Save hours of browsing through the Internet and accelerate your workflow with code-ready templates for your programming projects. Personally crafted by 365 Data Science instructors, these templates will bring a surprising level of efficiency to your work process.

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Logistic Regression with statsmodels in Python Template

The Logistic Regression with statsmodels in Python template shows how to solve a simple classification problem using the logistic regression model provided by the statsmodels library. The database used for the example is read using the pandas library.. Some other related topics you might be interested in are Confusion Matrix with statsmodels in Python, Logistic Regression Curve in Python, Model Accuracy in Python.
You can now download the Python template for free.
The Logistic Regression with statsmodels in Python template is among the topics covered in detail in the 365 Data Science program.

Confusion Matrix with statsmodels in Python Template

In this Confusion Matrix with statsmodels in Python template, we will show you how to solve a simple classification problem using the logistic regression algorithm. Then, we will create a python confusion matrix of the model using the statsmodels library and make the table more beautiful and readable with the help of the pandas library. Some other related topics you might be interested in are Logistic regression with statsmodels in Python, Logistic Regression Curve in Python, Model Accuracy in Python.
You can now download the Python template for free.
The Confusion Matrix with statsmodels in Python template is among the topics covered in detail in the 365 Data Science program.

Linear Regression Model in Python- Predictions versus Targets Template

In this Linear Regression Model in Python- predictions versus targets template, we will show you how to plot the predictions the model has made versus the true targets. Some other related topics you might be interested in are Predictions with statsmodels in Python, Feature Selection through Standardization with sklearn in Python, Predictions with standardized Coefficients with sklearn in Python, Visualizing Linear regressions with matplotlib in Python.
You can now download the Python template for free.
The Dummy Variables with pandas in Python template is among the topics covered in detail in the 365 Data Science program.

When preparing data for a machine learning algorithm, very often we see variables that do not bear numerical values The Dummy Variables with pandas in Python template demonstrates how to map categorical data onto numerical values using the pandas library. Some other related topics you might be interested in are Mapping Categorical to Numerical Data with pandas in Python, Removing Missing Values with pandas in Python, Removing Outliers with pandas in Python.
You can now download the Python template for free.
The Dummy Variables with pandas in Python template is among the topics covered in detail in the 365 Data Science program.

The OLS Assumptions in Python – No Multicollinearity

The OLS Assumptions in Python – No Multicollinearity shows how to detect possible collinearity between several data set features and deal with them. In this example, we investigate the possible collinearity between several car features and remove the unnecessary ones. Some other topics you might be interested in exploring are OLS Assumptions in Python - No Multicollinearity, Linear Regression Model in Python – Residuals.
You can now download the Python template for free.
The OLS Assumptions in Python - No Multicollinearity template is among the topics covered in detail in the 365 Data Science program.

The OLS Assumptions in Python - Linearity shows how to transform non-linear dependencies into linear. In this example, we check the dependencies between the price of a car with respect to the year of manufacturing, its price and its mileage. Some other related topics you might be interested are OLS assumptions in Python – Linearity and Linear regression model in Python - residuals.
You can now download the Python template for free.
The OLS Assumptions in Python - Linearity template is among the topics covered in detail in the 365 Data Science program.

The Removing Outliers with pandas in Python shows how to detect and remove samples that skew a dataset and might lead to building an inaccurate model. Some other related topics you might be interested are Removing Outliers with pandas in Python, Dummy Variables with pandas in Python, Feature Selection through p-values with sklearn in Python, Feature Selection through standardization with sklearn in Python, Linear Regression Model in Python – residuals.
You can now download the Python template for free.
The Removing Outliers with pandas in Python template is among the topics covered in detail in the 365 Data Science program.

Removing Missing Values with pandas in Python Template

The Removing Missing Values with pandas in Python shows how to detect and remove samples from a dataset that contain missing values. Some other related topics you might be interested are Removing Outliers with pandas in Python, Dummy Variables with pandas in Python, Feature Selection through p-values with sklearn in Python, Feature Selection through standardization with sklearn in Python, Linear Regression Model in Python – residuals.
You can now download the Python template for free.
The Removing Missing Values with pandas in Python template is among the topics covered in detail in the 365 Data Science program.

