How to Learn Python from Scratch

If you want to learn programming today, the easiest and most popular language is Python . It is not only easy to learn but you can do many things through it – like web development, data science, artificial intelligence, automation, machine learning, scripting, and much more. In this blog, we will see step-by-step how to learn Python from zero , what resources to use, what projects to work on and how to go from a beginner to an expert. Why learn Python? There are many benefits of learning Python: This is a simple and readable language, which even a beginner can understand easily. Python is open-source i.e. it is available for free. It has many libraries and frameworks like Django, Flask, Pandas, NumPy, TensorFlow etc. The demand for Python is increasing very rapidly in the job sector. Step 1: Start with the Basics If you are a complete beginner, first install Python on your computer. You can download it from the official website – python.org. After installing, you ...
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Top Techniques for Data Cleaning in Data Science Projects

1. Managing Absent Data

Determine the missing values and take appropriate action, such as deleting incomplete records if they are not essential or imputing the mean, median, or mode values.



2. Eliminating Copy

Preserve data integrity by identifying and getting rid of duplicate records, as these can distort the outcomes of analyses.

3. Identifying and Addressing Outliers

To ensure data accuracy, identify outliers using statistical or visual aids and determine whether to eliminate or modify them.


4. Creating Standard Data Formats

Format categorical data, dates, and numbers consistently to maintain consistency throughout the dataset.

5.Fixing Incorrect Data Entry

Automate the process of identifying and fixing typos, incorrect classifications, and other human entry mistakes that can result in inaccurate analysis.


6. Information Conversion

To ensure better model performance, apply scaling or normalization to numerical data, particularly when algorithms are sensitive to the data range.

7. Cleaning Text Data

For consistent processing, eliminate stop words, whitespace, and superfluous punctuation from textual data. Also, standardize text to lowercase.


8. Conversion of Data Types

To enable accurate analysis and computations, convert data types as needed (e.g., from strings to dates or numeric formats).

9. Verifying Data Accuracy

Cross-reference data with established guidelines or reliable data sources to ensure accuracy and applicability.

10. Anonymization of Data

In order to protect privacy and adhere to data protection laws, personally identifiable information (PII) should be deleted or concealed.


In summary

Reliable data science results depend on efficient data cleaning. You can make sure that your dataset is reliable, consistent, and prepared for analysis by putting these strategies into practice.







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