Python For Data Science A-Z: EDA With Real Exercises In 2024

Work With Pandas, Python For Data Science, ML & Data Analysis, Data Prep With EDA &100+ Exercises & Real Life Projects

What You will 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’
  • This course includes:
  • 15.5 hours on-demand video
  • 1 article
  • 5 downloadable resources
  • Access on mobile and TV
  • Full lifetime access
  • Certificate of completion

Description


Hi, dear learning aspirants welcome to “Python For Data Science A-Z: EDA With Real Exercises In 2024 ” 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.

Series at a Glance

Series Methods and Handling

Introducing DataFrames

DataFrames More In Depth

Working With Multiple DataFrames

Going MultiDimensional

GroupBy And Aggregates

Reshaping With Pivots

Working With Dates And Time

Regular Expressions And Text Manipulation

Visualizing Data

Data Formats And I/O



Pandas and python go hand-in-hand which is why this bootcamp also includes a Pandas Coding In full length to get you up and running writing pythonic code in no time.

This is the ultimate course on one of the most-valuable skills today. I hope you commit to mastering data analysis with Pandas.

See you inside!

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

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


Course Content

  • 11 sections • 103 lectures • 15h 45m total length
  • How To Get Most Out Of This Course
  • Better To Know These Things
  • How To Install Anaconda For macOS And Linux Users
  • How To Work With The Jupyter Notebook Part-1
  • How To Work With The Jupyter Notebook Part-2
  • How To Work With The Tabular Data
  • Theory On Pandas Data Structures
  • How To Construct The DataFrame Objects
  • How To Construct The Pandas Index Objects
  • Practice Part 01
  • Theory On Data Indexing And Selection
  • Data Selection In Series Part 1
  • Data Selection In Series Part 2
  • Indexers Loc And Iloc In Series
  • Data Selection In DataFrame Part 2
  • Practice Part 02
  • Practice Part 02 Solution
  • Theory On Essential Functionalities
  • How To Reindex Pandas Objects
  • How To Drop Entries From An Axis
  • Arithmetic And Data Alignment
  • Arithmetic Methods With Fill Values
  • Broadcasting In Pandas
  • Apply And Applymap In Pandas
  • How To Sort And Rank In Pandas
  • Summarising And Computing Descriptive Statistics
  • Unique Values Value Counts And Membership
  • Practice_Part_03
  • Practice_Part_03 Solution
  • Theory On Data Handling
  • How To Read The Csv Files Part – 2
  • How To Read Text Files In Pieces
  • How To Export Data In Text Format
  • Practice_Part_04
  • Practice_Part_04 Solution
  • Theory On Data Preprocessing
  • How To Handle Missing Values
  • How To Filter The Missing Values Part 2
  • How To Remove Duplicate Rows And Values
  • How To Replace The Non Null Values
  • How To Rename The Axis Labels
  • How To Descretize And Bin The Data Part – 1
  • How To Filter And Detect The Outliers
  • How To Reorder And Select Randomly
  • Converting The Categorical Variables Into Dummy Variables
  • How To Use ‘map’ Method
  • Using Regular Expressions
  • Working With The Vectorized String Functions
  • Practice_Part_05
  • Practice_Part_05 Solution
  • Theory On Data Wrangling
  • Hierarchical Indexing Reordering And Sorting
  • Summary Statistics By Level
  • Hierarchical Indexing With DataFrame Columns
  • Merging On Row Index
  • How To Concatenate Along An Axis
  • How To Combine With Overlap
  • How To Reshape And Pivot Data In Pandas
  • Practice_Part_06
  • Practice_Part_06 Solution
  • Thoery On Data Groupby And Aggregation
  • Groupby Operation
  • How To Iterate Over Groupby Object
  • How To Select Columns In Groupby Method
  • Grouping Using Dictionaries And Series
  • Grouping Using Functions And Index Level
  • Data Aggregation
  • Practice_Part_07
  • Practice_Part_07 Solution
  • Theory On Time Series Analysis
  • Introduction To Time Series Data Types
  • How To Convert Between String And Datetime
  • Time Series Basics With Pandas Objects
  • Date Ranges Frequencies And Shifting
  • Date Ranges Frequencies And Shifting Part – 2
  • Time Zone Handling
  • Periods And Period Arithmetic’s
  • Practice_Part_08
  • Practice_Part_08 Solution
  • A Brief Introduction To The Pandas Projects
  • Project_1 Description
  • Project_1 Solution Part – 1
  • Project_1 Solution Part – 2
  • Project_2 Description
  • Project_2 Solution
  • Project_3 Description
  • Project_3 Solution Part – 1
  • Project_3 Solution Part – 2
  • Project Assignment



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