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”.
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You will be able to analyze a large file
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Build a Solid Foundation in Data Analysis with Python
After completing the course you will have professional experience on;
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Pandas Data Structures: Series, DataFrame and Index Objects
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Essential Functionalities
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Data Handling
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Data Pre-processing
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Data Wrangling
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Data Grouping
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Data Aggregation
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Pivoting
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Working With Hierarchical Indexing
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Converting Data Types
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Time Series Analysis
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Advanced Pandas Features and much more with hands-on exercises and practice works.
Series at a Glance
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Series Methods and Handling
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Introducing DataFrames
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DataFrames More In Depth
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Working With Multiple DataFrames
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Going MultiDimensional
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GroupBy And Aggregates
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Reshaping With Pivots
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Working With Dates And Time
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Regular Expressions And Text Manipulation
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Visualizing Data
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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!
Pandas Building Blocks
Pandas_Data Structures
Data Indexing And Selection
Essential Functionalities
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16Theory On Data Indexing And Selection
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17Data Selection In Series Part 1
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18Data Selection In Series Part 2
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19Indexers Loc And Iloc In Series
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20Data Selection In DataFrame Part 1
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21Data Selection In DataFrame Part 2
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22Accessing Values Using Loc Iloc And Ix In DataFrame Objects
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23Practice Part 02
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24Practice Part 02 Solution
Data Handling
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25Theory On Essential Functionalities
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26How To Reindex Pandas Objects
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27How To Drop Entries From An Axis
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28Arithmetic And Data Alignment
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29Arithmetic Methods With Fill Values
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30Broadcasting In Pandas
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31Apply And Applymap In Pandas
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32How To Sort And Rank In Pandas
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33How To Work With The Duplicated Indices
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34Summarising And Computing Descriptive Statistics
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35Unique Values Value Counts And Membership
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36Practice_Part_03
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37Practice_Part_03 Solution
Data Cleaning And Preparation
Data Wrangling
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46Theory On Data Preprocessing
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47How To Handle Missing Values
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48How To Filter The Missing Values
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49How To Filter The Missing Values Part 2
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50How To Remove Duplicate Rows And Values
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51How To Replace The Non Null Values
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52How To Rename The Axis Labels
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53How To Descretize And Bin The Data Part - 1
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54How To Filter And Detect The Outliers
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55How To Reorder And Select Randomly
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56Converting The Categorical Variables Into Dummy Variables
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57How To Use 'map' Method
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58How To Manipulate With Strings
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59Using Regular Expressions
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60Working With The Vectorized String Functions
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61Practice_Part_05
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62Practice_Part_05 Solution
Data Grouping And Aggregation
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63Theory On Data Wrangling
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64Hierarchical Indexing
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65Hierarchical Indexing Reordering And Sorting
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66Summary Statistics By Level
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67Hierarchical Indexing With DataFrame Columns
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68How To Merge The Pandas Objects
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69Merging On Row Index
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70How To Concatenate Along An Axis
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71How To Combine With Overlap
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72How To Reshape And Pivot Data In Pandas
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73Practice_Part_06
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74Practice_Part_06 Solution