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DATA SCIENCE ULTIMA
Statistics. Basic concepts, descriptive statistics, inferential statistics such as hypothesis testing. This is the academic side of data science, essentially. BASIC DEEP LEARNING CONCEPTS Skip to content. Data Handling. Importing Data. From a CSV File; From the Web; From a Folder; From a PDFCOMPUTER VISION
Skip to content. Data Handling. Importing Data. From a CSV File; From the Web; From a Folder; From a PDFGREEDY ALGORITHMS
Greedy Algorithms. A greedy algorithm makes the optimal choice from those choices available at the time of decision. An example might be a driver taking the streets with the highest speed limit, with no regard for whether one of the streets with a lower speed limit leads to MULTIVARIABLE CALCULUS Multivariable Calculus. Partial Differentiation Explicit Differentiation Basic Example. The rule of thumb is to treat whatever variable we are not taking the derivative of as a constant: AUDIO SIGNAL PROCESSING Audio Signal Processing. There are two general approaches to audio processing with deep learning: Turn the audio file into an image, typically a log-scaled mel spectrogram wavelength. SQL SELECT STATEMENT SQL Select Statement. The select statement is used to grab data from a table. Select all values from a table. SELECT * FROM tbl; — Select all columns and rows in the tbl table. BIG O (TIME COMPLEXITY) Big O Notation: Name: Example: O(1) Constant: Choosing one item by index from an array, no matter how long the array it will be the same. O(log n) Logarithmic ONE-WAY ANALYSIS OF VARIANCE (ANOVA) Skip to content. Data Handling. Importing Data. From a CSV File; From the Web; From a Folder; From a PDF MULTI-LAYER PERCEPTRON (MLP) The above code is an implementation of a multi-layer perceptron using SciKitLearn. This is a great way to implement it as it is a quick and elegant. The code that defines the architecture of the MLP is the following line: model = MLPClassifier (alpha = 0.01, batch_size = 20, epsilon = 1e-08, hidden_layer_sizes = (450,200), learning_rateDATA SCIENCE ULTIMA
Statistics. Basic concepts, descriptive statistics, inferential statistics such as hypothesis testing. This is the academic side of data science, essentially. BASIC DEEP LEARNING CONCEPTS Skip to content. Data Handling. Importing Data. From a CSV File; From the Web; From a Folder; From a PDFCOMPUTER VISION
Skip to content. Data Handling. Importing Data. From a CSV File; From the Web; From a Folder; From a PDFGREEDY ALGORITHMS
Greedy Algorithms. A greedy algorithm makes the optimal choice from those choices available at the time of decision. An example might be a driver taking the streets with the highest speed limit, with no regard for whether one of the streets with a lower speed limit leads to MULTIVARIABLE CALCULUS Multivariable Calculus. Partial Differentiation Explicit Differentiation Basic Example. The rule of thumb is to treat whatever variable we are not taking the derivative of as a constant: AUDIO SIGNAL PROCESSING Audio Signal Processing. There are two general approaches to audio processing with deep learning: Turn the audio file into an image, typically a log-scaled mel spectrogram wavelength. SQL SELECT STATEMENT SQL Select Statement. The select statement is used to grab data from a table. Select all values from a table. SELECT * FROM tbl; — Select all columns and rows in the tbl table. BIG O (TIME COMPLEXITY) Big O Notation: Name: Example: O(1) Constant: Choosing one item by index from an array, no matter how long the array it will be the same. O(log n) Logarithmic ONE-WAY ANALYSIS OF VARIANCE (ANOVA) Skip to content. Data Handling. Importing Data. From a CSV File; From the Web; From a Folder; From a PDF MULTI-LAYER PERCEPTRON (MLP) The above code is an implementation of a multi-layer perceptron using SciKitLearn. This is a great way to implement it as it is a quick and elegant. The code that defines the architecture of the MLP is the following line: model = MLPClassifier (alpha = 0.01, batch_size = 20, epsilon = 1e-08, hidden_layer_sizes = (450,200), learning_rate BASIC DEEP LEARNING CONCEPTS Skip to content. Data Handling. Importing Data. From a CSV File; From the Web; From a Folder; From a PDF BLOG – DATA SCIENCE ULTIMA Skip to content. Data Handling. Importing Data. From a CSV File; From the Web; From a Folder; From a PDFSTANDARDIZING DATA
Standardizing Data Intuition. Its roughly centering data around the origin of the axis. This is achieved by subtracting the mean of the data from each individual observation:STACKS AND QUEUES
Skip to content. Data Handling. Importing Data. From a CSV File; From the Web; From a Folder; From a PDFVARIABLE TYPES
Variable Types Categorical (Qualitative) Nominal. Nominal data is comprised of named variables that serve as a category. It has no clear property such as an order or a direction.NORMALIZING DATA
