Gain insights into data science fundamentals, explore machine learning and big data, and delve into real-time projects. Discover systematic approaches to data acquisition, wrangling, and solving diverse problems.

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There is a lot of dispersed, and somewhat conflicting information on the internet when it comes to data science, making it tough to know where to start. Don't worry. 

This course will get you familiar with the state of data science and the related fields such as machine learning and big data. You will be going through the fundamental concepts and libraries which are essential to solve any problem in this field. 

You will work on real-time projects from Kaggle while also honing your mathematical skills which will be used extensively in most problems you face. You will also be taken through a systematic approach to learning about data acquisition to data wrangling and everything in between. This is your all-in-one guide to becoming a confident data scientist.

An Introductory Guide to Data Science and Machine Learning

# Sampling methods

The sampling methods help us to choose a subset of the population in a good manner which helps us to draw inference about the whole population using a sample. It is widely used in Data Science to choose a good representation of the data. Knowledge of the sampling methods is critical in building descriptive and predictive systems. In this lesson, we will be looking at the commonly used sampling methods in detail.


## Random Sampling

As the name suggests we randomly choose elements for our sample from the population. The sample size is defined at the beginning. Each of the data elements from the population has an equal probability of being selected. Each of the one-hundred sheep, being selected for a sample size of thirty sheep, has an equal probability of $\frac{1}{100}$ for being selected.

Random sampling is one of the most simple methods of sampling out there.


Sampling methods play an important role in sampling the dataset for analysis and modelling and you'll learn about them in this lesson. 


What is Data Science ?

Applications of Data Science

Overview of Libraries

Probability and Statistics

Machine Learning Part-1

Machine Learning Part-2

Machine Learning Part-3

Deep Learning

Machine Learning Tools and Libraries

Big Data Tools and Technologies

Where to go next ?

Sampling Methods

Sampling methods