Data Science Templates
Boost your programming skills with free SQL, R, Excel, and Python templates and create top-notch projects for your portfolio.Accelerate your workflow with code-ready data science templates from 365 Data Science instructors—designed to enhance efficiency in your programming projects.
Discover a myriad of valuable resources—from a time-saving data science cheat sheet to data analysis and visualization templates in Excel and ready-to-use SQL, R, and Python coding shortcuts.
Whether you’re unsure how to approach a task or work on a time-sensitive project, these templates are designed to streamline your work and enhance productivity.
Searching by Attributes with Beautiful Soup in Python Template
The Searching by Attributes with Beautiful Soup in Python is a template that shows how we can incorporate attributes in our search for tags using the 'find' and 'find_all' methods of Beautiful Soup. Some other related topics you might be interested in are Incorporating URL Parameters into a GET Request, Sending a GET request in Python, Commonly Used HTML Tags in Python, and Setting up Beautiful Soup and Choosing a Parser in Python. You can now download the Python template for free. The Searching by Attributes with Beautiful Soup in Python template is among the topics covered in detail in the 365 Data Science program.
Learn More
Searching for Тags with Beautiful Soup - find and find all in Python Template
This is a template that shows how we can use the 'find' and 'find_all' methods of Beautiful Soup to search for tags in the HTML document. It also demonstrates what happens if no tag is found. Some other related topics you might be interested in are Incorporating URL Parameters into a GET Request, Sending a GET request in Python, Commonly Used HTML Tags in Python, and Setting up Beautiful Soup and Choosing a Parser in Python. You can now download the Python template for free. The Searching for Тags with Beautiful Soup - find and find all in Python template is among the topics covered in detail in the 365 Data Science program.
Learn More
Setting up Beautiful Soup and Choosing a Parser in Python Template
The Setting up Beautiful Soup and Choosing a Parser in Python Template shows the first steps needed to be taken when starting to scrape with Beautiful Soup - connecting to the website, checking out the html, creating the soup and choosing a Parser, and finally, exporting the html to a file. Some other related topics you might be interested in are Incorporating URL Parameters into a GET Request, Sending a GET request in Python, Commonly Used HTML Tags in Python. You can now download the Python template for free. The Setting up Beautiful Soup and Choosing a Parser in Python template is among the topics covered in detail in the 365 Data Science program.
Learn More
Reading from and Writing to Files in Python Template
The Reading from and Writing to Files in Python Template demonstrates how one can read and write files in Python. It also introduces the 'with' statement through which we can automatically close the file after we finish working with it. Some other related topics you might be interested in are Incorporating URL Parameters into a GET Request, Sending a GET request in Python, Commonly Used HTML Tags in Python. You can now download the Python template for free. The Reading from and Writing to Files in Python template is among the topics covered in detail in the 365 Data Science program.
Learn More
Request Headers and Emulating a Browser in Python Template
In the Request Headers and Emulating a Browser in Python template we explore how to define different request headers and also manipulate the 'User-Agent' string in order to pretend that the request was sent through a browser. Some other related topics you might be interested in are Incorporating URL Parameters into a GET Request, Sending a GET request in Python, Reading from and Writing to Files in Python. You can now download the Python template for free. The Request Headers and Emulating a Browser in Python template is among the topics covered in detail in the 365 Data Science program.
Learn More
R-squared and Adjusted R-squared with sklearn in Python Template
The R-squared and Adjusted R-squared with sklearn in Python demonstrates how to return the R-squared and R-squared values of a model when performing linear regression. Some other related topics you might be interested in 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. You can now download the Python template for free. The R-squared and Adjusted R-squared with sklearn in Python template is among the topics covered in detail in the 365 Data Science program.
Learn More
Visualizing Linear Regressions with Matplotlib in Python Template
The Visualizing Linear Regressions with Matplotlib in Python template demonstrates how to plot the regression line of a linear regression model onto the data. We go through the steps of loading the data from a .csv file, then mapping dummy variables onto numerical values, performing a linear regression using statsmodels and, finally, visualize what we have created. Some other related topics you might be interested in are Regression Summary Table with statsmodels in Python, Predictions with statsmodels in Python, Linear Regression Model in Python - predictions versus targets. eeee You can now download the Python template for free. The Visualizing Linear Regressions with Matplotlib in Python template is among the topics covered in detail in the 365 Data Science program.
Learn More
Heatmaps and Dendrograms with seaborn in Python Template
The Heatmaps and Dendrograms with seaborn in Python template demonstrates how to create heatmaps and dendrograms using the seaborn package in Python.Some other related topics you might be interested in are K-Means Clustering of Numerical Data with sklearn in Python, The Elbow Method for K-Means Clustering in Python, K-Means Clustering of Categorical Data with sklearn in Python. You can now download the Python template for free. The Heatmaps and Dendrograms with seaborn in Python template is among the topics covered in detail in the 365 Data Science program.
Learn More
The Elbow Method for K-Means Clustering in Python Template
The Elbow Method for K-Means Clustering in Python template demonstrates a way to determine the most optimal value of K in a K-Means clustering problem. Recall that K represents the numbers of clusters. The way this is done is through the so-called elbow method which requires calculating the within-cluster sum of squares for each number of clusters.. Some other related topics you might be interested in are K-Means Clustering of Numerical Data with sklearn in Python, Heatmaps and Dendrograms with seaborn in Python, K-Means Clustering of Categorical Data with sklearn in Python. You can now download the Python template for free. The Elbow Method for K-Means Clustering in Python template is among the topics covered in detail in the 365 Data Science program.
