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Python Training

Course Summary

Enroll now for Intellipaat Python Online Training and master the concepts of widely-used and powerful programming language. Learn Python Libraries SciPy, NumPy, Lambda Function, Python on Hadoop,Python Web Scraping by working on real world industry projects.

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    Course Syllabus

    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 Pyhton IDE’s. Running basic python commands – Data types, Variables,Keywords,etcHands-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
    Wrting 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.
    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, subpackages: cluster, fftpack, linalg, signal, integrate, optimize, stats; Bayes Theorem using SciPyHands-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 APIHands-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 dataframe; 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 SeriesHands-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 PipelineHands-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 PartialHands-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 frameworkHands-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 MLlibHands-on Exercise – Implement sandbox, Run a python code in sandbox, Work with HDFS file system from sandbox
    Python Projects
    Project 1: – Python Web Scraping for Data ScienceIn this project you will be introduced to the process of web scraping using Python. It involves installation of Beautiful Soup, web scraping libraries, working on common data and page format on the web, learning the important kinds of objects, Navigable String, deploying the searching tree, navigation options, parser, search tree, searching by CSS class, list, function and keyword argument.
    Project 2 :-Create a password generatorObjective – To generate a password using Python code which would be tough to guessRequirements :
    • To generate a password that is 8-12 characters long
    • Password contains at least two special characters
    • Password doesn’t start with a special character
    Project 3 :– Impact of pre-paid plans on the preferences of investorsDomain – FinanceObjective – The project aims to find the most impacting factors in preferences of pre-paid model, also identifies which are all the variables highly correlated with impacting factorsRequirements :
    • To identify the various reasons for Pre-paid model preference and non-preference among the investors. And also understand the penetration of the Pre-paid model in the brokerage firms
    • To identify the Pre-paid scheme advantages and disadvantages and also identify brand wise market share
    • In addition to this, the project also looks to identify various insights that would help a newly established brand to foray deeper into the market on a large scale
    Project 4 :– Machine Learning – Prediction of stock pricesDomain – Stock MarketObjective – This project focuses on Machine Learning by creating predictive data model to predict future stock pricesRequirements :
    • Quatitative Value Investing: Predict 6-month price movements based fundamental indicators from companies’ quarterly reports
    • Forecasting: Build time series models on the delta between implied and actual volatility
    • Predict 6-month price movements based fundamental indicators from companies’ quarterly reports
    • Build time series models on the delta between implied and actual volatility?
    Project 5 : Server logs/Firewall logsObjective – This includes the process of loading the server logs into the cluster using Flume. It can then be refined using Pig Script, Ambari and HCatlog. You can then visualize it using elastic search and excel.This project task includes:
    • Server logs
    • Potential uses of server log data
    • Pig script
    • Firewall logs
    • Work flow editor

Course Fee:
USD 120

Course Type:


Course Status:



1 - 4 hours / week

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