Best Python Training in Bangalore /marathahalli /sarjapur/ Bellandur/ Malleshwaram/Jayanagar/Indiranagar/Cunningham
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About Python Course

“Python is a clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java. Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. ”

This System domain Python training course is a complete course that will help you to clearly understand the programming language that is exclusively used for Data Science. In this Python programming training, you will be exposed to both the basic and advanced concepts of Python.

What will you learn in this Python Online Training Course?

  1. Learn the basics, significance, and installation of Python
  2. Learn about file and sequence operations
  3. Learn about MapReduce concepts for Hadoop deployment
  4. Study OOP, expressions, data types, looping, etc.
  5. Understand SQLite in Python, functions, operations, and class defining
  6. Use Python for writing and deploying Pig UDF and Hive UDF
  7. Get to know the Machine Learning Algorithms in Python
  8. Work on a real-life Hadoop project running on Python
  9. Course Completion Certificate from System domain
  10. Be equipped for the Python Professional Certification

Who should take this Python Scripting Training Course?

  • BI Managers and Project Managers
  • Software Developers and ETL professionals
  • Analytics professionals
  • Big Data professionals
  • Those wanting to have a career in Python

What are the prerequisites for learning Python?

You don’t need any specific knowledge to learn Python. A basic knowledge of programming can help.

Why should you take this Python Certification Training?

  • Python’s design & libraries provide 10 times productivity compared to C, C++, or Java
  • A Senior Python Developer in India can earn 24Lacs per annum

Python is a highly popular object-oriented language that is fast to learn and easy to deploy. It can run on various systems like Windows, Linux and Mac thus make it highly coveted for the data analytics domain. Upon completion of training, you can work in the Big Data Hadoop environment for very high salaries


Python Course Content

Introduction to Python

What is Python Language and features, Why Python and why it is different from other languages, Installation of Python, Anaconda Python distribution for Windows, Mac, 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, NotImplemented, Ellipsis, __debug__; Basic Operators: Arithmetic, Comparison, Assignment, Logical, Bitwise, Membership, Indentity; 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 a connection with Database for pulling data.


File Handling, Exception 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); 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, sub packages: 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 Graphs 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

Data frames, NumPy array to a data frame; Import Data (csv, json, excel, SQL database); Data operations: View, Select, Filter, Sort, Groupby, 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 (Beautifulsoup, Scrapy); Installation of Beautifulsoup; 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 Mlib, 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