Python from Basics till Advance for Data Science

Python from Basics till Advance for Data Science, available at $19.99, has an average rating of 3.75, with 84 lectures, based on 2 reviews, and has 21 subscribers.

You will learn about You are going to learn core concepts of Python You are going to gain good insights of each and every functionality of Python scripting language Lot of hands on will be provided You will also learn how to solve problems step by step and make sure that all cases are passing in test You are going to learn Advance Python concepts with lot of hands on All the Python modules are covered which is required to perform Data Science related work i.e. Stats, NLP, Numpy, SciKit, Sklearn Matplotlib,Seaborn & many more This course is ideal for individuals who are This course is for those who wants to learn Python, Data Science and wants enter intto IT field or Those who are from Non-IT background It is particularly useful for This course is for those who wants to learn Python, Data Science and wants enter intto IT field or Those who are from Non-IT background.

Enroll now: Python from Basics till Advance for Data Science

Summary

Title: Python from Basics till Advance for Data Science

Price: $19.99

Average Rating: 3.75

Number of Lectures: 84

Number of Published Lectures: 84

Number of Curriculum Items: 84

Number of Published Curriculum Objects: 84

Original Price: ₹799

Quality Status: approved

Status: Live

What You Will Learn

You are going to learn core concepts of Python
You are going to gain good insights of each and every functionality of Python scripting language
Lot of hands on will be provided
You will also learn how to solve problems step by step and make sure that all cases are passing in test
You are going to learn Advance Python concepts with lot of hands on
All the Python modules are covered which is required to perform Data Science related work i.e. Stats, NLP, Numpy, SciKit, Sklearn Matplotlib,Seaborn & many more

Who Should Attend

This course is for those who wants to learn Python, Data Science and wants enter intto IT field
Those who are from Non-IT background

Target Audiences

This course is for those who wants to learn Python, Data Science and wants enter intto IT field
Those who are from Non-IT background

In this course you are going to learn every bits and pieces of Python from Basic till Advance. I am providing lot of hands on for each and every topic along with Jupyter notebooks as well as assignment for each topics. I am also providing step by step video guide to solve Hacker Rank Python quizzes.

In this course, I’ll start by giving a brief introduction of python to a beginner, making it a complete python tutorial for beginners in English.

This Python Course is totally in English and is made for Learning Python. All the topics related to python programming will be covered in this tutorial series.

I will make this python course as a complete python course with examples for beginners which will be taken to the advanced python level.

I wish you all the very best in your python learning journey through these Code tutorials!

Source code of all videos are separately given in all video’s description, you can grab all through via Jovian, GitHub or Google drive link.

It is relatively easy to learn when compared to pretty much any other programming language since most of the time, using Python to do something will take less lines of code than with another programming language.

I cover a wide variety of topics in this, including:

Basic and Advance Python:

Installing Python
Installing Anaconda
Strings
List
Tuple
Dictionaries
Functions
Built-in Functions
File I/O or File Handling
Error Handling
Filter, Map, Reduce
Modules
Local and Global Variables
args / kwargs
and much more!

Python modules for Data science :

Numpy
Pandas
Matplotlib
Seaborn
NLTK (Natural Language Tool Kit) for NLP (Natural Language Processing) Library

Many more……

All the jupyter notebooks can be downloaded from Jovian, Git hub and Google drive, In each content i’ll be providing those link details.