Train and Test Split with sklearn in Python Template

The Train and Test Split with sklearn in Python template shows how to prevent overtraining of the machine learning algorithm by using the convenient train_test_split() method provided by sklearn to split a database into two parts - a training and a testing dataset. Some other related topics you might be interested are Regression Summary Table with sklearn in Python, Feature Selection through p-values with sklearn in Python, Feature Selection through Standardization with sklearn in Python, Predictions with Standardized Coefficients with sklearn in Python.
You can now download the Python template for free.
The Train and Test Split with sklearn in Python template is among the topics covered in detail in the 365 Data Science program.

Predictions with Standardized Coefficients with sklearn in Python Template

The Predictions with Standardized Coefficients with sklearn in Python shows how to predict values using a model that was fit on standardized inputs. First, we solve a multiple linear regression problem with two continuous features using the machine learning package sklearn, after which we apply standardization. Some other related topics you might be interested are Predictions with statsmodels in Python, Feature Selection through Standardization with sklearn in Python, Visualizing Linear Regressions with matplotlib in Python.
You can now download the Python template for free.
The Predictions with Standardized Coefficients with sklearn in Python template is among the topics covered in detail in the 365 Data Science program.

The Correlation in Excel template demonstrates how the correlation coefficient can be calculated in Excel. Some other related topics you might be interested in are Calculating the Variance in Excel, Standard Deviation in Excel, Coefficient of Variation in Excel, Covariance in Excel.
You can now download the Excel template for free.
The Correlation in Excel template is among the topics covered in detail in the 365 Data Science program.

The Covariance in Excel template demonstrates how the covariance metric can be calculated in Excel. Some other related topics you might be interested in are Calculating the Variance in Excel, Standard Deviation in Excel, Coefficient of Variation in Excel, Correlation in Excel
You can now download the Excel template for free.
The Covariance in Excel template is among the topics covered in detail in the 365 Data Science program.

The following Standard Deviation in Excel template introduces the relevant Excel syntax for calculating sample standard deviation. Some other related topics you might be interested in are Calculating the variance in Excel, Coefficient of Variation in Excel, Covariance in Excel, Correlation in Excel
You can now download the Excel template for free.
Standard Deviation in Excel template is among the topics covered in detail in the 365 Data Science program.

The following Calculating the Variance in Excel template demonstrates the difference between sample variance and population variance. It introduces the relevant Excel syntax for calculating these 2 metrics. Some other related topics you might be interested in are Standard Deviation in Excel, Coefficient of Variation in Excel, Covariance in Excel, Correlation in Excel.
You can now download the Excel template for free.
Calculating the Variance in Excel template is among the topics covered in detail in the 365 Data Science program.

The following Negative Skew in Excel template includes a sample dataset resulting in a negatively (left) skewed frequency distribution graph. Some other related topics you might be interested in are Positive Skew in Excel, Zero Skew in Excel, Normal Distribution in Excel, Standard Normal Distribution in Excel.
You can now download the Excel template for free.
Negative Skew in Excel template is among the topics covered in detail in the 365 Data Science program.

The following Zero Skew in Excel template includes a sample dataset resulting in a zero skewed (no skew) frequency distribution graph Some other related topics you might be interested in are Positive Skew in Excel, Negative Skew in Excel, Normal Distribution in Excel, Standard Normal Distribution in Excel.
You can now download the Excel template for free.
Zero Skew in Excel template is among the topics covered in detail in the 365 Data Science program.

The Positive Skew in Excel template includes a sample dataset resulting in a positively (right) skewed frequency distribution graph. Some other related topics you might be interested in are Zero Skew in Excel, Negative Skew in Excel, Normal Distribution in Excel, Standard Normal Distribution in Excel.
You can now download the Excel template for free.
Positive Skew in Excel template is among the topics covered in detail in the 365 Data Science program.

