|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 data science, what does a data scientist do, the various examples of data science in the industries, how Python is deployed for data science applications.
What is data analysis, the various steps in data analysis process like data wrangling, data exploration and selecting the model, understanding data visualization, what is exploratory data analysis, building of hypothesis, plotting and other techniques.
Introduction to Python Language, features, the advantages of Python over other programming languages, Python installation, Windows, Mac & Linux distribution for Anaconda Python, deploying Python IDE, basic Python commands, data types, variables, keywords and more.
Hands-on Exercise – Installing Python Anaconda for the Windows, Linux and Mac.
Built-in data types in Python, tabs and spaces indentation, code comment Pound # character, variables and names, Python built-in data types, Numeric, int, float, complex, list tuple, set dict, containers, text sequence, exceptions, instances, classes, modules, Str(String), Ellipsis Object, Null Object, Ellipsis, Debug, basic operators, comparison, arithmetic, slicing and slice operator, logical, bitwise, loop and control statements, while, for, if, break, else, continue.
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
How to write OOP program in Python, connecting to a database, classes and objects in Python, OOPs paradigm, important concepts in OOP like polymorphism, inheritance, encapsulation, Python functions, return types, and parameters, Lambda expressions, connecting to database and pulling the data.
Introduction to arrays and matrices, indexing of array, datatypes, broadcasting of array math, standard deviation, conditional probability, coorelation and covariance.
Hands-on Exercise – How to import NumPy module, creating aray using ND-array, calculating standard deviation on array of numbers, calculating correlation between two variables.
Introduction to SciPy and its functions, building on top of NumPy, cluster, linalg, signal, optimize, integrate, subpackages, SciPy with Bayes Theorem.
Hands-on Exercise – Importing of SciPy, applying the Bayes theorem on the given dataset.
How to plot graph and chart with Python, various aspects of line, scatter, bar, histogram, 3D, the API of MatPlotLib, subplots.
Hands-on Exercise – deploying MatPlotLib for creating Pie, Scatter, Line, Histogram.
Introduction to Python dataframes, importing data from JSON, CSV, Excel, SQL database, NumPy array to dataframe, various data operations like selecting, filtering, sorting, viewing, joining, combining, how to handle missing values, time series analysis, linear regression.
Hands-on Exercise – working on importing data from JSON files, selecting record by a group, applying filter on top, viewing records, analyzing with linear regression, and creation of time series.
What is natural language processing, working with NLP on text data, setting up the environment using Jupyter Notebook, analyzing sentence, the Scikit-Learn machine learning algorithms, bags of words model, extracting feature from text, searching a grid, model training, multiple parameters, building of a pipeline.
Hands-on Exercise – setting up the Jupyter notebook environment, loading of a dataset in Jupyter, algorithms in Scikit-Learn package for performing machine learning techniques, training a model to search a grid.
Introduction to web scraping in Python, the various web scraping libraries, beautifulsoup, Scrapy Python packages, installing of beautifulsoup, installing Python parser lxml, creating soup object with input HTML, searching of tree, full or partial parsing, output print, searching the tree.
Hands-on Exercise – Installation of Beautiful soup and lxml Python parser, making a soup object with input HTML file, navigating using Py objects in soup tree.
Introduction to Python for Hadoop, the basics of the Hadoop ecosystem, Hadoop common, the architecture of MapReduce and HDFS, deploying Python coding for MapReduce jobs on Hadoop framework.
Hands-on Exercise – How to write a MapReduce job with Python, connecting to the Hadoop framework and performing the tasks.
Introduction to Apache Spark, importance of RDD, the Spark libraries, deploying Spark code with Python, the machine learning library of Spark MLlib, deploying Spark MLlib for classification, clustering and regression.
Hands-on Exercise – How to implement Python in a sandbox, working with the HDFS file system.
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 Intellipaat Python training will give you hands-on experience in mastering the one of the best programming languages that is Python. In this online Python course you will learn about the basic and advanced concepts of Python including MapReduce in Python, machine learning, Hadoop streaming and also Python packages like Scikit and Scipy. You will be awarded the Intellipaat Course Completion Certificate after successfully completing the training course.
As part of this online Python course you will be working on real time Python projects that have high relevance in the corporate world, step-by-step assignments and curriculum designed by industry experts. Upon completion of the Python online course you can apply for some of the best jobs in top MNCs around the world at top salaries. Intellipaat offers lifetime access to videos, course materials, 24/7 Support, and course material upgrading to latest version at no extra fees. Hence it is clearly a one-time investment for a hands-on Python online course.
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.
A senior software architect at NextGen Healthcare who has previously worked with IBM Corporation. Suresh has worked on Big Data, Data Science, advanced analytics, Internet of Things, Azure along with AI domains like Machine Learning and Deep Learning. He has successfully implemented high impact projects in major corporations around the world.
An experienced blockchain professional who has been bringing integrated blockchain particularly Hyperledger and Ethereum and big data solutions to the cloud. David has previously worked on Hadoop, AWS Cloud, Big Data and Pentaho projects that have had major impact on revenues of marquee brands around the world.
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