 ## What is Clustering & its Types? K-Means Clustering Example (Python)

Cluster Analysis Cluster is a group of data objects that are similar to one another within the same cluster, whereas, dissimilar to the objects in the other clusters. Cluster analysis is a technique used to classify the data objects into relative groups called clusters. Clustering is an unsupervised learning approach in which there are no … ## Logistic Regression (Python) Explained using Practical Example

Logistic Regression is a predictive analysis which is used to explain the data and relationship between one dependent binary variable and one or more nominal, ordinal, interval or ratio-level independent variables. It is mostly used in biological sciences and social science applications. For instance, predict whether received email is spam or not. Similarly, predict whether … ## Simple and Multiple Linear Regression in Python

Generally, Linear Regression is used for predictive analysis. It is a linear approximation of a fundamental relationship between two or more variables. Main processes of linear regression Get sample data Design a model that works best for that sample Make prediction for the whole population Main uses of regression analysis Finding the strength of predictors … ## Basics of Python for Data Science

Introduction to Python Python is an open-source, high-level and general-purpose programming language which was created by Guido Van Rossum and released in 1991. Python 3.7 was released on 27.06.2018. It is very powerful, fast, friendly and easy to learn programming language. From here to onward we will use Python 3. Jupyter The Jupyter Notebook is … ## How to install Anaconda (Python Distribution) on Windows

Anaconda Installation and setup guide for Windows. You will learn how to install Python on windows, step by step. Anaconda is most widely used open source distribution to perform Python/R for data science and machine learning. Data Scientists can easily analyze the data and visualize the results by using Python/R. Anaconda Download Link How to … ## Compare Data Science and Machine Learning (5 Key Differences)

Even after years of schooling, there are most common confusion that some students still face is the basic variance between Data Science and Machine Learning while they are for the most part firmly interconnected but each has a separate determination. Numerous companies are being utilized these technologies due to their gigantic dimensions and progressively hoping … ## 42 Must Know Data Science Interview Questions and Answers

To get the Data Scientist job, you must have grip on practical as well as theoretical knowledge of data science. You should be fully prepared before going through interview. For your convenience, we have gathered 42 data science interview questions and their answers. These will enable you grab the basic concepts of data science. Further … ## Statistics for Data Science (Descriptive & Inferential Statistics)

Today we are going to discuss statistics for data science. Statistics plays a vital role in the life of a Data Scientist. It is one of the must-have skills for the Data Scientist. For example, descriptive statistics is required in data analysis. Here we are going to present the basic concepts of the main branches … ## Different Types of Probability Distribution (Characteristics & Examples)

What is distribution? A distribution represent the possible values a random variable can take and how often they occur. Mean – it represent the average value which is denoted by µ (Meu) and measured in seconds. Variance – it represent how spread out the data is, denoted by σ2 (Sigma Square). It is pertinent to … ## Probability for Data Science

Probability is simply defined as a chance of something happening or the likelihood of an event is to happen. Probability played a vital role in decision making that’s why companies’ executives based on it before going to take any decision prior to investment. Probability for Data Science An event can be likely or unlikely and …