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To make your code easier to use and share with others, learn how to develop your own Python packages. Putting your code within Python packages can be beneficial. You will learn about package structure in this course, along with the additional files required to turn loose code into useful packages. Additionally, you'll discover how to use flake8 to preserve code style and learn about documentation and import structure. Finally, you'll discover how to create and upload your packages. Start learning by purchasing this Developing Python Packages course.

Linear regression and logistic regression are the two most commonly used statistical models, acting as master keys to reveal the secrets buried in datasets. This DataCamp Intermediate Regression in R course covers linear and logistic regression with many explanatory variables, building on the knowledge you obtained in "Introduction to Regression in R." By the end of the Intermediate Regression in R course, you'll grasp how to incorporate many explanatory factors into a model, how interactions between variables affect predictions, and how logistic and linear regression function. Take a step to begin your career with DataCamp.

Tableau's built-in statistical functions can help you advance your reporting abilities. You'll discover how to use drag and drop analytics to perform univariate and bivariate exploratory data analysis and build regression models to find unnoticed trends. You'll also apply machine learning techniques like clustering and forecasting while working with real-world information. Investigate your data more thoroughly now with DataCamp. Purchase the course by using DataCamp Statistical Techniques in Tableau using DataCamp Discount Code to grab offers.

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Data scientists are supposed to spend 80% of their time cleaning and modifying data and just 20% of their time evaluating it. The time spent cleaning is crucial because using stale data to analyze can result in false results. In data science, cleansing data is a crucial effort. Any type of data analysis or machine learning model could produce erroneous results without adequately cleaned data. In this DataCamp Cleaning Data in Python course you will learn how to recognise, assess, and resolve a range of Python data cleaning issues from basic to advanced.

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