About Python Big Data:

Python is an interactive high-level programming language used in data science. It is an open source programme and very easy to install. It is simple to learn and it is a common language for web-based analytics products and analytics. Python is an interpreted language and a general-purpose language, with a strong community support from The Python Software Foundation which has the Intellectual property rights of Python programming.

In the current era of big data analytics, python is getting more popular due to its easy-to-use features which supports big data processing. Another main reason for its popularity is its rich set of utilities and libraries for data processing and analytics. Python can make scalable applications. All these features make Python popular in Big Data.

Our training program will help you to gain expertise in basic and advanced features and concepts of Python programming and take a deep dive into big data analytics.

Course overview:

Our training course in Python will help you to understand the popularly used programming language for Data Science. In this Python programming training, you will be exposed to both the basic and advanced concepts of Python like machine learning, Deep Learning, Hadoop streaming, Map Reduce in Python, and work with packages like Scikit and Scipy. Even after you complete the course, we offer lifetime access to an upgraded latest version of videos, course materials, 24/7 Support free of cost.

Upon completion of the training course you can apply for some of the best jobs in top MNCs around the world at top salaries.

Our training course is useful for:

• Big data analytics professionals
• ETL and software professionals
• BI managers and Project Managers

Pre-requisites:

To learn this course, a basic programming language knowledge is necessary. A knowledge in C, or C++ or Java programming is desired.

Course content:

Introduction to Python:

• Python Language and features
• Python and how it is different from other languages
• Installation of Python
• Anaconda Python distribution for Windows, Mac, and Linux. Run a sample python script, working with Python IDE’s. Running basic python commands – Data types, Variables, Keywords, etc

Hands-on Exercise – Install Anaconda Python distribution for your OS (Windows/Linux/Mac)

Basic constructs of Python language:

• Indentation (Tabs and Spaces) and Code Comments (Pound # character);
• Variables and Names
• Built-in Data Types in Python – Numeric: int, float, complex – Containers: list, tuple, set, dict – Text Sequence: Str (String) Others
• Modules, Classes, Instances, Exceptions, Null Object, Ellipsis Object – Constants: False, True, None, Not Implemented, Ellipsis, debug
• Basic Operators: Arithmetic, Comparison, Assignment, Logical, Bitwise, Membership, Identity
• Slicing and The Slice Operator [n:m]
• Control and Loop Statements: if, for, while, range(), break, continue, else;

Hands-on Exercise – Write your first Python program Write a Python Function (with and without parameters) Use Lambda expression Write a class, create a member function and a variable, Create an object Write a for loop to print all odd numbers

Writing Object Oriented Program in Python and connecting with Database:

• Classes – classes and objects,
• Access modifiers, instance and class members OOPS paradigm
• Inheritance,
• Polymorphism and Encapsulation in Python.
• Functions: Parameters and Return Types; Lambda Expressions, Making connection with Database for pulling data.

Python Object and Data Structure:

• Numbers
• Strings
• Print Formatting
• List
• Dictionaries
• Tuples
• Sets and Booleans 

File Handling in Python:

• Open a File, Read from a File, Write into a File
• Resetting the current position in a File; The Pickle (Serialize and Deserialize Python Objects)

Error handling:

• Errors and Exceptions 
• Exception Handling: try, except, finally 
• Errors and Exceptions Homework 
• Errors and Exceptions - Solutions 
• The Shelve (Overcome the limitation of Pickle)
• What is an Exception; Raising an Exception; Catching an Exception

Hands-on Exercise – Open a text file and read the contents, Write a new line in the opened file, Use pickle to serialize a python object, deserialize the object, Raise an exception and catch it

Mathematical Computing with Python (NumPy):

• Arrays and Matrices,
• ND-array object, Array indexing,
• Datatypes, Array math Broadcasting, Std Deviation, Conditional Prob, Covariance and Correlation.

Hands-on Exercise – Import numpy module, create an array using ND-array, Calculate std deviation on an array of numbers, Calculate correlation between two variables

Scientific Computing with Python (SciPy):

• Builds on top of NumPy,
• SciPy and its characteristics,
• Subpackages: cluster, fftpack, linalg, signal, integrate, optimize, stats; Bayes Theorem using SciPy

Hands-on Exercise – Import SciPy, Apply Bayes theorem using SciPy on the given dataset

Data Visualization (Matplotlib):

• Plotting Grapsh and Charts (Line, Pie, Bar, Scatter, Histogram, 3-D); Subplots; The Matplotlib API

Hands-on Exercise – Plot Line, Pie, Scatter, Histogram and other charts using Matplotlib

Data Analysis and Machine Learning (Pandas) OR Data Manipulation with Python:

• Dataframes, NumPy array to a data frame
• Import Data (csv, json, excel, SQL database)
• Data operations: View, Select, Filter, Sort, Group by, Cleaning, Join/Combine, handling Missing Values
• Introduction to Machine Learning (ML)
• Linear Regression; Time Series

Hands-on Exercise – Import Pandas, Use it to import data from a json file,,Select records by a group and apply filter on top of that, View the records, Perform Linear Regression analysis, Create a Time Series

Natural Language Processing, Machine Learning (Scikit-Learn):

• Introduction to Natural Language Processing (NLP)
• NLP approach for Text Data; Environment Setup (Jupyter Notebook)
• Sentence Analysis
• ML Algorithms in Scikit-Learn
• What is Bag of Words Model; Feature Extraction from Text
• Model Training; Search Grid; Multiple Parameters; Build a Pipeline

Hands-on Exercise – Setup Jupyter Notebook environment, Load a dataset in Jupyter, Use algorithm in Scikit-Learn package to perform ML techniques, Train a model Create a search grid

Web Scraping for Data Science:

• What is Web Scraping
• Web Scraping Libraries (Beautiful soup, Scrapy)
• Installation of Beautiful soup
• Install lxml Python Parser
• Making a Soup Object using an input html
• Navigating Py Objects in the Soup Tree
• Searching the Tree
• Output Print
• Parsing Full or partial.

Hands-on Exercise – Install Beautifulsoup and lxml Python parser, Make a Soup object using an input html file, Navigate Py objects in the soup tree, Search tree, print output

Python on Hadoop:

• Understanding Hadoop and its various components
• Hadoop ecosystem and Hadoop common
• HDFS and MapReduce Architecture
• Python scripting for MapReduce Jobs on Hadoop framework

Hands-on Exercise – Write a basic MapReduce Job in Python and connect with Hadoop Framework to perform the task

Writing Spark code using Python:

• What is Spark, understanding RDDs
• Spark Libs
• Writing Spark code using python
• Spark Machine Libraries MLlib,
• Regression
• Classification and Clustering using Spark MLlib

Hands-on Exercise – Implement sandbox, Run a python code in sandbox, Work with HDFS file system from sandbox

Certification:

Our training course is designed to complete the Python certification exam. We provide real-time case studies and projects to facilitate an easy transition into the Big Data- Python programming world.

Our certified Python professionals are placed in many Top MNC companies and they are drawing high salaries.

Job and Placement:

Python is a highly popular object-oriented language that is easy to learn and ready to deploy. It can run on various systems like Windows, Linux and Mac thus make it highly coveted for the data analytics domain. Our certified Python professionals work in the Big Data Hadoop environment for very high salaries. Python’s design & libraries provide 10 times productivity compared to C, C++, or Java. A python professional has a lot of demand in Big data analytics domain.

A Senior Python Developer in the United States can earn $102,000 – indeed.com