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

## Indexing, slicing, and iterating

Numpy arrays can be indexed, sliced, and iterated like other Python data structures. 





## Broadcasting

Broadcasting is meant to vectorize the array operations. Numpy is written in C language. Broadcasting operations vectorize Numpy arrays, so the traversing operation of arrays occurs in C language which makes it very fast. It also avoids making extra copies of the array, which makes it consume less memory.


Numpy operations, on a pair of arrays, usually happen on an element-by-element basis. For example, suppose we multiply two arrays of the same shape, as seen below. See the results.

## Coding exercise







# Indexing, slicing, and iterating

Numpy arrays can be indexed, sliced, and iterated like other Python data structures. 





# Broadcasting

Broadcasting is meant to vectorize the array operations. Numpy is written in C language. Broadcasting operations vectorize Numpy arrays, so the traversing operation of arrays occurs in C language which makes it very fast. It also avoids making extra copies of the array, which makes it consume less memory.


Numpy operations, on a pair of arrays, usually happen on an element-by-element basis. For example, suppose we multiply two arrays of the same shape, as seen below. See the results.

# Coding exercise







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 ?

Broadcasting in Numpy Arrays

Indexing, slicing, and iterating

Broadcasting