Defining Classes in Python
In Python, we can design and create our own objects. This is done with the help of classes. In this template notebook, we define a very simplistic class without a body and create an object which is an instance of that class. Finally, we store and retrieve information from that object. Some other related topics you might be interested in are Creating a constructor in Python - the INIT method, The pass-statement in Python, Class variables in Python, Methods in Python - functions inside classes, Inheritance in Python. The Defining Classes in Python template is among the topics covered in the 365 Data Science 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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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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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
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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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Histogram in Excel Template
This Histogram in Excel includes a sample dataset, a frequency distribution table constructed from this dataset, and 2 histograms visualizing the data - one representing frequency and a second one representing relative frequency. Some other related topics you might be interested to explore are Pie Chart in Excel, Line Chart in Excel , Bar and Line Chart in Excel and Stacked Area Chart in Excel. You can now download the Excel template for free. Histogram in Excel is among the topics covered in detail in the 365 Data Science program.
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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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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.
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Correlation in Excel Template
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.
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