The Calculating the Mode in Excel template shows how to apply the Excel function corresponding to finding the mode of a set of numbers. Some other related topics you might be interested in are Calculating the Mean in Excel, Calculating the Median in Excel, Covariance in Excel, and Correlation in Excel.
You can now download the Excel template for free.
Calculating the Mode in Excel template is among the topics covered in detail in the 365 Data Science program.

The Calculating the Median in Excel template shows how to apply the Excel function corresponding to finding the median of a set of numbers. Some other related topics you might be interested in are Calculating the Mean in Excel, Calculating the Mode in Excel, Covariance in Excel, and Correlation in Excel.

The Calculating the Mean in Excel template shows how to apply the Excel function corresponding to finding the mean of a set of numbers. Some other related topics you might be interested in are Pareto Diagram in Excel, Side-by-Side Bar Chart in Excel, Normal Distribution in Excel, Stacked Area Chart in matplotlib Python, Bar and Line chart in Excel
You can now download the Excel template for free.
Calculating the Mean in Excel template is among the topics covered in detail in the 365 Data Science program.

Check out our most helpful downloadable resources according to 365 Data Science’s students and expert team of instructors.

Templatespython

Linear Regression with statsmodels in Python Template

The following Linear Regression with Statsmodels in Python free .ipynb template shows how to solve a simple linear regression problem using the Ordinary Least Squares statsmodels library. We are going to examine the causal relationship between the independent variable in the dataset - SAT score of a student, and the dependent variable -the GPA score. This database is read with the help of the pandas library. Download and unzip the .zip file in a new folder. Inside the folder you will find a .csv and a .ipynb file. The first one contains the database and the second one contains the Python code. Open the .ipynb file using Jupyter notebook.

A box and whiskers chart graphically represents the distribution of data through their quartiles. It is often used in financial settings when analyzing the market volatility and can reveal the skewness of data or potential outliers. Тhis free .r template goes over the Titanic’s data set using the ggplot2 library in R, revealing interesting insights about e the survival rate based on age and sex. By following the outlined steps in this R template, you will learn how to convey the information professionally using the ggplot2 functionalities.

Developed in the 1970s by a group of IBM researchers, SQL continues to be the most popular programming language for relational database management and is used by companies like Facebook to store mounts of user data. None of this would be possible without the foundation of the SQL language- the database. That is the place where information is organized into tables and can be accessed, manipulated, and retrieved in any desired way. Consider this as the very first step in your SQL journey as this free sql template will show you how to make a SQL database in MYSQL.

A line chart is often used when we want to chronologically track the changes in value of a variable over a period of time and identify existing patterns and trends. Therefore, the line chart is often applied in financial statements, weather forecasts, stock market analysis and experiment statistics reports. This free .xlsx template displays the S&P 500 and Footsie indices for the second half of the economically devastating 2008 on a line chart

Feature Selection Through Standardization with sklearn in Python Template

The following Feature Selection Through Standardization with sklearn in Python template shows how to solve a multiple linear regression problem with two continuous features. These features are standardized using a StandardScaler() object. After fitting the model to the scaled data, we construct a summary table in the form of a dataframe. It stores the features as well as their biases and weights (the machine learning jargon for intercepts and coefficients). The irrelevant features are automatically penalized by a small magnitude of the weight. Such a procedure is known as feature scaling through standardization. Open the .ipynb file using Jupyter notebook. Another related topics is Feature selection through p-values with sklearn in Python. You can now download the Python template for free.
Feature Selection Through Standardization with sklearn in Python is among the topics covered in detail in the 365 Data Science program.

Pie charts are one of the most popular data visualization tools since they express the part-to whole relationship of a dataset in a very intuitive manner. As such they are best used when we want to communicate for example the revenue of each product and its relationship to the whole. The following free. xlsx template shows an Excel pie chart, displaying the number of cars using a particular type of fuel.

Joins are the SQL tools that allow us to work with data from multiple tables simultaneously relying on the logical relationship between their objects. The INNER JOIN clause, in particular, creates a new instance of a table that combines rows with matching values from two tables. Thus, null values, or values appearing only in one of the tables, will not be extracted. In this free sql template you will be applying the SQL Inner Join clause on a set of business department tables.

