Data science is that the process of deriving knowledge and insights from an enormous and diverse set of knowledge through organizing, processing and analysing the data. It involves many various disciplines like mathematical and statistical modelling, extracting data from it source and applying data visualization techniques. Often it also involves handling big data technologies to collect both structured and unstructured data. Below we’ll see some example scenarios where Data science is employed.

Recommendation systems

As online shopping becomes more prevalent, the e-commerce platforms are ready to capture users shopping preferences also because the performance of varied products within the market. This results in creation of advice systems which create models predicting the patrons needs and show the products the consumer is presumably to shop for.

Financial Risk management

The financial risk involving loans and credits are better analysed by using the purchasers past spend habits, past defaults, other financial commitments and lots of socio-economic indicators. These data are gathered from various sources in several formats. Organizing them together and getting insight into customers profile needs the assistance of knowledge science. the result is minimizing loss for the financial institution by avoiding debt.

Improvement in Health Care services

The health care industry deals with a spread of knowledge which may be classified into technical data, financial data, patient information, drug information and legal rules. All this data got to be analysed during a coordinated manner to supply insights which will save cost both for the health care provider and care receiver while remaining legally compliant.

Computer Vision

The advancement in recognizing a picture by a computer involves processing large sets of image data from multiple objects of same category. for instance , Face recognition. These data sets are modelled, and algorithms are created to use the model to newer images to urge a satisfactory result. Processing of those huge data sets and creation of models need various tools utilized in Data science.

Efficient Management of Energy

As the demand for energy consumption soars, the energy producing companies got to manage the varied phases of the energy production and distribution more efficiently. This involves optimizing the assembly methods, the storage and distribution mechanisms also as studying the purchasers consumption patterns. Linking the data from of these sources and deriving insight seems a frightening task. this is often made easier by using the tools of knowledge science.

Python in Data Science

The programming requirements of knowledge science demands a really versatile yet flexible language which is straightforward to write down the code but can handle highly complex mathematical processing. Python is most fitted to such requirements because it has already established itself both as a language for general computing also as scientific computing. More over it’s being continuously upgraded in sort of new addition to its plethora of libraries aimed toward different programming requirements. Below we’ll discuss such features of python which makes it the well-liked language for data science.

A simple and straightforward to find out language which achieves end in fewer lines of code than other similar languages like R. Its simplicity also makes it robust to handle complex scenarios with minimal code and far less confusion on the overall flow of the program.
It is cross platform, therefore the same code works in multiple environments without having any change. that creates it perfect to be utilized in a multi-environment setup easily.
It executes faster than other similar languages used for data analysis like R and MATLAB.
Its excellent memory management capability, especially garbage pickup makes it versatile in gracefully managing very large volume of knowledge transformation, slicing, dicing and visualization.
Most importantly Python possesses a really large collection of libraries which function special purpose analysis tools. for instance – the NumPy package deals with scientific computing and its array needs much less memory than the traditional python list for managing numeric data. and therefore the number of such packages is continuously growing.
Python has packages which may directly use the code from other languages like Java or C. This helps in optimizing the code performance by using existing code of other languages, whenever it gives a far better result.

Python – Data Science Environment Setup

To successfully create and run the instance code during this tutorial we’ll need an environment found out which can have both general-purpose python also because the special packages required for Data science. we’ll first look as installing the general-purpose python which may be python 2 or python 3. But we’ll prefer python 2 for this tutorial mainly due to its maturity and wider support of external packages.

Getting Python

The most up-to-date and current ASCII text file , binaries, documentation, news, etc., is out there on the official website of Python https://www.python.org/

You can download Python documentation from https://www.python.org/doc/. The documentation is out there in HTML, PDF, and PostScript formats.

Installing Python

Python distribution is out there for a good sort of platforms. you would like to download only the code applicable for your platform and install Python.

If the code for your platform isn’t available, you would like a C compiler to compile the ASCII text file manually. Compiling the ASCII text file offers more flexibility in terms of choice of features that you simply require in your installation.

Here may be a quick overview of putting in Python on various platforms −

Unix and Linux Installation

Here are the straightforward steps to put in Python on Unix/Linux machine.

Open an internet browser and attend https://www.python.org/downloads
Follow the link to download zipped ASCII text file available for Unix/Linux.
Download and extract files.
Editing the Modules/Setup file if you would like to customize some options.
run ./configure script
make
make install

This installs Python at standard location /usr/local/bin and its libraries at /usr/local/lib/pythonXX where XX is that the version of Python.

Windows Installation

Here are the steps to put in Python on Windows machine.

