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.

Templatesr

Export Data as csv in R Template

The Export Data as csv in R template shows how to export a data set from R and save it as an csv file. The file in question contains an employee database. Some other related topics you might be interested in are Calculating Standard Deviation in R, Calculating Data Mean in R, Calculating Standard Deviation of Data in R, Exploring Data Skewness in R.
You can now download the R template for free.
Export Data as csv in R template is among the topics covered in detail in the 365 Data Science program.

The Adding a New Column to a Data Frame in R template shows how to create a dataframe in R and add additional columns to it. We create a Star Wars data frame containing movies and stats and include two additional columns to extend the dataframe. Some other related topics you might be interested are Adding a New Row to a Data Frame in R, Dealing with Missing Data in R, Read a csv File into R, Export Data as csv in R.
You can now download the R template for free.
Adding a New Column to a Data Frame in R template is among the topics covered in detail in the 365 Data Science program.

The Adding a New Row to a Data Frame in R template shows how to add a row in an R data frame. The file shows how to create a data frame object in R from scratch, containing information and stats about movies and then shows how to extend the data frame by adding new columns. Some other related topics you might be interested are Adding a New Column to a Data Frame in R, Dealing with Missing Data in R, Read a csv File into R, Export Data as csv in R.
You can now download the R template for free.
Adding a New Row to a Data Frame in R template is among the topics covered in detail in the 365 Data Science program.

The Read a csv File in R template shows how to import a csv file into R with the help of the read csv() function. The file in question contains a real estate data set detailing information on different properties. Some other related topics you might be interested are Scatter plot with ggplot2 in R, Regression Scatter with ggplot2 in R, and Correlation between Two Variables in R. You can now download the R template for free.
The Read a csv File in R template is among the topics covered in detail in the 365 Data Science program.

The Linear Regression in R template shows how to perform a linear regression in R using lm() on real estate property data. Some other related topics you might be interested are Scatter plot with ggplot2 in R, Regression Scatter with ggplot2 in R, and Correlation between Two Variables in R.
You can now download the R template for free.
Linear Regression in R template is among the topics covered in detail in the 365 Data Science program.

The Correlation between Two Variables in R template shows how to calculate the correlation coefficient between two variables in a dataset with the help of the corr() function. Some other related topics you might be interested in checking are Calculating Data Variance in R, Calculating Data Mean in R, Calculating Standard Deviation of Data in R, and Exploring Data Skewness in R.
You can now download the R template for free. Correlation between Two Variables in R is among the topics covered in detail in the 365 Data Science program.

The While Loops in R shows how to create a while loop which sums up the numbers from 1 to n. A while loop operates as long as, or while its condition evaluates to TRUE. In contrast to a for loop which iterates over a sequence of number, the while loop evaluates whether a condition holds or not. Some other related topics you might be interested in exploring are For Loops in R and If Else Else If Statements in R.
You can now download the R template for free.
Calculating Data Variance in R is among the topics covered in detail in the 365 Data Science program.

The Calculating Data variance in R shows how variance measures the dispersion of a set of data points around their mean value. Some other related topics you might be interested are Calculating Standard Deviation in R, Calculating Data Mean in R, Calculating Standard Deviation of Data in R, Exploring Data Skewness in R
You can now download the R template for free.
Calculating Data Variance in R is among the topics covered in detail in the 365 Data Science program.

The If Else Else IF Statements in R template shows whether a certain condition is met and can perform distinct actions provided the condition is met, and provided it is not. Some other related topics you might be interested in exploring are While loops in R and For Loops in R
You can now download the R template for free.
If Else Else If Statements in R is among the topics covered in detail in the 365 Data Science program.

The Scatter Plot with ggplot2 in R includes a color scheme from the Wes Anderson movie palette. The chart displays the relationship between the price and area of houses from a property data set. A scatter plot is one of the most widely used charts in statistics and data analytics. It shows a collection of data points across two axes, signifying two categories of the data we wish to compare. Some other related topics you might be interested are Bar chart with ggplot2 in R, Histogram with ggplot2 in R, Regression Scatter Plot with ggplot2 in R, and Line Chart with ggplot2 in R.
You can now download the R template for free.

The Exploring Data Skewness in R template shows whether the observations in a data set are concentrated on one side. Here you can see examples of three different data skews implemented in R - right skewness, zero skewness and left skewness. Some other related topics you might be interested in exploring are Calculating Data Variance in R, Calculating Data Median in R, Calculating Standard Deviation of data in R.
You can now download the R template for free.
Calculating Data Median in R is among the topics covered in detail in the 365 Data Science program.

