Python Certification Training

Learn Python the Big data way with integration of Machine learning, Hadoop, Pig, Hive and Web Scraping.

Python Certification Training

Course Description

Edureka's Python Certification Training not only focuses on fundamentals of Python, Statistics, Machine Learning and Spark but also helps one gain expertise on applied Data Science at scale using Python. The training is a step by step guide to Python and Data Science with extensive hands on. The course is packed with several activity problems, quiz and assignments and scenarios that help you gain practical experience in addressing an automation problem that would either require only Python or Machine Learning using Python. Starting from basics of Statistics such as mean, median and mode to exploring features such as Data Analysis, Regression, Classification, Clustering, Naive Bayes, Cross Validation, Label Encoding, Random Forests, Decision Trees and Support Vector Machines with a supporting example and exercise help you get into t... Read More »

Edureka’s Python Certification Training not only focuses on fundamentals of Python, Statistics, Machine Learning and Spark but also helps one gain expertise on applied Data Science at scale using Python. The training is a step by step guide to Python and Data Science with extensive hands on. The course is packed with several activity problems, quiz and assignments and scenarios that help you gain practical experience in addressing an automation problem that would either require only Python or Machine Learning using Python. Starting from basics of Statistics such as mean, median and mode to exploring features such as Data Analysis, Regression, Classification, Clustering, Naive Bayes, Cross Validation, Label Encoding, Random Forests, Decision Trees and Support Vector Machines with a supporting example and exercise help you get into the weeds.

Python has been one of the premier, flexible, and powerful open-source language that is easy to learn, easy to use, and has powerful libraries for data manipulation and analysis. For over a decade, Python has been used in scientific computing and highly quantitative domains such as finance, oil and gas, physics, and signal processing.

  • It’s continued to be a favourite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging programs is a breeze in Python with its built in debugger.
  • It runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license.
  • It has evolved as the most preferred Language for Data Analytics and the increasing search trends on Python also indicates that it is the ” Next Big Thing ” and a must for Professionals in the Data Analytics domain.

With exponential growth in data (as is evident from new Kaggle competitions that now support Torrent for downloading humongous data sets), it goes without saying that Data Scientist is incomplete without Big Data. So, in this course, we ensure you become a fully qualified Data Scientist by also teaching you basics of Spark in the context of data analysis and Machine Learning.

Python course will also cover both basic and advanced concepts of Python like writing Python scripts, sequence and file operations in Python. You will use libraries like pandas, numpy, matplotlib, scipy, scikit, pyspark and master the concepts like Python machine learning, scripts, sequence, web scraping and big data analytics leveraging Apache Spark.

Read Less
Course Outcomes:
  • Write Python scripts, unit test code
  • Understand different types of Machine Learning problems and related data
  • Programmatically download and analyze data
  • Apply machine learning techniques and algorithms over data
  • Learn feature engineering techniques like PCA
  • Ascertain accuracy of predictions using RMSE, Log Loss, AUC, Cross Validation
  • Learn techniques to deal with different types of data – ordinal, categorical, encoding
  • Compare algorithms and improve accuracy
  • Learn data visualization
  • Using IPython notebooks, master the art of presenting step by step data analysis.
Course Details:

Target Audience

Experienced professionals or Beginners. Anyone who wants to learn programming with Python can start right away! The course is exclusively designed for professionals aspiring to make a career in Big Data Analytics using Python. Software Professionals, Analytics Professionals, ETL developers, Project Managers, Testing Professionals are the key beneficiaries of this course. Other professionals who are looking forward to acquire a solid foundation of this widely-used open source general-purpose scripting language, can also opt for this course.

Access Timeframe

Lifetime access to Learning Management System (LMS) which has class presentations, quizzes, installation guide & class recordings.

Prerequisites

Although there are no hard pre-requisites, attendees having prior programming experience and familiarity with basic concepts such as variables/scopes, flow-control, and functions would be beneficial. Prior exposure to object-oriented programming concepts is not required, but definitely beneficial.
Certificate Info:

Type of Certification

Python Expert

Format of Certification

Digital

Professional Association/Affiliation

edureka certification has industry recognition and we are the preferred training partner for many MNCs e.g.Cisco, Ford, Mphasis, Nokia, Wipro, Accenture, IBM, Philips, Citi, Ford, Mindtree, BNYMellon etc.