Feature Selection Through p-values with sklearn in Python Template

The following Feature Selection Through p-values with sklearn in Python template shows how to solve a multiple linear regression problem using the machine learning package sklearn. Based on the p-value of each feature, we can determine whether it is useful or irrelevant. Download and unzip the .zip file in a new folder. Inside the folder you will find a .csv and a .ipynb file. The first one contains the database and the second one contains the Python code. Open the .ipynb file using Jupyter notebook. Some other related topics are Feature selection through standardization with sklearn in Python.

Check out our most helpful downloadable resources according to 365 Data Science’s students and expert team of instructors.

Templatespython

Linear Regression with statsmodels in Python Template

The following Linear Regression with Statsmodels in Python free .ipynb template shows how to solve a simple linear regression problem using the Ordinary Least Squares statsmodels library. We are going to examine the causal relationship between the independent variable in the dataset - SAT score of a student, and the dependent variable -the GPA score. This database is read with the help of the pandas library. Download and unzip the .zip file in a new folder. Inside the folder you will find a .csv and a .ipynb file. The first one contains the database and the second one contains the Python code. Open the .ipynb file using Jupyter notebook.

A box and whiskers chart graphically represents the distribution of data through their quartiles. It is often used in financial settings when analyzing the market volatility and can reveal the skewness of data or potential outliers. Тhis free .r template goes over the Titanic’s data set using the ggplot2 library in R, revealing interesting insights about e the survival rate based on age and sex. By following the outlined steps in this R template, you will learn how to convey the information professionally using the ggplot2 functionalities.

Developed in the 1970s by a group of IBM researchers, SQL continues to be the most popular programming language for relational database management and is used by companies like Facebook to store mounts of user data. None of this would be possible without the foundation of the SQL language- the database. That is the place where information is organized into tables and can be accessed, manipulated, and retrieved in any desired way. Consider this as the very first step in your SQL journey as this free sql template will show you how to make a SQL database in MYSQL.

A line chart is often used when we want to chronologically track the changes in value of a variable over a period of time and identify existing patterns and trends. Therefore, the line chart is often applied in financial statements, weather forecasts, stock market analysis and experiment statistics reports. This free .xlsx template displays the S&P 500 and Footsie indices for the second half of the economically devastating 2008 on a line chart

Feature Selection Through Standardization with sklearn in Python Template

The following Feature Selection Through Standardization with sklearn in Python template shows how to solve a multiple linear regression problem with two continuous features. These features are standardized using a StandardScaler() object. After fitting the model to the scaled data, we construct a summary table in the form of a dataframe. It stores the features as well as their biases and weights (the machine learning jargon for intercepts and coefficients). The irrelevant features are automatically penalized by a small magnitude of the weight. Such a procedure is known as feature scaling through standardization. Open the .ipynb file using Jupyter notebook. Another related topics is Feature selection through p-values with sklearn in Python. You can now download the Python template for free.
Feature Selection Through Standardization with sklearn in Python is among the topics covered in detail in the 365 Data Science program.

Pie charts are one of the most popular data visualization tools since they express the part-to whole relationship of a dataset in a very intuitive manner. As such they are best used when we want to communicate for example the revenue of each product and its relationship to the whole. The following free. xlsx template shows an Excel pie chart, displaying the number of cars using a particular type of fuel.

Joins are the SQL tools that allow us to work with data from multiple tables simultaneously relying on the logical relationship between their objects. The INNER JOIN clause, in particular, creates a new instance of a table that combines rows with matching values from two tables. Thus, null values, or values appearing only in one of the tables, will not be extracted. In this free sql template you will be applying the SQL Inner Join clause on a set of business department tables.

Feature Selection Through p-values with sklearn in Python Template

The following Feature Selection Through p-values with sklearn in Python template shows how to solve a multiple linear regression problem using the machine learning package sklearn. Based on the p-value of each feature, we can determine whether it is useful or irrelevant. Download and unzip the .zip file in a new folder. Inside the folder you will find a .csv and a .ipynb file. The first one contains the database and the second one contains the Python code. Open the .ipynb file using Jupyter notebook. Some other related topics are Feature selection through standardization with sklearn in Python.