Templates
Boost your programming skills with free Python, SQL, R, and Excel templates and create top-notch projects for your portfolio.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 bring a surprising efficiency level to your work process.
Creating a Data Frame Object in R Template
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.
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Stacked Area Chart in Excel Template
This Stacked Area Chart in Excel template displays the use of different fuel types from the year 1975 to 2016 for used cars. The chart is created with the stacked area chart in Excel. For completion a 100% stacked version of the chart is shown. Some other related topics you might be interested to explore are Pie Chart in Excel, Line Chart in Excel, and Scatter Plot in Excel. You can now download the Excel template for free. Stacked Area Chart is among the topics covered in detail in the 365 Data Science program.
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Dealing with Missing Data in R Template
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.
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Line Chart in Excel Template
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
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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.
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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.
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Inner Join in SQL Template
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.
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Creating a database in SQL Template
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.
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Having Clause in SQL Template
SQL is specifically designed to help you navigate through large amounts of structured data, modifying, retrieving, and creating new tables out of it. The having clause works in conjunction with common aggregate functions like min, max, ave, count and sum, and allows you to filter and return group results that meet the specified conditions. In this free sql template, you will apply the having clause to display salaries that are higher than a certain value in ascending order.
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Box and Whiskers Chart with ggplot2 in R
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.
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Implementing a Stack in Python
Inevitably, all of us have experienced the stack structure in our daily lives- whether it is shuffling a deck of cards, using the undo button in Word, or browsing back and forth between webpages. In the following free .ipynb notebook, you will find an implementation of a Stack class from scratch – showing you how to check the stack’s status, append(push) as well as remove(pop) items from the stack
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Line and Scatterplots with Matplotlib in Python
The following notebook will guide you through the process of creating a line plot and a scatter plot using the matplotlib library in Python. The data that you are going to be working with in this free .ipynb template is the number of daily views on a video for a period of 30 days. You will learn how to customize the style of your plot by changing the figure sizes, and the line’s style and colors. You will also learn how to put markers on the plot, give name to the axes and put a title on the figure.
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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.
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Handling JSON with “json” library in Python Template
This is a free .ipynb template demonstrating how to handle JSON files using Python and the 'json' library. JSON is a standard data exchange format, frequently encountered on the web, mainly as the output of APIs. It uses 2 main datatypes common to many programming languages - lists and dictionaries, which are going to be covered in the template.
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Scatter Plot with Seaborn in Python Template
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.
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Pie Chart in Excel Template
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.
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Histogram with ggplot2 in R Template
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.
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Sending a GET Request in Python Template
This free .ipynb template demonstrates how to send an HTTP GET request in Python which is the backbone of the modern internet- the most popular one is the GET request. This type of request is the most basic form of request since it is sent to servers when opening web pages or accessing APIs. In this template, we have implemented a simple GET request using the Python library "requests".
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Stacked Area Chart Notebook in Matplotlib Python Template
In this free .ipynb template we have a stacked area chart, implemented in Python with the Pyplot module of Matplotlib. The chart shows the popularity of different engine types in automobiles across the span of several decades. In the stacked area chart each category is ‘stacked’ or ‘placed’ on top of the previous, presenting the totality of the data and avoiding overlap among categories.
Learn MoreCheck out our most helpful downloadable resources according to 365 Data Science students and our expert team of instructors.
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.
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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.
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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.
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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 MoreMost Popular Templates
Check out our most helpful downloadable resources according to 365 Data Science students and our expert team of instructors.
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 MoreData 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 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 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
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