A-Z Python Bootcamp(2021)-Basics to Data Science (50+ Hours)

Python Basic, Data Structure, API, Scraping, Regex, Pandas, Numpy, Matplotlib, Scikit Learn, Supervised Learning

What you'll learn
Python basic to advanced in One course
Create your first python project
Create your own data science project
Create your project using Django

Requirements
Willingness to learn Python
Decent computer configuration

Description
Learn python basics by practicing Basic syntax, Regular Expression, Data structure & Algorithm and API

This course is aimed at complete beginners who have never programmed before, as well as existing programmers who pursue to increase their career options by learning Python.

Python is one of the most popular programming languages in the world – Huge companies like Google, amazon use it in mission critical applications like Google Search.

By the end of the course you’ll be able to code with confidence using Python programming. This will help you understanding the usage of python in different circumstance.


Become a Junior Python Programmer and land a job in silicon valley.

Get access to all the codes used in the course.

This course will contain all 80+ videos explaining necessary things a beginner needs to know in a programming language.

This course will get continuously updated for beginners to get learn more. I promise to get at least 1 video section to be added per quarter for the next 2 years.


Objective of the Python basic content:

Giving confidence that any student they can be a programmer.

Detailed Installation process

Covers syntax in Python.

Decision making and loops

Python basics like Data types, functions, Modules.

Excel Operation

Python file handling.

Regular Expression.

Programming with OOPS Concept.

Tools required for a Junior python developer job.

This course will teach you Python in a practical manner, with every lecture comes a full coding screen cast and a corresponding code notebook! Learn in whatever manner is best for you!

Help you in enabling processing the data from different source.

File handling from different sources.

The course covers basic algorithmic techniques and ideas for computational problems arising frequently in practical applications: sorting and searching, divide and conquer, greedy algorithms, dynamic programming.

You will learn a lot of theory: how to sort data and how it helps for searching. How to break a large problem into pieces and solve them recursively and it makes sense to proceed greedily.


Objective of the Python data structure content:

Recursion.

Algorithm run time analysis

Arrays

Stack

Linked list

Data Structure

Binary Tree

Binary Search Tree

AVL Tree

Heap tree

Queue

Sorting

Hash Table

Graph Theory

Magic Framework

Computer Programming

Dynamic Programming

Regular expression (Regex):

Fetch the textual information from logs.

Perform the changes in the existing textual information for re-using.


API Python:

This section help you understand the working on API and how to implement the same using Python.

Here we will learn how to get and post the request using API and implement the same.

Will create a simple currency conversion calculator.

We will also cover API for website which we need to sign in. We will be using the API keys and ID to login and fetch the details.

We will explain how to structure and export the data in CSV using Pandas.


Scraping:

Fetch the dat from the URL

Get the information from Robot protected the website.

Fetch the information using pagination

Fetch the information by crawling the pages and storing it in DB.

Pandas:

Creation of Data representation

Data filtering

Data framework

Selection and viewing

Data Manipulation


Numpy:

Datatypes in Numpy

Creating arrays and Matrix.

Manipulation of data.

Standard deviation and variance.

Reshaping of Matrix.

Dot function

Mini-project using Numpy and Pandas package

Matplotlib:

Creation Plots - Line, Scatter, bar and Histogram.

Creating plots from Pandas and Numpy data

Creation of subplots

Customization and saving plots

Scikit Learn

End to end Implementation of Data science and Machine Learning model using Scikit-Learn(SKLearn)

Explained the option of improving the results by changing parameters and Hyper-parameter in a model.

Getting data ready

Choosing estimators

Fitting the data

Predicting values

Evaluation of results

Improving the results of the model

Saving the model.


Supervised Learning

Data analysis and Basic Plotting

Data Correlation in modelling

Getting data ready for modelling

Model explained in Detail

Improving the Model Randomized SearchCV

Grid Search CV


Unsupervised Learning

K-Means Clusterng

Finding Distance between Clusters

Hierarchial Clusterng

Mini-Project

Who this course is for:
Beginners who are willing to learn to Code or program
People willing to learn programming from scratch
Get all python related information in a single course

More Info