Learn More
K-Means Clustering of Numerical Data with sklearn in Python Template
The K-Means Clustering of Numerical Data with sklearn in Python template shows how to solve a simple clustering problem using the K-Means algorithm provided by the sklearn machine learning package. After performing the clustering, we will visualize the results and identify the clusters. Some other related topics you might be interested in are The Elbow Method for K-Means Clustering in Python, Heatmaps and Dendrograms with seaborn in Python, K-Means Clustering of Categorical Data with sklearn in Python. You can now download the Python template for free. The K-Means Clustering of Numerical Data with sklearn in Python is among the topics covered in detail in the 365 Data Science program.
Learn More
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.
Learn More
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.
Learn More
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.
Learn More
Dummy Variables with pandas in Python Template
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.
Learn More
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.
Learn More
OLS Assumptions in Python - Linearity Template
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.
Learn More
Removing Outliers with pandas in Python Template
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.
Learn More
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.
Learn More
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.
Learn More
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.
Learn MoreExplore our top data science templates, recommended by our students and expert team of instructors—free to download.
Data Science Shortcuts Cheat Sheet
Discover how to boost your productivity using this data science shortcuts cheat sheet with over 2,000 workarounds in Python IDEs, such as Jupyter, Spyder Rodeo, PyCharm, and Atom, compatible with various operating systems. Amplify your proficiency in R with R Studio shortcuts, streamline MATLAB operations, and manage databases efficiently with SQL shortcuts. Enhance data visualization in Tableau, easily manage Excel spreadsheets, and conduct statistical analyses seamlessly in SPSS and SAS. This data science shortcuts cheat sheet lets you speed up your everyday tasks while achieving your goals.
Learn More
Obtaining Descriptive Statistics about the Data in Python
The following template demonstrates how to obtain an overview about the dataset. It shows the application of the .describe() method on a pandas Series object. Some other related topics you might be interested in are Delivering an Array with the Unique Values from a Dataset in Python, Converting Series into Arrays in Python, Ordering the Rows from a Data Table According to the Values in a Column in Python, Data Selection in Python, and Common Attributes for Working with DataFrames in Python. The Obtaining Descriptive Statistics about the Data in Python template is among the topics covered in detail in the 365 Program.
Learn More
Normal Distribution in Excel Template
This Normal Distribution in Excel template demonstrates that the sum of 2 randomly thrown dice is normally distributed. Open the .xlsx file with Microsoft Excel. Study the structure of the file and experiment with different values. Some other related topics you might be interested to explore are Positive Skew in Excel, Zero Skew in Excel, Negative Skew in Excel, Uniform Distribution in Excel, Standard Normal Distribution in Excel You can now download the Excel template for free. Normal Distribution in Excel is among the topics covered in detail in the 365 Data Science program
Learn More
Common Attributes for Working with DataFrames in Python
The following template demonstrates the application of important pandas attributes when cleaning, preprocessing, and analyzing a dataset. Some other related topics you might be interested in are Data Selection in Python, Indexing with.iloc[] and .loc[] in Python, Delivering an Array with the Unique Values from a Dataset in Python, Converting Series into Arrays in Python, and Using Pandas Methods for Working with Series Objects in Python. The Common Attributes for Working with DataFrames in Python template is among the topics covered in detail in the 365 Program.
Learn MoreMost Popular Templates
Explore our top data science templates, recommended by our students and expert team of instructors—free to download.
Data Science Shortcuts Cheat Sheet
Discover how to boost your productivity using this data science shortcuts cheat sheet with over 2,000 workarounds in Python IDEs, such as Jupyter, Spyder Rodeo, PyCharm, and Atom, compatible with various operating systems. Amplify your proficiency in R with R Studio shortcuts, streamline MATLAB operations, and manage databases efficiently with SQL shortcuts. Enhance data visualization in Tableau, easily manage Excel spreadsheets, and conduct statistical analyses seamlessly in SPSS and SAS. This data science shortcuts cheat sheet lets you speed up your everyday tasks while achieving your goals.
Learn MoreObtaining Descriptive Statistics about the Data in Python
The following template demonstrates how to obtain an overview about the dataset. It shows the application of the .describe() method on a pandas Series object. Some other related topics you might be interested in are Delivering an Array with the Unique Values from a Dataset in Python, Converting Series into Arrays in Python, Ordering the Rows from a Data Table According to the Values in a Column in Python, Data Selection in Python, and Common Attributes for Working with DataFrames in Python. The Obtaining Descriptive Statistics about the Data in Python template is among the topics covered in detail in the 365 Program.
Learn MoreNormal Distribution in Excel Template
This Normal Distribution in Excel template demonstrates that the sum of 2 randomly thrown dice is normally distributed. Open the .xlsx file with Microsoft Excel. Study the structure of the file and experiment with different values. Some other related topics you might be interested to explore are Positive Skew in Excel, Zero Skew in Excel, Negative Skew in Excel, Uniform Distribution in Excel, Standard Normal Distribution in Excel You can now download the Excel template for free. Normal Distribution in Excel is among the topics covered in detail in the 365 Data Science program
Learn MoreCommon Attributes for Working with DataFrames in Python
The following template demonstrates the application of important pandas attributes when cleaning, preprocessing, and analyzing a dataset. Some other related topics you might be interested in are Data Selection in Python, Indexing with.iloc[] and .loc[] in Python, Delivering an Array with the Unique Values from a Dataset in Python, Converting Series into Arrays in Python, and Using Pandas Methods for Working with Series Objects in Python. The Common Attributes for Working with DataFrames in Python template is among the topics covered in detail in the 365 Program.
Learn MoreJoin 3M+ Students and Start Learning
Learn from the best, develop an invaluable skillset, and secure a job in data and AI.
