May 1st,2015 - 8no.s
May 4th,2015 - 10no.s
May 1st,2015 - 8no.s
Harvesting and collecting web data can be performed manually via humans “cutting and pasting” or using any number of web data scraping, mining and harvesting tools. Essentially, extracting data from the web involves capturing what you see in your web browser. However, the dynamic nature of the web requires a web browser enabled approach to extracting data.
When defining web data, most people focus on well-known consumer and social media sites such as Google, Amazon, Wikipedia, Facebook and Twitter, but the most valuable data is usually located somewhere else. It could be in industry or location specific sites, password protected B2B portals, cloud apps, government sites and even your competitor’s site. It also includes data locked in applications that lives inside your firewall. Once you expand the extraction to include data from all relevant sources and understand how easy it is to collect and make available to your employees, you begin to realize the enormous potential real-time web data offers.
Media3 offers an exclusive training program. The total training program will take 2 months of time with 5 sessions per week. The final Capstone project will take 1 week of time, With 15 hours of live development experience.
Learn, Work & innovate @ Media3
I am dividing the course into two parts
1) Basic Python Programming
2) Python for Data Science
The first module will contain 15 sessions
Why should you learn to write program?
What is a program?
The building blocks of program?
Introduction to Python Programming
Why Python ?
Difference between python2 and python3
Values and types
Python reserved keywords
Statements in python.
Operators and operands in Python
Expression in python
Running python from terminal
Built-in Functions in Python
Type conversion function in Python
Random numbers in python
Math functions in Python – math Library
Adding new Functions
Use of generators in Python
Break and continue statements
String and string slices in python
Looping and counting
String methods and parsing strings
File handling in python
Searching through a file
Lists in Python
Lists and Functions
Lists and strings
Looping and Dictionaries
Advanced text parsing
Dictionaries and tuples
Multiple assignment with dictionaries
Regular expressions in python
Extracting data using regular expressions
Combining searching and extracting
Handling Json files using python
Handling html files using python
Handling xml files using python
Web Scraping using python ( This will take 5 sessions if needed. Selenium will be covered and left as optional)
Python oop concepts
Use of Python Class functions
Super in python
Building packages using python
Using Decorates in python
Methods in python
Each project will take two days for time.
Python For Data Science
Installing Anaconda Package
Understanding the use of it
Installing packages for yml file
Installing packages using pip
Installing packages using conda
Installing packages from Git
Introduction to Git
Using Git in Live Projects
Creating Environments in Python
Using jupyter notebooks
Using numpy for matrix operations and data handling
Numpy operations (Around 50 operations will be discussed)
Scipy functions ( Around 50 important operations will be discussed)
Advanced Algebraic functions in Scipy
Contributing to scipy
Data Reading and manpulations using pandas
Data Harmonizing using pandas
Using Matplotlib (Graphics in Python)
Using barplot, scatter plot, histograms, stacked bar charts, pie charts etc.
Integrating Numpy, Scipy, Pandas and matplotlib for Data Manuplations
Statistical Analysis using Python
Mean, Median, Mode
Data Distributions generation
Random variable generations
Variance and Standard deviations
Hypothesis testing in Python
Linear regression using python
Logistic Regression using Python
Risk Analytics ( Project -1 )
HR Analytics (Project-2)
Churn and Telecom Analytics (Project-3)
Projects are subjected to change.
All sessions from 9 will take 3-4 days of time to complete. It depends on Employee/Student understanding of mathematics.