Data Cleaning 101. Data quality is crucial aspect and centre of attraction for any data science project.
Data cleaning is a process to remove, add or modify data for analyzing and other machine learning tasks. If data cleaning is necessary, it is always done before any kind of analysis or machine learning task.
Clive Humby said, “Data is the new oil.” But we know data still needs to be refined.
Data is considered one of the major assets of a company. Misleading or inaccurate data is risky and can be a reason for the fall of a company.
It is not necessary that data available to us is useful every-time, we must perform many operations to make it useful. So, it is a good idea to remove unnecessary data and, format and modify important data so that we can use it. In some scenarios, it is also required to add information externally by processing the available data. For example, adding a language column based on some data already exist or to generate a column with average value based on some other columns’ data.
There are many steps involved in data cleaning process. These all steps are not necessary for everyone to follow or use. To perform the data cleaning, we will use python programming language with *pandas *library.
I have used python because of its expressiveness and, it is easy to learn and understand. More importantly, python is choice of many experts for machine learning tasks because person without computer science background can easily learn it. Apart from python’s benefits; pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool and it is one of the most popular data analysis and processing tools out there.
To know your data is very important before one start data cleaning process, because what cleaning process to perform, is all depends on what kind of data one has and what is the nature of that data.
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A data scientist/analyst in the making needs to format and clean data before being able to perform any kind of exploratory data analysis.
Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.
This Data Science with Python Tutorial will help you understand what is Data Science, basics of Python for data analysis, why learn Python, how to install Python, Python libraries for data analysis, exploratory analysis using Pandas, introduction to series and dataframe, loan prediction problem, data wrangling using Pandas, building a predictive model using Scikit-Learn and implementing logistic regression model using Python.