamentals-Solutions
-
Appendix - Working with Text Files in Python
- An Introduction to Working with Files in Python
- File vs File Object, Reading vs Parsing Data
- Structured, Semi-Structured and Unstructured Data
- Text Files and Data Connectivity
- Importing Data in Python - Principles
- Plain Text Files, Flat Files and More
- Text Files of Fixed Width
- Common Naming Conventions
- Importing Text Files - open()
- Importing Text Files - with open()
- Importing .csv Files - Part I
- Importing .csv Files - Part II
- Importing .csv Files - Part III
- Importing Data with index_col
- Importing Data with .loadtxt() and .genfromtxt()
- Importing Data - Partial Cleaning While Importing Data
- Importing Data from .json Files
- An Introduction to Working with Excel Files in Python
- Working with Excel (.xlsx) Data
- Importing Data in Python - an Important Exercise
- Importing Data with the .squeeze() Method
- Importing Files in Jupyter
- Saving Your Data with pandas
- Saving Your Data with NumPy - Part I - .npy
- Saving Your Data with NumPy - Part II - .npz
- Saving Your Data with NumPy - Part III - .csv
- Working with Text Files in Python - Conclusion
- pdf Common-Naming-Conventions
- ipynb Working-with-Text-Files-Lectures
- pdf Common-Naming-Conventions
- ipynb Importing-Text-Files-in-Python-open
- txt source
- ipynb Importing-Text-Files-in-Python-with-open
- txt source
- ipynb Importing.csv-Files-with-pandas-Part-I
- csv Lending-company-single-column-data
- csv Lending-company
- ipynb Importing-Text-Data-with-NumPy-Complete
- ipynb Importing-Text-Data-with-NumPy-Template
- csv Lending-Company-Numeric-Data-NAN
- csv Lending-Company-Numeric-Data
- html Importing Data with NumPy - Exercise
- ipynb Importing-Text-Data-DSc-Exercise
- ipynb Importing-Text-Data-DSc-Solution
- json Lending-company
- xlsx Lending-company
- csv Customer-Gender
- ipynb Importing-Data-with-the-pandas-Squeeze-Method
- csv Lending-Company-Saving
- ipynb Saving-Data-NP-Complete
- ipynb Saving-Data-NP-Template
- html Saving Data with Numpy - Exercise
- ipynb Saving-Data-NP-Exercise
- ipynb Saving-Data-NP-Solution
- ipynb Working-with-Text-Files-Lectures
- txt external-links
- Bonus Lecture