|Ease of use||Verbose||Verbose||Simpler, dynamically typed|
|Scalability||Platform depended||Cross Platform||Cross Platform|
|Deployment||Android, web application||Big Data||Data Science, Machine learning|
|Python Web Scraping for Data Science||Web Development||Introduction to the process of web scrapping using python|
|Create a password generator||Internet||To generator password using python code which would tough to guess|
|Impact of pre-paid plans on the preferences of investors||Finance||To find the most impacting factors in preferences of pre-paid model, also identifies which are all the variables highly correlated with impacting factors|
|Machine Learning – Prediction of stock prices||Stock Market||This project focuses on Machine Learning by creating predictive data model to predict future stock prices|
|Server logs/Firewall logs||Security||The process of loading the server logs into the cluster using Flume. It can then be refined using Pig Script, Ambari and HCatlog|
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)
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
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.
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
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
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
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
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 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
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
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
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
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
Project 1 : Analyzing the naming pattern using Python
Industry : General
Problem Statement : How to analyze the trends and most popular baby names
Topics : In this Python project you will work with the United States Social Security Administra4on (SSA) has made available data on the frequency of baby names from 1880 through 2016. The project requires analyzing the data considering different methods. You will visualize the most frequent names, determine the naming trends, and come up with the most popular names for a certain year.
Project 2 : – Python Web Scraping for Data Science
In 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 3 : Predicting customer churn in Telecom Company
Industry – Telecommunications
Problem Statement – How to increase the profitability of a telecom major by reducing the churn rate
Topics :In this project you will work with the telecom company’s customer dataset. This dataset includes subscribing telephone customer’s details. Each of the column has data on phone number, call minutes during various times of the day, the charges incurred, lifetime account duration, whether or not the customer has churned some services by unsubscribing it. The goal is to predict whether a customer will eventually churn or not.
Project 4 : Server logs/Firewall logs
Objective – 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:
This Python online course is designed for clearing the Intellipaat Python Certification Exam. The entire python training course content is designed by industry professionals to get the best jobs in the top MNCs. As part of this online Python course training you will be working on real time projects and assignments that have immense implications in the real world industry scenario thus helping you fast track your career effortlessly.
At the end of this online Python course there will be quizzes that perfectly reflect the type of questions asked in the respective certification exams and helps you score better marks in certification exam.
Intellipaat Course Completion Certification will be awarded on the completion of Project work (on expert review) and upon scoring of at least 60% marks in the quiz. Intellipaat certification is well recognized in top 80+ MNCs like Ericsson, Cisco, Cognizant, Sony, Mu Sigma, Saint-Gobain, Standard Chartered, TCS, Genpact, Hexaware, etc.
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