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Development Data Science Data Analysis

Python For Data Analysis, Data Science & ML With Pandas

Learn How To Code Python For Data Science, ML & Data Analysis, With 100+ Exercises and 4 Real Life Projects !
Rating: 4.2 out of 54.2 (186 ratings)
57,635 students
Created by Pruthviraja L
Last updated 3/2020
English
30-Day Money-Back Guarantee

What you'll learn

  • Build a Solid Foundation in Data Analysis with Python
  • You will be able to work with the Pandas Data Structures: Series, DataFrame and Index Objects
  • Learn hundreds of methods and attributes across numerous pandas objects
  • You will be able to analyze a large and messy data files
  • You can prepare real world messy data files for AI and ML
  • Manipulate data quickly and efficiently
  • You will learn almost all the Pandas basics necessary to become a 'Data Analyst'

Course content

11 sections • 103 lectures • 15h 45m total length

  • Preview03:35
  • How To Get Most Out Of This Course
    02:05
  • Better To Know These Things
    02:57
  • Preview08:26
  • How To Install Anaconda For macOS And Linux Users
    06:37
  • How To Work With The Jupyter Notebook Part-1
    16:12
  • How To Work With The Jupyter Notebook Part-2
    10:59

  • How To Work With The Tabular Data
    05:22
  • Preview13:48

  • Theory On Pandas Data Structures
    05:43
  • Preview12:18
  • How To Construct The DataFrame Objects
    13:01
  • How To Construct The Pandas Index Objects
    12:16
  • Practice Part 01
    04:10
  • Preview21:58

  • Theory On Data Indexing And Selection
    05:49
  • Data Selection In Series Part 1
    05:43
  • Data Selection In Series Part 2
    02:15
  • Indexers Loc And Iloc In Series
    12:12
  • Preview04:33
  • Data Selection In DataFrame Part 2
    03:27
  • Preview09:01
  • Practice Part 02
    02:38
  • Practice Part 02 Solution
    12:49

  • Theory On Essential Functionalities
    10:02
  • How To Reindex Pandas Objects
    11:44
  • How To Drop Entries From An Axis
    08:11
  • Arithmetic And Data Alignment
    07:20
  • Arithmetic Methods With Fill Values
    15:25
  • Broadcasting In Pandas
    06:56
  • Apply And Applymap In Pandas
    07:52
  • How To Sort And Rank In Pandas
    13:22
  • Preview04:06
  • Summarising And Computing Descriptive Statistics
    07:02
  • Unique Values Value Counts And Membership
    12:00
  • Practice_Part_03
    02:16
  • Practice_Part_03 Solution
    16:57

  • Theory On Data Handling
    04:32
  • Preview19:27
  • How To Read The Csv Files Part - 2
    14:38
  • How To Read Text Files In Pieces
    07:24
  • How To Export Data In Text Format
    09:47
  • Preview10:40
  • Practice_Part_04
    02:41
  • Practice_Part_04 Solution
    15:30

  • Theory On Data Preprocessing
    10:53
  • How To Handle Missing Values
    09:34
  • Preview09:01
  • How To Filter The Missing Values Part 2
    09:08
  • How To Remove Duplicate Rows And Values
    12:25
  • How To Replace The Non Null Values
    09:04
  • How To Rename The Axis Labels
    06:41
  • How To Descretize And Bin The Data Part - 1
    22:03
  • How To Filter And Detect The Outliers
    03:46
  • How To Reorder And Select Randomly
    07:07
  • Converting The Categorical Variables Into Dummy Variables
    09:49
  • How To Use 'map' Method
    06:52
  • Preview12:24
  • Using Regular Expressions
    20:09
  • Working With The Vectorized String Functions
    08:07
  • Practice_Part_05
    02:33
  • Practice_Part_05 Solution
    14:37

  • Theory On Data Wrangling
    07:42
  • Preview08:12
  • Hierarchical Indexing Reordering And Sorting
    06:47
  • Summary Statistics By Level
    02:47
  • Hierarchical Indexing With DataFrame Columns
    05:03
  • Preview19:40
  • Merging On Row Index
    13:10
  • How To Concatenate Along An Axis
    18:37
  • How To Combine With Overlap
    06:46
  • How To Reshape And Pivot Data In Pandas
    08:51
  • Practice_Part_06
    01:22
  • Practice_Part_06 Solution
    06:11

  • Thoery On Data Groupby And Aggregation
    03:59
  • Groupby Operation
    15:37
  • How To Iterate Over Groupby Object
    05:45
  • How To Select Columns In Groupby Method
    02:59
  • Grouping Using Dictionaries And Series
    02:57
  • Grouping Using Functions And Index Level
    05:28
  • Data Aggregation
    10:19
  • Practice_Part_07
    02:54
  • Practice_Part_07 Solution
    13:10

  • Theory On Time Series Analysis
    06:28
  • Introduction To Time Series Data Types
    10:12
  • How To Convert Between String And Datetime
    14:40
  • Time Series Basics With Pandas Objects
    12:53
  • Date Ranges Frequencies And Shifting
    11:21
  • Date Ranges Frequencies And Shifting Part - 2
    10:41
  • Time Zone Handling
    08:50
  • Periods And Period Arithmetic’s
    10:46
  • Practice_Part_08
    02:41
  • Practice_Part_08 Solution
    12:12

Requirements

  • Students must be willing to learn the Data Analysis with Python language
  • If you know basics of Python that is well and good
  • Basic Or intermediate experience with Microsoft Excel or another spreadsheet software, but not necessary
  • Basic knowledge of data types (strings, integers, floating points, Booleans) etc, but not necessary
  • Basic Programming knowledge Or knowing any other programming languages will also helps

Description

Hi, dear learning aspirants welcome to “Data Analysis With Pandas: A Complete Tutorial ” from beginner to advanced level. We love programming. Python is one of the most popular programming languages in today’s 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 the Python data analysis toolkit, i.e. "Pandas".

This tutorial is designed for beginners and intermediates but that doesn't 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'll 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 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.


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.

Who this course is for:

  • Beginner Python developers - Curious to learn about Data Science Or Data Analysis
  • Data Analysis Beginners
  • Aspiring data scientists who want to add Python to their tool arsenal
  • Students and Other Professionals
  • AI and ML aspirants to upgrade their knowledge in Data Preprocessing before applying the machine learning algorithms to their projects
  • Data Analyst job seekers who wants to update their Resume with Python's data analysis toolkit

Instructor

Pruthviraja L
Professional Educator, Software Trainer and Author
Pruthviraja L
  • 4.2 Instructor Rating
  • 273 Reviews
  • 66,098 Students
  • 2 Courses

Hi, I am Pruthviraja L, with more than 7+ 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 (ISBN: 9789389583939).

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