Skip to content. Data Handling. Importing Data. From a CSV File; From the Web; From a Folder; From a PDF IMPORTING DATA FROM THE WEB Skip to content. Data Handling. Importing Data. From a CSV File; From the Web; From a Folder; From a PDF SIMPLE LINEAR REGRESSION Skip to content. Data Handling. Importing Data. From a CSV File; From the Web; From a Folder; From a PDF CHAIN RULE – DATA SCIENCE ULTIMA Skip to content. Data Handling. Importing Data. From a CSV File; From the Web; From a Folder; From a PDF BIG O (TIME COMPLEXITY) Skip to content. Data Handling. Importing Data. From a CSV File; From the Web; From a Folder; From a PDFDATA SCIENCE ULTIMA
Statistics. Basic concepts, descriptive statistics, inferential statistics such as hypothesis testing. This is the academic side of data science, essentially.VARIABLE TYPES
Variable Types Categorical (Qualitative) Nominal. Nominal data is comprised of named variables that serve as a category. It has no clear property such as an order or a direction.NORMALIZING DATA
Skip to content. Data Handling. Importing Data. From a CSV File; From the Web; From a Folder; From a PDF GIT – DATA SCIENCE ULTIMA Skip to content. Data Handling. Importing Data. From a CSV File; From the Web; From a Folder; From a PDF IMPORTING DATA FROM THE WEB Skip to content. Data Handling. Importing Data. From a CSV File; From the Web; From a Folder; From a PDFGREEDY ALGORITHMS
Greedy Algorithms. A greedy algorithm makes the optimal choice from those choices available at the time of decision. An example might be a driver taking the streets with the highest speed limit, with no regard for whether one of the streets with a lower speed limit leads to AUDIO SIGNAL PROCESSING Audio Signal Processing. There are two general approaches to audio processing with deep learning: Turn the audio file into an image, typically a log-scaled mel spectrogram wavelength. MULTIVARIABLE CALCULUS Multivariable Calculus. Partial Differentiation Explicit Differentiation Basic Example. The rule of thumb is to treat whatever variable we are not taking the derivative of as a constant: SQL SELECT STATEMENT SQL Select Statement. The select statement is used to grab data from a table. Select all values from a table. SELECT * FROM tbl; — Select all columns and rows in the tbl table. STATISTICS RULES OF THUMB Skip to content. Data Handling. Importing Data. From a CSV File; From the Web; From a Folder; From a PDFDATA SCIENCE ULTIMA
Statistics. Basic concepts, descriptive statistics, inferential statistics such as hypothesis testing. This is the academic side of data science, essentially.VARIABLE TYPES
Variable Types Categorical (Qualitative) Nominal. Nominal data is comprised of named variables that serve as a category. It has no clear property such as an order or a direction.NORMALIZING DATA
Skip to content. Data Handling. Importing Data. From a CSV File; From the Web; From a Folder; From a PDF GIT – DATA SCIENCE ULTIMA Skip to content. Data Handling. Importing Data. From a CSV File; From the Web; From a Folder; From a PDF IMPORTING DATA FROM THE WEB Skip to content. Data Handling. Importing Data. From a CSV File; From the Web; From a Folder; From a PDFGREEDY ALGORITHMS
Greedy Algorithms. A greedy algorithm makes the optimal choice from those choices available at the time of decision. An example might be a driver taking the streets with the highest speed limit, with no regard for whether one of the streets with a lower speed limit leads to AUDIO SIGNAL PROCESSING Audio Signal Processing. There are two general approaches to audio processing with deep learning: Turn the audio file into an image, typically a log-scaled mel spectrogram wavelength. MULTIVARIABLE CALCULUS Multivariable Calculus. Partial Differentiation Explicit Differentiation Basic Example. The rule of thumb is to treat whatever variable we are not taking the derivative of as a constant: SQL SELECT STATEMENT SQL Select Statement. The select statement is used to grab data from a table. Select all values from a table. SELECT * FROM tbl; — Select all columns and rows in the tbl table. STATISTICS RULES OF THUMB Skip to content. Data Handling. Importing Data. From a CSV File; From the Web; From a Folder; From a PDF BASIC DEEP LEARNING CONCEPTS Skip to content. Data Handling. Importing Data. From a CSV File; From the Web; From a Folder; From a PDFSTACKS AND QUEUES
Skip to content. Data Handling. Importing Data. From a CSV File; From the Web; From a Folder; From a PDF MEAN, MEDIAN AND MODE Skip to content. Data Handling. Importing Data. From a CSV File; From the Web; From a Folder; From a PDFDATA SCIENCE ULTIMA
Statistics. Basic concepts, descriptive statistics, inferential statistics such as hypothesis testing. This is the academic side of data science, essentially. BLOG – DATA SCIENCE ULTIMA Skip to content. Data Handling. Importing Data. From a CSV File; From the Web; From a Folder; From a PDF MULTIVARIABLE CALCULUS Multivariable Calculus. Partial Differentiation Explicit Differentiation Basic Example. The rule of thumb is to treat whatever variable we are not taking the derivative of as a constant:VARIABLE TYPES
Variable Types Categorical (Qualitative) Nominal. Nominal data is comprised of named variables that serve as a category. It has no clear property such as an order or a direction. IMPORTING DATA FROM THE WEB Skip to content. Data Handling. Importing Data. From a CSV File; From the Web; From a Folder; From a PDFCOMPUTER VISION
