Now, you have decided to learn Data Science using Python, but you don’t have any basic programming knowledge. So don’t be confused from where to start, and how to learn Python for becoming an expert. In this article, you can learn Data Science Python techniques from scratch. Codekul is one of the best training institutes in Pune. You are at right place to join data science course. Before that, These are some of the common questions for every beginner has while getting started with Python.

             “How much time to learn Python??”

             “Which is the best training institute to learn Python??”

It is very confusing time where we can’t take a right decision. But here we clear all basic concepts and you can easily learn python for data science as one of the best training institutes in Pune at Codekul.

Basic of Python for Data Science:

  • What is Python??

Python is a general-use high-level, interpreted, object-oriented, powerful, fast, friendly, open source, and easy to learn a programming language. Python “plays well with others” and “runs everywhere”.  It is simple, quickly understand, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance. Another thing is that Python supports modules and packages, which encourages program modularity and code reuse. Simplicity is one of Python’s greatest strengths. How it is ?? Let us we see the example of “Hello World..” in Python and Java language.

In Python:

               #!/usr/bin/python

              print “Hello, World!”

In Java:

            public class Main {

                          public static void main(String[] args) {

                         System.out.println(“Hello, world!”);

    }

}

In above example, both languages are showing the same output but Python can accomplish the same tasks with less code than other languages.

Python Installation On Windows:

  • First Open a Web browser and go to https://www.python.org/downloads/.
  • Check the latest version and then Click the Download Python 3.6.2 button.
  • Move this file to a more permanent location, so that you can install Python.
  • If you want to immediately installation then close the web browser.
  • Then go to the python-3.6.2.exe file where it is located and Double-click the icon labeling the file python-3.6.2.exe.

  • Click Run.

            You will see Python 3.6.2 (32-bit) Setup pop-up window.

Then click on both check box option Install launcher for all users (recommended) and the Add Python 3.6 to PATH.

  • Then seems User Account Control pop-up window, in that ask the question Do you want the allow the following program to make changes to this computer?

  • Click the Yes button.

           After that you will see new Python 3.6.2 (32-bit) Setup pop-up window.

  • After the completion of setup process, window will appear with a Setup was successfully message.

  • Click on Close Button.

What is Pandas Python?

Pandas is an open-source Python Library which provides high – performance, data structures and data analysis tools for the Python. The panda’s name arrives from Panel Data. Its library is used for data manipulation and analysis. Pandas Python is most of the used in a commercial and academic area including finance, statistics, analytics, economics etc. In pandas there are five process steps of data – load, organize, manipulate, model, and analyze the data.

Key Features of Pandas:-

  • Easily insert and delete columns in the data structure.
  • The fast DataFrame object with default and customized indexing.
  • Date sets reshaping and pivoting.
  • Time Series functionality.
  • High performance joining and merging of data.

What is NumPy in Python??

NumPy is stands for “Numeric Python” or “Numerical Python”. It is Python package and an open source extension module. It provides fast precompiled functions for mathematical and numerical routines. Fourier transforms and routines for shape manipulation, Mathematical and logical operations on arrays, linear algebra operations etc. operations are used in NumPy.

What is SciPy??

SciPy stands for Scientific Python. It is a set of open source scientific and numerical tools for Python. SciPy is used to supports special functions, gradient optimization, integration, parallel programming tools, an expression-to – C++ compiler for fast execution etc.

What is Matplotlib??

Matplotlib is a Python 2D plotting library. This plotting library is used to provides a pylot module which makes easy for plotting points features to control fonts parameters and properties, line styles etc.  Normally it working on wide variety of graphs and plots such as histogram, bar charts, power spectra, error charts etc. It is an emerging technology which deals with identification, analysis, representation, and extraction of meaningful information from data sources.

What is Data Science?

Data is stored in two type – unstructured and structured data. Data Science includes statistics, mathematics, programming, problem-solving, technology-hacking, strong business acumen, capturing data in ingenious ways, the ability to look at things differently etc. It is the activity of cleansing, preparing and analysis the data.

Data Science Process:-

Bellow we show the basic sample Data Science Process diagram and explain each step.

Step 1: Ask an Interesting Questions

  • What are the scientific goal?
  • What do you want to predict and estimates?
  • Ask the questions regarding to discovery and right goal identification.

Step 2: Get the data

  • In this steps skills are web scraping, data processing, data cleaning, querying databases, CS stuff etc.
  • Tools: Python, pandas etc.

Step 3: EXPLORE the data

  • Storing the incoming data in a way that will allow further modeling and reporting.
  • Initial exploratory data analysis (EDA).
  • To Join a relevant and logical data from multiple sources.
  • Tools: matplotlib, numpy, scipy, pandas.

Step 4: Model the data

  • Build data model
  • Perform the necessary statistical analyses.
  • Machine-learning or recursive analysis.
  • Validate data model.
  • Done regression testing and classical statistical analyze techniques.
  • Measuring and improving results.
  • Tools: scikits learn, pandas, mrjob, MapReduce.

Step 5: Communicate the Data

  • Presentation (Information in Chart and Graph).
  • Speak, Deliver, and write actual result.
  • Repeat the process to solve a new problem.
  • Tool: PowerPoint, adobe illustrator.

Hopefully, this article has helped to who want to make a career in data science and other related roles. You will a lot to learn data science with python. Stay in touch with {Code}kul; for daily updates!!!!!