Course Curriculum

Chapter 1: Python Basic Introduction

Lecture 1: Python Introduction

Lecture 2: Python Operators

Lecture 3: Python String Data Type

Lecture 4: Python List Data Type

Lecture 5: Python Tuple Data Type

Lecture 6: Python If..else, if..elif…else conditional statements

Lecture 7: Python Sets

Lecture 8: Python Iterators and Generators

Lecture 9: Python List and Dictionary Comprehension

Chapter 2: Python Advance

Lecture 1: Python Functions

Lecture 2: Python Lambda Functions

Lecture 3: Python Modules and Packages

Lecture 4: Python File Handling

Lecture 5: Python Regular Expressions

Lecture 6: Python Debugging

Lecture 7: Python Exceptions

Lecture 8: Python Logging

Lecture 9: Python Memory Management

Lecture 10: Python Threading

Chapter 3: Numpy Tutorial

Lecture 1: Numpy Part-1

Lecture 2: Numpy Part-2

Lecture 3: Numpy Part-3

Lecture 4: Numpy Part-4

Chapter 4: Pandas Tutorial

Lecture 1: Pandas Part-1

Lecture 2: Pandas Part-2

Lecture 3: Pandas Part-3

Lecture 4: Pandas Part-4

Lecture 5: Pandas Part-5

Chapter 5: Matplotlib Tutorial

Lecture 1: Matplotlib Part-1

Lecture 2: Matplotlib Part-2

Lecture 3: Matplotlib Part-3

Chapter 6: Seaborn Tutorial

Lecture 1: Seaborn Part-1

Lecture 2: Seaborn Part-2

Chapter 7: Natural Language Processing (NLP)

Lecture 1: NLP Part-1

Lecture 2: NLP Part-2

Lecture 3: NLP Part-3

Lecture 4: NLP Part-4

Lecture 5: NLP Part-5

Lecture 6: NLP with Spacy Part-1

Lecture 7: NLP with Spacy Part-2

Lecture 8: NLP with Spacy Part-3

Lecture 9: NLP with Spacy Part-4

Chapter 8: Hacker Rank Series for Problem Solving

Lecture 1: Quiz-1

Lecture 2: Quiz-2

Lecture 3: Quiz-3

Lecture 4: Quiz-4

Lecture 5: Quiz-5

Lecture 6: Quiz-6

Lecture 7: Quiz-7

Lecture 8: Quiz-8

Lecture 9: Quiz-9

Lecture 10: Quiz-10

Lecture 11: Quiz-11

Lecture 12: Quiz-12

Chapter 9: Statistics for Machine Learning

Lecture 1: 01_Statistics_Introduction

Lecture 2: 02_Mean_Median_Mode

Lecture 3: 03_Percentile_Quartiles_Quantiles_IQR

Lecture 4: 04_Histogram_PDF_CDF_KDE

Lecture 5: 05_ViolinPlot_Population_Sample

Lecture 6: 06_Range_Variance_StandardDeviation

Lecture 7: 07_Empirical_&_Chebyshev_Rule

Lecture 8: 08_PDF_CDF_of_Gausian_Normal_Distribution

Lecture 9: 09_Standard_Normal_Variate

Lecture 10: 10_Symmetric_Distribution_Skewness_Kurtosis

Lecture 11: 11_Central_Limit_Theorem_CLT

Lecture 12: 12_Q-Q_Plot

Lecture 13: 13_Discrete_&_Continuous_Distribution

Lecture 14: 14_Box_Cox_Power_Transform

Lecture 15: 15_Covariance

Lecture 16: 16_Pearson_&_Spearman_rank

Lecture 17: 17_Confidence_Interval

Lecture 18: 18_Hypothesis_Testing

Chapter 10: Exploratory Data Analysis

Lecture 1: 01_EDA_Titanic_DataSet

Lecture 2: 02_EDA_Automotive_DataSet

Lecture 3: 03_EDA_Twitter_Cyberbully_DataSet

Lecture 4: 04_EDA_US_DataScience_Salary_DataSet

Lecture 5: 05_EDA_Customer_Service_Chat_DataSet

Lecture 6: 06_EDA_Disaster_Tweet_DataSet

Chapter 11: Machine Learning

Lecture 1: 01_ML_Introduction

Lecture 2: 02_Linear_Regression

Lecture 3: 03_Bias_Variance_under_overfitting

Lecture 4: 04_Lasso_&_Ridge_Regression

Lecture 5: 05_Logistics_Regression

Lecture 6: 06_Decision_Tree

Instructors

Pankaj Soni
Sr. Staff Engineer at Enphase

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4 stars: 1 votes
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