Method of Obtaining Certification

edureka! certifies the learner as ‘Python Expert’ based on project performance, reviewed by their expert panel.

Course Outline

In this module, you will understand what Python is and why it is so popular. You will also learn how to set up Python environment, flow control and will write your first Python program.

Topics - Python Overview, About Interpreted Languages, Advantages/Disadvantages of Python, pydoc. Starting Python, Interpreter PATH, Using the Interpreter, Running a Python Script, Python Scripts on UNIX/Windows, Python Editors and IDEs. Using Variables, Keywords, Built-in Functions, Strings, Different Literals, Math Operators and Expressions, Writing to the Screen, String Formatting, Command Line Parameters and Flow Control.
In this module, you will learn different types of sequences in Python, the power of dictionary and how to use files in Python.

Topics - Lists, Tuples, Indexing and Slicing, Iterating through a Sequence, Functions for all Sequences, Using Enumerate(), Operators and Keywords for Sequences, The xrange() function, List Comprehensions, Generator Expressions, Dictionaries and Sets.
In this module, you will understand how to use and create functions, sorting different elements, Lambda function, error handling techniques and using modules in Python.

Topics - Functions, Function Parameters, Global Variables, Variable Scope and Returning Values. Sorting, Alternate Keys, Lambda Functions, Sorting Collections of Collections, Sorting Dictionaries, Sorting Lists in Place. Errors and Exception Handling, Handling Multiple Exceptions, The Standard Exception Hierarchy, Using Modules, The Import Statement, Module Search Path, Package Installation Ways.
In this module, we understand the Object Oriented Programming world in Python, use of standard libraries and regular expressions.

Topics - The Sys Module, Interpreter Information, STDIO, Launching External Programs, Paths, Directories and Filenames, Walking Directory Trees, Math Function, Random Numbers, Dates and Times, Zipped Archives, Introduction to Python Classes, Defining Classes, Initializers, Instance Methods, Properties, Class Methods and Data, Static Methods, Private Methods and Inheritance, Module Aliases and Regular Expressions.
In this module, you will learn how to debug, how to use databases and how a project skeleton looks like in Python.

Topics - Debugging, Dealing with Errors, Using Unit Tests. Project Skeleton, Required Packages, Creating the Skeleton, Project Directory, Final Directory Structure, Testing your Setup, Using the Skeleton, Creating a Database with SQLite 3, CRUD Operations, Creating a Database Object.
This module will help you understand what Machine Learning is, why Python is preferred for it and some important packages used for scientific computing.

Topics - Introduction to Machine Learning, Areas of Implementation of Machine Learning, Why Python, Major Classes of Learning Algorithms, Supervised vs Unsupervised Learning, Learning NumPy, Learning Scipy, Basic plotting using Matplotlib. In this module we will also build a small Machine Learning application and discuss the different steps involved while building an application.
In this module, you will learn in detail about Supervised and Unsupervised learning and examples for each category.

Topics - Classification Problem, Classifying with k-Nearest Neighbours (kNN) Algorithm, General Approach to kNN, Building the Classifier from Scratch, Testing the Classifier, Measuring the Performance of the Classifier. Clustering Problem, What is K-Means Clustering, Clustering with k-Means in Python and an Application Example. Introduction to Pandas, Creating Data Frames, Grouping, Sorting, Plotting Data, Creating Functions, Converting Different Formats, Combining Data from Various Formats, Slicing/Dicing Operations.
This module will cover Scikit and an introduction to Hadoop MapReduce concepts.

Topics - Introduction to Scikit-Learn, Inbuilt Algorithms for Use, What is Hadoop and why it is popular, Distributed Computation and Functional Programming, Understanding MapReduce Framework, Sample MapReduce Job Run.
In this module, you will understand how to use Python in Hadoop MapReduce as well as in PIG and HIVE.

Topics - - PIG and HIVE Basics, Streaming Feature in Hadoop, Map Reduce Job Run using Python, Writing a PIG UDF in Python, Writing a HIVE UDF in Python, Pydoop and MRjob Basics.
In this module, we will discuss about the powerful web scraping using Python and a real world project.

Topics - Web Scraping, Introduction to Beautifulsoup Package, How to Scrape Webpages. A real world project showing scrapping data from Google finance and IMDB.

DON'T HAVE TIME?

We can send you everything you need to know about this course through email.
We respect your privacy. Your information is safe and will never be shared.