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Publisher : O’Reilly Media; 1st edition (October 24, 2023)
Language : English
Paperback : 594 pages
ISBN-10 : 1098113004
ISBN-13 : 978-1098113001
Item Weight : 2.1 pounds
Dimensions : 7.4 x 1.2 x 9.2 inches
Are you interested in learning data science with Python? In this post, we will cover the essential steps in the data science process: data wrangling, exploration, visualization, and modeling.
Data wrangling is the process of cleaning and transforming raw data into a usable format for analysis. This step is crucial as the quality of your data will directly impact the accuracy of your models. Python provides powerful tools such as Pandas and NumPy for data wrangling tasks like cleaning missing values, handling categorical variables, and scaling features.
Once your data is cleaned and prepared, the next step is data exploration. This involves analyzing and understanding the patterns and relationships in your data. Python libraries like Matplotlib and Seaborn make it easy to create visualizations such as histograms, scatter plots, and heatmaps to gain insights into your data.
After exploring your data, the next step is data visualization. This involves creating visual representations of your data to communicate your findings effectively. Python libraries like Matplotlib, Seaborn, and Plotly allow you to create interactive and informative visualizations to present your results.
Finally, the last step in the data science process is modeling. This involves building predictive models using machine learning algorithms to make predictions or classify data. Python provides libraries like Scikit-learn and TensorFlow for building and evaluating machine learning models.
By mastering these essential steps in the data science process with Python, you will be well-equipped to analyze data, gain insights, and make informed decisions. Start your data science journey today and unlock the power of Python for data analysis!
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