Skip to content. Data Handling. Importing Data. From a CSV File; From the Web; From a Folder; From a PDFGREEDY ALGORITHMS
Greedy Algorithms. A greedy algorithm makes the optimal choice from those choices available at the time of decision. An example might be a driver taking the streets with the highest speed limit, with no regard for whether one of the streets with a lower speed limit leads to STATISTICS RULES OF THUMB Skip to content. Data Handling. Importing Data. From a CSV File; From the Web; From a Folder; From a PDF CONVOLUTIONAL NEURAL NETWORK (CNN) Skip to content. Data Handling. Importing Data. From a CSV File; From the Web; From a Folder; From a PDF ONE-WAY ANALYSIS OF VARIANCE (ANOVA) Skip to content. Data Handling. Importing Data. From a CSV File; From the Web; From a Folder; From a PDFDATA SCIENCE ULTIMA
Statistics. Basic concepts, descriptive statistics, inferential statistics such as hypothesis testing. This is the academic side of data science, essentially. BLOG – DATA SCIENCE ULTIMA Skip to content. Data Handling. Importing Data. From a CSV File; From the Web; From a Folder; From a PDF MULTIVARIABLE CALCULUS Multivariable Calculus. Partial Differentiation Explicit Differentiation Basic Example. The rule of thumb is to treat whatever variable we are not taking the derivative of as a constant:VARIABLE TYPES
Variable Types Categorical (Qualitative) Nominal. Nominal data is comprised of named variables that serve as a category. It has no clear property such as an order or a direction. IMPORTING DATA FROM THE WEB Skip to content. Data Handling. Importing Data. From a CSV File; From the Web; From a Folder; From a PDFCOMPUTER VISION
Skip to content. Data Handling. Importing Data. From a CSV File; From the Web; From a Folder; From a PDFGREEDY ALGORITHMS
Greedy Algorithms. A greedy algorithm makes the optimal choice from those choices available at the time of decision. An example might be a driver taking the streets with the highest speed limit, with no regard for whether one of the streets with a lower speed limit leads to STATISTICS RULES OF THUMB Skip to content. Data Handling. Importing Data. From a CSV File; From the Web; From a Folder; From a PDF CONVOLUTIONAL NEURAL NETWORK (CNN) Skip to content. Data Handling. Importing Data. From a CSV File; From the Web; From a Folder; From a PDF ONE-WAY ANALYSIS OF VARIANCE (ANOVA) Skip to content. Data Handling. Importing Data. From a CSV File; From the Web; From a Folder; From a PDFSTACKS AND QUEUES
Skip to content. Data Handling. Importing Data. From a CSV File; From the Web; From a Folder; From a PDF MEAN, MEDIAN AND MODE Skip to content. Data Handling. Importing Data. From a CSV File; From the Web; From a Folder; From a PDFSkip to content
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The core aspects of data science such as classical machine learning and deep learning are explored in this section of the website.DATA VISUALIZATION
This section explores communicating data with graphs and the like as well as guides to tools such as tableau and Power BI.MATHEMATICS
Linear algebra and calculus forms the backbone of many statistical and machine learning techniques. This is necessary to learn to have a more full understanding of data science.PROGRAMMING
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* SQL
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* Intermediate SQL
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* Statistics __
* Fundamentals
* Rules of Thumb
* Variable Types
* Performance Evaluation Metrics * Probability Theory* Power
* Coefficient of Multiple Determination (R2) * Residual Sum of Squares (RSS) * Descriptive Statistics * Discriminant Function Analysis (DFA) * Canonical Correlation Analysis (CCA) * Multi-dimensional Scaling (MDS) * Multivariate Distance Measures (DM) * Principal Component Analysis * Measures of Central Tendency * Mean, Median and Mode * Measures of Variability (spread)* Range
* Inferential Statistics* Cluster Analysis
* Factor Analysis
* Hypothesis Testing* ANOVA
* ANOM
* Regression
* Simple Linear Regression * Logistic Regression* Ridge Regression
* Lasso Regression
* Elastic Net Regression* Tobit Regression
* Cox Regression
* Quasi Poisson Regression * Negative Binomial Regression * Poisson Regression * Ordinal Regression * Support Vector Regression * Partial Least Squares (PLS) Regression * Principal Components Regression (PCR) * Quantile Regression * Polynomial Regression* Kernel Regression
* Experimental Design* Randomization
* Control
* Factorial Design
* Machine Learning/AI__
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* Random Forest / Decision Tree Ensemble * Logistic Regression * Gradient Boosting Algorithms (gbm)* Naive Bayes
* Deep Learning Concepts* Basics
* Multi-Layer Perceptron (MLP) * Convolutional Neural Network (CNN) * Recurrent Neural Network (RNN) * Long Short Term Memory (LSTM) * Deep Neural Networks (DNNs) * Region Based Convolutional Neural Networks (R-CNN) * Deep Belief Network* Autoencoder
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