Open an internet browser and attend https://www.python.org/downloads
Follow the link for the Windows installer python-XYZ.msi file where XYZ is that the version you would like to put in .
To use this installer python-XYZ.msi, the Windows system must support Microsoft Installer 2.0. Save the installer file to your local machine then run it to seek out out if your machine supports MSI.
Run the downloaded file. This brings up the Python install wizard, which is basically easy to use. Just accept the default settings, wait until the install is finished, and you’re done.

Macintosh Installation

Recent Macs accompany Python installed, but it’s going to be several years out of date. See http://www.python.org/download/mac/ for instructions on getting the present version along side extra tools to support development on the Mac. For older Mac OS’s before Mac OS X 10.3 (released in 2003), MacPython is out there .

Jack Jansen maintains it and you’ll have full access to the whole documentation at his website − http://www.cwi.nl/~jack/macpython.html you’ll find complete installation details for Mac OS installation.

Setting up PATH

Programs and other executable files are often in many directories, so operating systems provide an enquiry path that lists the directories that the OS searches for executables.

The path is stored in an environment variable, which may be a named string maintained by the OS . This variable contains datarmation available to the command shell and other programs.

The path variable is known as as PATH in Unix or Path in Windows (Unix is case sensitive; Windows is not).

In Mac OS, the installer handles the trail details. To invoke the Python interpreter from any particular directory, you want to add the Python directory to your path.

Setting path at Unix/Linux

To add the Python directory to the trail for a specific session in Unix −

In the csh shell − type setenv PATH “$PATH:/usr/local/bin/python” and press Enter.
In the bash shell (Linux) − type export ATH=”$PATH:/usr/local/bin/python” and press Enter.
In the sh or ksh shell − type PATH=”$PATH:/usr/local/bin/python” and press Enter.
Note − /usr/local/bin/python is that the path of the Python directory

Setting path at Windows

To add the Python directory to the trail for a specific session in Windows −

At the prompt − type path %path%;C:\Python and press Enter.

Note − C:\Python is that the path of the Python directory

Python Environment Variables

Here are important environment variables, which may be recognized by Python –

Sr.No.	Variable & Description
1	PYTHONPATH It has a task almost like PATH. This variable tells the Python interpreter where to locate the module files imported into a program. It should include the Python source library directory and therefore the directories containing Python ASCII text file . PYTHONPATH is usually preset by the Python installer.
2	PYTHONSTARTUP It contains the trail of an initialization file containing Python ASCII text file . it’s executed whenever you begin the interpreter. it’s named as .pythonrc.py in Unix and it contains commands that load utilities or modify PYTHONPATH.
3	PYTHONCASEOK It is utilized in Windows to instruct Python to seek out the primary case-insensitive match in an import statement. Set this variable to any value to activate it.
4	PYTHONHOME It is an alternate module search path. it’s usually embedded within the PYTHONSTARTUP or PYTHONPATH directories to form switching module libraries easy.

Running Python

There are three alternative ways to start out Python −

Interactive Interpreter

You can start Python from Unix, DOS, or the other system that gives you a command-line interpreter or shell window.

Enter python the instruction .

Start coding directly within the interactive interpreter.

$python # Unix/Linux

python% # Unix/Linux

C:> python # Windows/DOS

Here is that the list of all the available instruction options –

1	-d It provides debug output.
2	-O It generates optimized bytecode (resulting in .pyo files).
3	-S Do not run import site to look for Python paths on startup.
4	-v verbose output (detailed trace on import statements).
5	-X disable class-based built-in exceptions (just use strings); obsolete starting with version 1.6.
6	-c cmd run Python script sent in as cmd string
7	file run Python script from given file

Script from the Command-line

A Python script are often executed at instruction by invoking the interpreter on your application, as within the following −

$python script.py # Unix/Linux
or
python% script.py # Unix/Linux
or
C: >python script.py # Windows/DOS

Note − make certain the file permission mode allows execution.

Integrated Development Environment

You can run Python from a Graphical interface (GUI) environment also , if you’ve got a GUI application on your system that supports Python.

Unix − IDLE is that the very first Unix IDE for Python.
Windows − PythonWin is that the first Windows interface for Python and is an IDE with a GUI.
Macintosh − The Macintosh version of Python along side the IDLE IDE is out there from the most website, downloadable as either MacBinary or BinHex’d files.

Installing SciPy Pack

The best thanks to enable the specified packs is to use an installable binary package specific to your OS . These binaries contain full SciPy stack (inclusive of NumPy, SciPy, matplotlib, IPython, SymPy and nose packages along side core Python).

