Python A to Z Bootcamp (2021) Basic-DataScience-API(50 Hrs)

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