Pandas For Data Wrangling: Core Skills For Data Scientists

Master data analysis with Pandas and Python through hands-on projects and real-world case studies.

What you'll learn

Data manipulation techniques using libraries like pandas in Python.

Statistical analysis methods for exploring and understanding datasets.

Machine learning algorithms and their applications for predictive modeling.

Data visualization techniques to effectively communicate insights.

Programming skills in Python and R languages.

Proficiency in using libraries such as NumPy, Matplotlib, scikit-learn, and TensorFlow.

Hands-on experience through projects and case studies.

Practical application of learned concepts to real-world data science problems.

Requirements

Students should have a basic understanding of programming concepts, preferably in Python, and a fundamental grasp of mathematics and statistics.

Description

Welcome to the "Data Analysis with Pandas and Python" course! This course is designed to equip you with the essential skills and knowledge required to proficiently analyze and manipulate data using the powerful Pandas library in Python.Whether you're a beginner or have some experience with Python programming, this course will provide you with a solid foundation in data analysis techniques and tools. Throughout the course, you'll learn how to read, clean, transform, and analyze data efficiently using Pandas, one of the most widely used libraries for data manipulation in Python.From understanding the basics of Pandas data structures like Series and DataFrames to performing advanced operations such as grouping, filtering, and plotting data, each section of this course is crafted to progressively enhance your proficiency in data analysis.Moreover, you'll have the opportunity to apply your skills in real-world scenarios through case studies and projects, allowing you to gain hands-on experience and build a portfolio of projects to showcase your expertise.By the end of this course, you'll have the confidence and competence to tackle a wide range of data analysis tasks using Pandas and Python, empowering you to extract valuable insights and make informed decisions from diverse datasets. Let's embark on this exciting journey into the world of data analysis together!Section 1: Pandas with Python TutorialIn this section, students will embark on a comprehensive journey into using Pandas with Python for data manipulation and analysis. Starting with an introductory lecture, they will become familiar with the Pandas library and its integration within the Python ecosystem. Subsequent lectures will cover practical aspects such as reading datasets, understanding data structures like Series and DataFrames, performing operations on datasets, filtering and sorting data, and dealing with missing values. Advanced topics include manipulating string data, changing data types, grouping data, and plotting data using Pandas.Section 2: NumPy and Pandas PythonThe following section introduces students to NumPy, a fundamental package for scientific computing in Python, and its integration with Pandas. After an initial introduction to NumPy, students will learn about the advantages of using NumPy over traditional Python lists for numerical operations. They will explore various NumPy functions for creating arrays, performing basic operations, and slicing and dicing arrays. The section then seamlessly transitions to Pandas, where students will learn to create DataFrames from Series and dictionaries, perform data manipulation operations, and generate summary statistics on data.Section 3: Data Analysis With Pandas And PythonThis section focuses on practical data analysis using Pandas and Python. Students will learn about the installation of necessary software, downloading and loading datasets, and slicing and dicing data for analysis. A case study involving the analysis of retail dataset management will allow students to apply their newfound skills in a real-world scenario, gaining valuable experience in data management and analysis tasks.Section 4: Pandas Python Case Study - Data Management for Retail DatasetIn this section, students will delve deeper into a comprehensive case study involving the management of a retail dataset using Pandas. They will work through various parts of the project, including data cleaning, transformation, and analysis, gaining hands-on experience in handling large datasets and deriving actionable insights from them.Section 5: Analyzing the Quality of White Wines using NumPy PythonThe final section introduces students to a specific application of data analysis using NumPy and Python: analyzing the quality of white wines. Through file handling, slicing, sorting, and gradient descent techniques, students will learn how to analyze and draw conclusions from real-world datasets, reinforcing their understanding of NumPy and Python for data analysis tasks.