Windows

Anaconda (from www.continuum.io) may be a free Python distribution for SciPy stack. it’s also available for Linux and Mac.

Canopy (www.enthought.com/products/canopy/) is out there as free also as commercial distribution with full SciPy stack for Windows, Linux and Mac.

Python (x,y): it’s a free Python distribution with SciPy stack and Spyder IDE for Windows OS. (Downloadable from www.python-xy.github.io/)

Linux

Package managers of respective Linux distributions are wont to install one or more packages in SciPy stack.

For Ubuntu

sudo apt-get install python-numpy
python-scipy python-matplotlibipythonipythonnotebook python-pandas
python-sympy python-nose

For Fedora

sudo yum install numpyscipy python-matplotlibipython
python-pandas sympy python-nose atlas-devel

Building from Source

Core Python (2.6.x, 2.7.x and 3.2.x onwards) must be installed with distutils and zlib module should be enabled.

GNU gcc (4.2 and above) C compiler must be available.

To install NumPy, run the subsequent command.

Python setup.py install

Let us test whether NumPy module is correctly installed, attempt to import it from Python prompt.

If it’s not installed, the subsequent error message are going to be displayed.

Traceback (most recent call last):

File "", line 1, in

import numpy

ImportError: No module named 'numpy'

So, this brings us to the end of blog. This Tecklearn ‘Python Data Science Introduction’ blog helps you with commonly asked questions if you are looking out for a job in Python Programming. If you wish to learn Python and build a career in Data Science domain, then check out our interactive, Python with Data Science Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

https://www.tecklearn.com/course/python-with-data-science/

Python with Data Science Training

About the Course

Python with Data Science training lets you master the concepts of the widely used and powerful programming language, Python. This Python Course will also help you master important Python programming concepts such as data operations, file operations, object-oriented programming and various Python libraries such as Pandas, NumPy, Matplotlib which are essential for Data Science. You will work on real-world projects in the domain of Python and apply it for various domains of Big Data, Data Science and Machine Learning.

Why Should you take Python with Data Science Training?

Python is the preferred language for new technologies such as Data Science and Machine Learning.
Average salary of Python Certified Developer is $123,656 per annum – Indeed.com
Python is by far the most popular language for data science. Python held 65.6% of the data science market.

What you will Learn in this Course?

Introduction to Python

Define Python
Understand the need for Programming
Know why to choose Python over other languages
Setup Python environment
Understand Various Python concepts – Variables, Data Types Operators, Conditional Statements and Loops
Illustrate String formatting
Understand Command Line Parameters and Flow control

Python Environment Setup and Essentials

Python installation
Windows, Mac & Linux distribution for Anaconda Python
Deploying Python IDE
Basic Python commands, data types, variables, keywords and more

Python language Basic Constructs

Looping in Python
Data Structures: List, Tuple, Dictionary, Set
First Python program
Write a Python Function (with and without parameters)
Create a member function and a variable
Tuple
Dictionary
Set and Frozen Set
Lambda function

OOP (Object Oriented Programming) in Python

Object-Oriented Concepts

Working with Modules, Handling Exceptions and File Handling

Standard Libraries
Modules Used in Python (OS, Sys, Date and Time etc.)
The Import statements
Module search path
Package installation ways
Errors and Exception Handling
Handling multiple exceptions

Introduction to NumPy

Introduction to arrays and matrices
Indexing of array, datatypes, broadcasting of array math
Standard deviation, Conditional probability
Correlation and covariance
NumPy Exercise Solution

Introduction to Pandas

Pandas for data analysis and machine learning
Pandas for data analysis and machine learning Continued
Time series analysis
Linear regression
Logistic Regression
ROC Curve
Neural Network Implementation
K Means Clustering Method

Data Visualisation

Matplotlib library
Grids, axes, plots
Markers, colours, fonts and styling
Types of plots – bar graphs, pie charts, histograms
Contour plots

Data Manipulation

Perform function manipulations on Data objects
Perform Concatenation, Merging and Joining on DataFrames
Iterate through DataFrames
Explore Datasets and extract insights from it

Scikit-Learn for Natural Language Processing

What is natural language processing, working with NLP on text data
Scikit-Learn for Natural Language Processing
The Scikit-Learn machine learning algorithms
Sentimental Analysis – Twitter

Introduction to Python for Hadoop

Deploying Python coding for MapReduce jobs on Hadoop framework.
Python for Apache Spark coding
Deploying Spark code with Python
Machine learning library of Spark MLlib
Deploying Spark MLlib for Classification, Clustering and Regression

Got a question for us? Please mention it in the comments section and we will get back to you.

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