data science - Nomidl

Explanation for AI and Data Science by ChatGPT AI

Question to ChatGPT: Explain AI Artificial intelligence (AI) is the ability of a computer program or a machine to simulate human intelligence, including the ability to reason, learn, and solve problems. AI can be applied to a wide range of field, including robotics, natural language processing, computer vision, and machine learning. The goal of AI…

3 Concepts Every Data Scientist Must Know Part – 3

1. What is the significance of sampling? Name some techniques for sampling? For analyzing the data, we cannot proceed with the whole volume at once for large datasets. We need to take some samples from the data which can represent the whole population. While making a sample out of complete data, we should take the…

3 Concepts Every Data Scientist Must Know Part – 2

1. Bagging and Boosting Bagging and Boosting are two different ways used in combining base estimators for ensemble learning (Like random forest combining decision trees). Bagging means aggregating the predictions of several weak learners. We can think of it combining weak learners is used in parallel. The average of the predictions of several weak learners…

NumPy for Data Science – Part 5

The difference between copy and view Copy The copy owns the data The copy of an array is a new array. The changes made in the copy data does not reflect in the original array. View The view does not own the data A view of the original array. Any changes made in the view…

NumPy for Data Science – Part 4

Broadcasting NumPy Arrays The term broadcasting describes how NumPy treats arrays with different shapes during arithmetic operations. Subject to certain constraints, the smaller array is “broadcast” across the larger array in order that they have compatible shapes. NumPy operations are usually done on pairs of arrays on an element-by-element basis. Within the simplest case, the…

NumPy for Data Science – Part 3

Arithmetic Operations in NumPy Arrays In NumPy there are multiple functions which we can use to perform the arithmetic operation, we will be looking them one by one. The add() function can also be used to perform the same operation. The subtract() function can also be used to perform the same operation. The multiply() function…

NumPy for Data Science – Part 2

Create NumPy Arrays with Random Numbers rand() – the function is used to generate value between 0 to 1. randn() – the function is used to generate value close to zero. This may return positive or negative numbers as well. ranf() – the function for doing random sampling in NumPy. It returns an Array of…

NumPy for Data Science – Part 1

What is NumPy Array? An array is a grid of values and it contains information about the raw data, how to locate an element, and how to interpret an element. Numpy vs Python List Advantages of using NumPy Arrays over Python List: Consumes less memory. Fast as compared to the Python List. Convenient to use.…

Difference between Data Science and Machine Learning

Data Science Data science is a field that studies data and how to extract meaning from it, using a series of methods, algorithms, systems, and tools to extract insights from structured and unstructured and unstructured data. That knowledge then gets applied to business, government, and other bodies to help drive profits, innovate products and services…

Difference between Big Data and Data Science

Big Data Hugh volumes of data which cannot be handled using traditional database programming. Characterized by volume, variety, and velocity. Data Science A data-focused on scientific activity. Approaches to process big data. Harnesses the potential of big data for business decisions. Similar to data mining. Concept Big Data Diverse data types generated from multiple data…