Python For Data Analysis and Data Science: Zero To Mastery With Pandas
Created by Pruthviraja L, Last Updated 16-Apr-2020, Language:English
Python For Data Analysis and Data Science: Zero To Mastery With Pandas
Learn how to use Python for Data Science Or Data Analysis. Learn Hand's on With 100+ Exercises & Real Life Projects!
Created by Pruthviraja L, Last Updated 16-Apr-2020, Language:English
What Will I Get ?
- How to code with Pandas toolkit
- Learn hundreds of methods and attributes across numerous pandas objects
- Manipulate data quickly and efficiently
- Create dataframes with pandas and Recognize analytical approaches to data
- Solid Foundation in Data Analysis with Python
- You will be able to analyze a large file
- You will be able to analyze Time Series Analysis
- You will be able to work with the String Manipulation
- Data Munging and Data Cleaning
Requirements
- Basic Knowledge of Computers and Programming.
- Download Anaconda 4.2.0, the free data science platform by Continuum, which contains Python 3.5.2 and pandas 0.19.2.
- Basic Or intermediate experience with Microsoft Excel or another spreadsheet software but not necessary
- Basic Programming knowledge or knowing any other programming languages will also helps
Description
Hello, dear learning aspirants welcome to “Python For Data Analysis and Data Science: Zero To Mastery With Pandas ”. We love programming. Python is one of the most popular programming languages in today is technical world. Python offers both object oriented and structural programming features. Hence, we are interested in data analysis with Pandas in this course.
This course is for those who are ready to take their data analysis skill to the next higher level with Python data analysis toolkit, i.e. "Pandas". This tutorial is designed for beginners and intermediates but that does not mean that we will not talk about the advanced stuff as well. Our approach of teaching in this tutorial is simple and straightforward, no complications are included to make bored Or lose concentration.
In this tutorial, I will be covering all the basic things you will need to know about the Pandas to become a data analyst or data scientist. We are adopting a hands on approach to learn things easily and comfortably. You will enjoy learning as well as the exercises to practice along with the real life projects (The projects included are the part of large size research oriented industry projects).
I trust it is a wonderful platform and I got an wonderful opportunity to share and gain my technical knowledge with the learning aspirants and data science enthusiasts like you. We will also provide you with a course completion certificate and it will add great value to your career.
Who is this course for?
- Data Analysis Beginner
- Business and Analyst
- Students and Other Professionals
- Beginner Python developers Curious to learn about Data Science
- Aspiring data scientists who want to add Python to their tool arsenal
- Any curious learner who wants to update their knowledge in Business Analysis
- AI and ML aspirants to upgrade their knowledge in Data Preprocessing before applying the machine learning algorithms to their project
What you will learn?
You will become a specialist in the following things while learning via this course
“Data Analysis With Pandas”.
You will be able to analyze a large file
Build a Solid Foundation in Data Analysis with Python
After completing the course you will have professional experience on;
Pandas Data Structures: Series, DataFrame and Index Objects
Essential Functionalities
Data Handling
Data Pre processing
Data Wrangling
Data Grouping
Data Aggregation
Pivoting
Working With Hierarchical Indexing
Converting Data Types
Time Series Analysis
Advanced Pandas Features and much more with hands on exercises and practice works.
I think it is a wonderful platform and I got a wonderful opportunity to share and gain my technical knowledge with the learning aspirants and data science enthusiasts.
So, what are you waiting for? Take this course for having an exploratory, engaging, and enlightening learning experience. I wish you all good luck with the course and happy learning.
Course Content
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Getting Started
6 Lectures 00:47:54-
Course Introduction
Preview00:03:35 -
How To Get Most Out Of This Course
00:02:05 -
How To Install Python IPython And Jupyter Notebook
00:08:26 -
How To Install Anaconda For macOS And Linux Users
00:06:37 -
How To Work With The Jupyter Notebook Part - 1
00:16:12 -
How To Work With The Jupyter Notebook Part - 2
00:10:59
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Pandas Building Blocks
2 Lectures 00:19:10-
How To Work With The Tabular Data
Preview00:05:22 -
How To Read The Documentation In Pandas
Preview00:13:48
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-
Pandas Data Structures
6 Lectures 01:09:25-
Theory On Pandas Data Structures
00:05:43 -
How To Construct The Pandas Series
Preview00:12:18 -
How To Construct The DataFrame Objects
00:13:00 -
How To Construct The Pandas Index Objects
00:12:16 -
Practice Part 01
00:04:10 -
Practice Part 01 Solution
Preview00:21:58
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-
Data Indexing And Selection
9 Lectures 00:58:24-
Theory On Data Indexing And Selection
00:05:49 -
Data Selection In Series Part 1
00:05:42 -
Data Selection In Series Part 2
00:02:15 -
Indexers Loc And Iloc In Series
00:12:12 -
Data Selection In DataFrame Part 1
00:04:33 -
Data Selection In DataFrame Part 2
00:03:27 -
Indexers Loc Iloc And Ix In DataFrame Objects
00:09:00 -
Practice Part 02
00:02:38 -
Practice Part 02 Solution
00:12:48
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Essential Functionalities
13 Lectures 02:03:12-
