Data science is just about as broad of a term as they come. It may be easiest to describe what it is by listing its more concrete components:

Data exploration & analysis.

Included here: Pandas; NumPy; SciPy; a helping hand from Python’s Standard Library.

Data visualization. A pretty self-explanatory name. Taking data and turning it into something colorful.

Data science and Python

Classical machine learning. Conceptually, we could define this as any supervised or unsupervised learning task that is not deep learning (see below). Scikit-learn is far-and-away the go-to tool for implementing classification, regression, clustering, and dimensionality reduction, while StatsModels is less actively developed but still has a number of useful features.

Deep learning. This is a subset of machine learning that is seeing a renaissance, and is commonly implemented with Keras, among other libraries. It has seen monumental improvements over the last ~5 years, such as AlexNet in 2012, which was the first design to incorporate consecutive convolutional layers.

Included here: Keras, TensorFlow, and a whole host of others.

Data storage and big data frameworks. Big data is best defined as data that is either literally too large to reside on a single machine, or can’t be processed in the absence of a distributed environment. The Python bindings to Apache technologies play heavily here.

Apache Spark; Apache Hadoop; HDFS; Dask; h5py/pytables.

Odds and ends. Includes subtopics such as natural language processing, and image manipulation with libraries such as OpenCV.

Python for Data Science is a must learn for professionals in the Data Analytics domain. With the growth in the IT industry, there is a booming demand for skilled Data Scientists and Python has evolved as the most preferred programming language. Through this article, you will learn the basics, how to analyze data and then create some beautiful visualizations using Python.

Why Learn Python For Data Science?

Python is no-doubt the best-suited language for a Data Scientist. I have listed down a few points which will help you understand why people go with Python for Data Science:

Python is a free, flexible and powerful open source language

Python cuts development time in half with its simple and easy to read syntax

With Python, you can perform data manipulation, analysis, and visualization

Python provides powerful libraries for Machine learning applications and other scientific computations.

Basics of Python For Data Science

Now is the time when you get your hands dirty in Python programming. But for that, you should have a basic understanding of the following topics:

Variables: Variables refers to the reserved memory locations to store the values. In Python, you don’t need to declare variables before using them or even declare their type.

Data Types: Python supports numerous data types, which defines the operations possible on the variables and the storage method. The list of data types includes – Numeric, Lists, Strings, tuples, Sets and Dictionary.

Operators: Operators helps to manipulate the value of operands. The list of operators in Python includes- Arithmetic, Comparison, Assignment, Logical, Bitwise, Membership, and Identity.

Conditional Statements: Conditional statements helps to execute a set of statements based on a condition. There are namely three conditional statements – If, Elif and Else.

Loops: Loops are used to iterate through small pieces of code. There are three types of loops namely – While, for and nested loops.