Overview

Section 1: Pandas with Python Tutorial

Lecture 1 Introduction to Pandas with Python

Lecture 2 Understanding Jupiter Environment

Lecture 3 Reading the Data Set

Lecture 4 Series and Data Frame

Lecture 5 Operations in Data Set

Lecture 6 More on Panda Functions

Lecture 7 Column Names and Operation

Lecture 8 Removing Columns and Rows

Lecture 9 Sorting Data Frame

Lecture 10 Filtering Data

Lecture 11 Filter Multiple Criteria

Lecture 12 Selective Columns and Rows

Lecture 13 Data Frame and Series

Lecture 14 Axis Parameter

Lecture 15 String Methods in Pandas

Lecture 16 Changing the Data Types

Lecture 17 Example of Data Type Change

Lecture 18 Group by Functions

Lecture 19 Functions on Series

Lecture 20 Plotting series in Pandas

Lecture 21 Dealing with Null Values

Lecture 22 Uses of Index

Lecture 23 Column in Index

Lecture 24 Output of Data

Lecture 25 Functions of iX Method

Lecture 26 InPlace Parameter

Lecture 27 Inspecting the Space

Lecture 28 Reducing the Space

Lecture 29 Using in Country Series

Lecture 30 Creating Manual Data Frame

Lecture 31 Random Sampling with Pandas

Lecture 32 Concept of Dummy Coding

Lecture 33 Creating Dummified Values

Lecture 34 Duplicates in Data Frame

Lecture 35 Functions for Date and Time

Lecture 36 Setting with Copy Warning

Lecture 37 Example on Copy Warning

Lecture 38 Changing the Display Option

Lecture 39 Formatting the Data

Lecture 40 Tricks for Display Options

Lecture 41 Data with Rows and Columns

Lecture 42 Converting Data Frame

Lecture 43 Introduction to Azure Data Lake

Lecture 44 Merging Data Frames

Lecture 45 Shaping a Data Frame

Lecture 46 Filling NA Values

Lecture 47 Importing Time Series Data

Lecture 48 Working with Interpolate Method

Lecture 49 Stacking and Unstacking

Lecture 50 Stacking and Unstacking for 3 Levels

Lecture 51 Concept of Crosstab

Lecture 52 More on Crosstab

Lecture 53 More Options with Crosstab

Lecture 54 Functions of Pivot

Lecture 55 Pivot Table Method

Lecture 56 Example on Pivot Table

Lecture 57 Data Frame to CSV File

Lecture 58 Using Excel Functions

Lecture 59 Summary on Pandas

Section 2: NumPy and Pandas Python

Lecture 60 Introduction to Numpy

Lecture 61 Importing Numpy Package and Basic Commands

Lecture 62 Comparision Between List

Lecture 63 Numpy on Basis of Memory and Time

Lecture 64 Why we are using Numpy and why not List

Lecture 65 Numpy Operations and Subsetting

Lecture 66 2D Numpy Arrays

Lecture 67 Subsetting Operations

Lecture 68 Descriptive Statistics in Numpy Arrays

Lecture 69 Array Updating

Lecture 70 Concatenate Functions

Lecture 71 Introduction to Pandas

Lecture 72 Creating Dataframe from Series and Dictionary

Lecture 73 Making Dataframe from Dictionary

Lecture 74 Concatenate Dataframe

Lecture 75 Joins and Pivot

Lecture 76 Unipivot Dataframe

Lecture 77 Dataframe Operations

Lecture 78 Slicing

Lecture 79 Dicing

Lecture 80 Sorting Dataframes

Lecture 81 Summary Statistics

Lecture 82 Dealing with Duplicate Values

Lecture 83 Importing Dataset

Lecture 84 Head Tail and Unique Function

Lecture 85 Accessing Column

Lecture 86 Rename Variables

Lecture 87 Dropping Variables

Lecture 88 Descriptive Statisitcs

Lecture 89 Group by Functions

Lecture 90 Filtering Functions

Lecture 91 Introduction to Jupyter Notebook

Lecture 92 Missing Values Introduction

Lecture 93 Imputation

Lecture 94 Working with Different Conditions

Section 3: Data Analysis With Pandas And Python

Lecture 95 Introduction to Data Analysis with Pandas and Python

Lecture 96 Installation of Softwares

Lecture 97 More on Installation

Lecture 98 Downloading and Loading Data

Lecture 99 Wine Data Set

Lecture 100 Slicing and Dicing

Section 4: Pandas Python Case Study - Data Management for Retail Dataset

Lecture 101 Introduction to Pandas Project

Lecture 102 Pandas Project Part 1

Lecture 103 Pandas Project Part 2

Lecture 104 Pandas Project Part 3

Lecture 105 Pandas Project Part 4

Lecture 106 Pandas Project Part 5

Lecture 107 Pandas Project Part 6

Lecture 108 Pandas Project Part 7

Lecture 109 Pandas Project Part 8

Lecture 110 Pandas Project Part 9

Lecture 111 Pandas Project Part 10

Lecture 112 Pandas Project Part 11

Lecture 113 Pandas Project Part 12

Lecture 114 Pandas Project Part 13

Lecture 115 Pandas Project Part 14

Lecture 116 Pandas Project Part 15

Lecture 117 Pandas Project Part 16

Lecture 118 Pandas Project Part 17

Lecture 119 Pandas Project Part 18

Section 5: Analyzing the Quality of White Wines using NumPy Python

Lecture 120 Introduction to Course

Lecture 121 File Handling

Lecture 122 Slicing and Broadcasting

Lecture 123 Splitting

Lecture 124 Stacking

Lecture 125 Sorting

Lecture 126 Gradient Descent

Lecture 127 Gradient Descent Continue

Lecture 128 Linear Algebra

Aspiring data scientists, analysts, researchers, and anyone interested in data science careers.,Individuals with a passion for data analysis and a desire to acquire essential skills in data science.,Students seeking to enhance their knowledge and proficiency in data manipulation, visualization, and analysis.,Professionals aiming to transition into data-related roles or advance their careers in data science.,Anyone looking to develop practical skills in statistical analysis, machine learning, and data-driven decision-making.