Learn the basics of data science and how to work with important libraries such as NumPy, pandas, Beautiful Soup, etc.

Become a Data Scientist

This module will get us familiar with the state of data science and its fundamental concepts. We'll be going through the applications of data science and a few libraries which are essential to solve any problem in this field, such as Beautiful Soup, Scrapy, NumPy, pandas, spaCy, etc.
We'll also learn about data manipulation with NumPy as well as probability and statistics.
By the end of this module, we'll have basic and essential knowledge of data science and its libraries.

Getting Started with Data Science

## 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

Data Manipulation with NumPy

Data Analysis with pandas

Probability and Statistics

Conclusion

Broadcasting in Numpy Arrays

Indexing, slicing, and iterating

Broadcasting