Theory On Essential Functionalities
00:10:02 -
How To Reindex Pandas Objects
00:11:44 -
How To Drop Entries From An Axis
00:08:11 -
Arithmetic And Data Alignment
00:07:20 -
Arithmetic Methods With Fill Values
00:15:25 -
Broadcasting In Pandas
00:06:56 -
Apply And Applymap In Pandas
00:07:51 -
How To Sort And Rank In Pandas
00:13:22 -
How To Work With The Duplicated Indices
00:04:06 -
Summarising And Computing Descriptive Statistics
00:07:02 -
Unique Values Value Counts And Membership
00:12:00 -
Practice_Part_03
00:02:16 -
Practice_Part_03 Solution
00:16:57
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Data Handling
8 Lectures 01:24:38-
Theory On Data Handling
00:04:32 -
How To Read The Csv Files Part - 1
00:19:27 -
How To Read The Csv Files Part - 2
00:14:38 -
How To Read Text Files In Pieces
00:07:24 -
How To Export Data In Text Format
00:09:47 -
How To Use Python's Csv Module
00:10:40 -
Practice_Part_04
00:02:41 -
Practice_Part_04 Solution
00:15:29
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Data Cleaning And Preparation
17 Lectures 02:54:13-
Theory On Data Preprocessing
00:10:53 -
How To Handle Missing Values
00:09:34 -
How To Filter The Missing Values
00:09:01 -
How To Filter The Missing Values Part 2
00:09:08 -
How To Remove Duplicate Rows And Values
00:12:25 -
How To Replace The Non Null Values
00:09:04 -
How To Rename The Axis Labels
00:06:41 -
How To Descretize And Bin The Data Part
00:22:03 -
How To Filter And Detect The Outliers
00:03:46 -
How To Reorder And Select Randomly
00:07:07 -
Converting The Categorical Variables Into Dummy Variables
00:09:49 -
How To Use 'map' Method
00:06:52 -
How To Manipulate With Strings
00:12:24 -
Using Regular Expressions
00:20:09 -
Working With The Vectorized String Functions
00:08:07 -
Practice_Part_05
00:02:33 -
Practice_Part_05 Solution
00:14:37
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-
Data Wrangling
12 Lectures 01:45:08-
Theory On Data Wrangling
00:07:42 -
Hierarchical Indexing
00:08:12 -
Hierarchical Indexing Reordering And Sorting
00:06:47 -
Summary Statistics By Level
00:02:47 -
Hierarchical Indexing With DataFrame Columns
00:05:03 -
How To Merge The Pandas Objects
00:19:40 -
Merging On Row Index
00:13:10 -
How To Concatenate Along An Axis
00:18:37 -
How To Combine With Overlap
00:06:46 -
How To Reshape And Pivot Data In Pandas
00:08:51 -
Practice_Part_06
00:01:22 -
Practice_Part_06 Solution
00:06:11
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-
Data Grouping And Aggregation
9 Lectures 01:03:08-
Theory On Data GroupBy And Aggregation
00:03:59 -
Groupby Operation
00:15:37 -
How To Iterate Over Groupby Object
00:05:45 -
How To Select Columns In Groupby Method
00:02:59 -
Grouping Using Dictionaries And Series
00:02:57 -
Grouping Using Functions And Index Level
00:05:28 -
Data Aggregation
00:10:19 -
Practice_Part_07
00:02:54 -
Practice_Part_07 Solution
00:13:10
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-
Time Series Analysis
10 Lectures 01:38:57-
Theory On Time Series Analysis
00:06:28 -
Introduction To Time Series Data Types
00:10:12 -
How To Convert Between String And Datetime
00:12:53 -
Time Series Basics With Pandas Objects
00:12:53 -
Date Ranges Frequencies And Shifting Part - 1
00:11:21 -
Date Ranges Frequencies And Shifting Part - 2
00:10:41 -
Periods And Period Arithmetic’s
00:10:46 -
Time Zone Handling
00:08:50 -
Practice_Part_08
00:02:41 -
Practice_Part_08 Solution
00:12:12
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How To Analyse With The Part of Real Life Projects
9 Lectures 01:35:24-
A Brief Introduction To The Pandas Projects
00:10:30 -
Project_1 Description
00:04:41 -
Project_1 Solution Part - 1
Preview00:17:29 -
Project_1 Solution Part - 2
00:13:47 -
Project_2 Description
00:02:19 -
Project_2 Solution
00:19:29 -
Project_3 Description
00:02:37 -
Project_3 Solution Part - 1
00:12:07 -
Project_3 Solution Part - 2
00:12:25
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Pruthviraja L
Hi, I am Pruthviraja L, with more than 6+ years of Training and Teaching experience from Technical Institutes, Teaching is my passion. I've obtained my both PG( M.Tech) in Power Systems Engineering and UG(B.E) in Electrical and Electronics Engineering from V.T.U - Belgaum, Karnataka, India.
I'm a Certified Data Analyst. I got certifications from various eLearning centers including Udemy, Intellipaat-Bengaluru, LinkedIn eLearning center, etc.
I've successfully published and presented 6 + research papers in various 'National & International Journals and Conferences'. I'm a member of various National and International Journals including Elsevier and IEEE.
I'm a multi-faceted software professional aspirant with demonstrated capability in deploying analytical and programming methodologies to extract insights for boosting and bolstering user requirements. Adept at conducting statistical analysis and data modeling for transforming raw data into actionable strategies. Proficient in visualizing data to execute projects & set organizations on the path to profitability. 6 + years of teaching experience in engineering institutes with programming skills in Matlab, Python, SAS, R and enthusiasm in developing AI and Machine learning skills motivated me to involve in the dynamic working environment to utilize skills and maximize the profit for the organization.
I've written a student-friendly textbook in the electrical engineering field titled 'Elements of Electrical Engineering (ISBN: 9789386768001)' under the publication of 'I.K. International Publishing House Pvt Ltd', New Delhi-110016 India. The book is available in many countries including the USA and UK via Amazon and many other seller portals. The book is now started distributing under Wiley India Pvt. Ltd.