Python Certification Training - Melbourne, Australia

1. Python programming introduction
2. Scientific, mathematical & technical computing
3. Introduction to various Python libraries
4. Web scraping with Python language
5. Integrating Python with Hadoop & MapReduce
6. Machine learning algorithms in Python.

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

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

Melbourne is one of the biggest cities in Australia. It has a vibrant economy that includes all the major industry verticals. The technology sector in Melbourne is heavily invested in the Python programming language. Due to this there is a huge abundance of Python job opportunities in Melbourne.

There is a huge growth in the Python market in Melbourne, Australia. This boom in Python market in Melbourne will only intensify in the future thanks to the need for a powerful programming language for working on different applications cutting across industry verticals in the city of Melbourne, Australia.

• Python’s design & libraries provide 10 times productivity compared to C, C++, or Java
• A Senior Python Developer in the United States can earn $102,000 – indeed.com

Learning a top software programming language like Python can help professionals get ahead in their careers on a fast-track basis. Intellipaat is offering the definitive training in Python that can be taken by professionals for getting hands-on experience in working with Python through industry-designed projects.

Jersey City, Dubai, Los Angeles, San Francisco, Singapore, Toronto, Bangalore, Chicago, Houston, Hyderabad, London, New York, San Jose, Sydney, Pune

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 concepts 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.

Highlights :

• Analyzing data using Pandas Library
• Deploying Data Frame Manipulation
• Bar & box plots with MatPlotLib.

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.

Highlights :

• Deploy Scikit-learn ML library
• Develop code with Jupyter Notebook
• Build a model using performance matrix.

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:

• Server logs
• Potential uses of server log data
• Pig script
• Firewall logs
• Work flow editor