The following Calculating Data Median in R template shows the mean, also known as the simple average. We can find the mean of a data set by adding up all of its components and then dividing them by the number of components contained in the data set. Some other related topics you might be interested in exploring are Calculating Data Variance in R, Calculating Data Median in R, Calculating Standard Deviation of Data in R and Exploring Data Skewness in R.
You can now download the R template for free.
Calculating Data Median in R is among the topics covered in detail in the 365 Data Science program.

The following Stacked Area Chart with ggplot2 in R template shows the popularity of different engine types in automobiles across several decades. An area chart is used when we are interested in tracking and comparing the volume of a few variables over time. In a stacked area chart each category is ‘stacked’ or ‘placed’ on top of the previous and there is no overlap among categories. Some other related topics you might be interested in exploring are Line Chart with ggplot2 in R, Bar Chart with ggplot2 in R, Pie Chart with ggplot2 in R and Scatter Plot with ggpot2 in R.
You can now download the R template for free.
Stacked Area Chart with ggplot2 in R is among the topics covered in detail in the 365 Data Science program.

The following For Loops in R shows how to calculate the sum of the numbers from 1 to 10 using a for loop. For loops are one of the most commonly used tools by any programmer and they are used when we want to repeat an action in the loop for a certain number of times. Some other related topics you might be interested in exploring are While Loops in R and If Else Else If statements .

The following Creating a Data Frame Object in R template shows you how to create a data frame in R from scratch and populate with different values. The example shows a car data frame, containing 5 columns, 3 of which are numeric and 2 of which are strings. You can use this template to create and fill out your own data frame. Some other related topics you might be interested to explore are Calculating Data Variance in R, Calculating Data Mean in R, Calculating Standard Deviation of Data in R, Exploring Data Skewness in R.
You can now download the R template for free.
Creating a Data Frame Object in R is among the topics covered in detail in the 365 Data Science program.

This Dealing with Missing Data in R template shows you how to check for missing values in a dataset and count them. It also provides two ways of dealing with missing values in a dataset - either by substituting the missing values with the average, or by removing the entries which have missing values in them.
Some other related topics you might be interested to explore are correlation between two variables in R, export data as csv in R, calculating data median in R, calculating data mean in R, and calculating standard deviation of data in R.
You can now download the R template for free.
Dealing with missing data in R is among the topics covered in detail in the 365 Data Science e-learning program.

Calculating Standard Deviation of Data in R Template

This free .r template will show you how to calculate the standard deviation of data using the R programming language. The standard deviation is obtained by taking the square root of the variance of the data. It is used in many statistical analyses, whenever we have numerical data and is a measure of data dispersion. The standard deviation shows how closely data sits around the mean – a small value shows data, which is densely populated around the mean, a large standard deviation signifies data which is more dispersed.

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.

The histogram is a popular graph for visually representing the data distribution of a feature. This is a free .r histogram template showing the distribution of house prices implemented in R using ggplot2. You will be taught how to build the first three layers of a ggplot- defining the data, aesthetics, and geometry, and set bin parameters You finish by adding a title, changing the background theme and visually tweaking the aesthetics of your histogram.

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 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.

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.

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.

Incorporating URL parameters into a GET request in Python Template

A GET request is used when we want to obtain a certain document from a server- like a web page or API output. However, we can also add different parameters to the request to obtain a more specific result – either by modifying or adding additional information. In this free .ipynb template, we will show you how to incorporate such parameters into the URL by using the "requests" Python package.

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.

The seaborn library has been one of the most popular Python libraires in recent years. Compared to matplotlib, seaborn has simpler and more intuitive syntax, and wider visual-enhancing features. This free .ipynb Scatter Plot with Seaborn in Python template shows the relationship between the price and area of houses, based on real estate data. It's easy and intuitive to build and customize a scatter plot with the help of seaborn.

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 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.

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.

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.

Incorporating URL parameters into a GET request in Python Template

A GET request is used when we want to obtain a certain document from a server- like a web page or API output. However, we can also add different parameters to the request to obtain a more specific result – either by modifying or adding additional information. In this free .ipynb template, we will show you how to incorporate such parameters into the URL by using the "requests" Python package.

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.

The seaborn library has been one of the most popular Python libraires in recent years. Compared to matplotlib, seaborn has simpler and more intuitive syntax, and wider visual-enhancing features. This free .ipynb Scatter Plot with Seaborn in Python template shows the relationship between the price and area of houses, based on real estate data. It's easy and intuitive to build and customize a scatter plot with the help of seaborn.