What is the role of Python in data science?

Expert Digi asked 4 months ago

Python is a major part of the ExcelR Data Science Training in Pune and data science institutes follow and teach Python as a major topic. Python is basically a programming language, which allows its users to work and program more efficiently and effortlessly. Python is very simple to understand and learn and is one of the most popular and widely used programming languages and has replaced many programming languages in the industry.
There are a lot of reasons why Python is widespread among developers, Data Scientists and one of them is that it has an incredibly enormous assembly of libraries that operators can work with.
Here are a few significant causes as to why Python is common:
• Python has a huge collection of libraries.
• Python is known as the beginner’s level programming language because of it uncomplicatedness.
• Movability is another reason for the huge popularity of Python.
• Python’s programming syntax is simple to learn and is of high level compared to C, Java, and C++.
If you are presently occupied with a machine learning project in Python, then you may have caught a popular open-source library known as TensorFlow.
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2 Answers
Success answered 3 months ago

Qualitative data: you may have vanilla, strawberry, and butterscotch, etc. Here we are talking about the description and describing the ice cream that’s what called qualitative data. It is descriptive in nature and attributes that can observe but cannot be measured. Quantitative data: something which we can measure numeric and is measurable can be thought as quantitative data it captures the measure of the attribute. Example: height of student cost and age. Basic of data categorization There are multiple types of data categorizing the data, one of them is based on the type of the data and the other one is how we process, how do we form it based on the source generation of the data, based on the size of the data we categorize. TYPES OF DATA 1. Categorical variable: binary, nominal, ordinal 2. Quantitative random variable: discreet and continuous · A categorical variable is similar to the qualitative Binary data: it has only two possible states · 0-1 Toss the coin · Switch on or switch off These are the examples of binary data; generally, it looks at two options true or false like that Nominal data: it is categorical data it means something which we can categorize. If we take a library group of books is categorized as humor, history, art, etc… things like that. That’s what meant by categorical data. It is coded we are giving code and referring that bunch of books as humor and things like that. That’s what meant by code. You can only count but cannot measure nominal dataexample: names of cars , married or unmarried, book titles in the library. Ordinal data: · Data is ordered · It has a natural hierarchy · The intervals between the ranks may not be necessarily equal(distance between groups can be different) Example: customer satisfaction score and medal tally Discreet data and continuous data Discreet data: · Numerical data · Finite number of possible valuesExample: number of people in a room, number of items in a basket, number hours in a day Continuous data: · It is also a numerical data · Infinite number of possible values · Usually is in decimalsExamples: height, weight, sales, account balance. For More information about data Science Course in Pune note the below details- ExcelR – Data Science, Data Analytics Course Training in Pune Address- ExcelR Solutions – Pune102, 1st floor, Phase II, Prachi Residency,Baner Road, Baner, Pune, Maharashtra 411045 E-mail- surya@excelr.com Phone Number- +91 98809 13504Hour: Mon- Sat 07AM – 11PMEstablished in Year: 2013Category- Training & Education https://g.page/ExcelRPuneDataSciencecourses?sharehttps://www.excelr.com/data-science-course-training-in-pune/

sheeba answered 3 months ago

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