Data Manipulation With Python, Pandas, R ,Sql And Alteryx

Unlock the Power of Data: Master Comprehensive Data Manipulation Techniques with Python, Pandas, R, SQL, and Alteryx

What you'll learn

Understand the role and importance of data manipulation in data analysis and data science.

Install and set up Python, R, SQL, Pandas, and Alteryx for data manipulation tasks.

Understand the basics of programming with Python and R, and writing SQL queries.

Manipulate data in Python using the Pandas library, including loading, cleaning, transforming, and analyzing data.

Use Python for data cleaning, including handling missing values and formatting data.

Write complex SQL queries to retrieve and manipulate data from relational database

Understand and use SQL operators, indexes, and table joins for effective data manipulation.

Understand the features and functionalities of the Alteryx platform for data manipulation.

Import and export data in various formats using Alteryx.

Use Alteryx for advanced data manipulation tasks, including data blending and spatial analysis.

Handle missing data and format data in Alteryx.

Understand and use data wrangling techniques in Alteryx, including data transformation, pivoting, and binning.

Create calculated fields and perform time series analysis in Alteryx.

Create and use macros in Alteryx to automate repetitive tasks.

Understand the basics of data manipulation with R, including using the dplyr and tidyr packages.

Write R scripts to filter, select, mutate, and arrange data using dplyr.

Reshape data in R using the gather and spread functions in the tidyr package.

Use the integration of Python, R, SQL, Pandas, and Alteryx in a single data manipulation workflow.

Apply the learned skills in real-world data manipulation projects.

Analyze and interpret the results of data manipulation tasks.

Troubleshoot and solve problems related to data manipulation.

Write efficient and reusable code for data manipulation.

Develop a systematic and strategic approach to handle large datasets.

Understand ethical considerations in data manipulation, including data privacy and data integrity.

Requirements

Basic computer skills.

Familiarity with programming concepts would be beneficial but not required.

No prior knowledge of Python, R, SQL, Pandas, or Alteryx is required, as this course will start from the basics.

Access to a computer with an Internet connection to install the necessary software and libraries.

Description

In the era of Big Data, the ability to manipulate and analyze complex datasets is not just an advantage; it's a necessity. The Comprehensive Data Manipulation course offers a deep dive into the world of data manipulation using five potent tools: Python, Pandas, R, SQL, and Alteryx. Whether you're a beginner just embarking on a career in data analysis, or a seasoned professional looking to expand your skillset, this course offers a robust foundation and advanced techniques in data manipulation.This course adopts a project-based approach, reinforcing learning through practical application. Starting with an overview of data manipulation and its role in data analysis, the course progresses to an in-depth exploration of each tool, covering their installation, setup, features, and unique strengths.Python, a versatile language renowned for its readability, is the first tool we tackle. Here, you'll learn the basics of Python programming for data manipulation, moving onto mastering the use of Python's powerful library, Pandas. With Pandas, you'll explore data cleaning, preprocessing, and analysis. Handling missing data, converting data types, parsing dates, and more become straightforward with this handy library.Next, we delve into SQL, a standard language for managing data held in relational databases. Through this section, you'll grasp SQL commands and functions, enabling you to interact with databases, retrieve, and manipulate data with precision.We then transition to R, another popular language for data analysis, with a focus on dplyr and tidyr packages. These packages allow for efficient data transformation, reshaping, and overall manipulation.Finally, we introduce Alteryx, a platform that provides advanced data blending, spatial analysis, and enables the creation of repeatable workflows. The Alteryx section covers all these features and includes how to handle missing data, format data, and perform time series analysis.While each of these tools is powerful in its own right, their true strength comes from their integration. The course culminates in a real-world data manipulation project requiring the use of Python, Pandas, R, SQL, and Alteryx in a unified workflow. This capstone project, focusing on the analysis and prediction of energy consumption, allows you to apply the learned skills in real-time and gives you a taste of real-world data manipulation challenges.With this comprehensive course, you'll not only learn the mechanics of each tool but also when and how to use them most effectively. You'll develop a systematic and strategic approach to handle large datasets, write efficient and reusable code, and understand ethical considerations in data manipulation. By the end of the course, you'll be well-equipped to tackle any data manipulation task, thereby opening new avenues in your data analysis or data science career.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Overview of Data Manipulation

Lecture 3 Introduction to Data Manipulation

Lecture 4 Role of Data Manipulation in Data Analysis

Lecture 5 Introduction to Data Manipulation Tools

Lecture 6 Overview of Python, R, SQL, Pandas and Alteryx

Lecture 7 Installing Required Software and Libraries

Section 2: Python Environment Setup

Lecture 8 Introduction to Jupyter Notebook

Lecture 9 Installing Jupyter Notebook

Lecture 10 Running Jupyter Notebook Server

Lecture 11 Common Jupyter Notebook Commands

Lecture 12 Jupyter Notebook Components

Lecture 13 The Notebook Dashboard

Lecture 14 Notebook User Interface

Lecture 15 Creating a new Notebook

Section 3: Python Fundamentals

Lecture 16 Python Expressions

Lecture 17 Python Statements

Lecture 18 Python Comments

Lecture 19 Python Data Types

Lecture 20 Casting Data Types

Lecture 21 Python Variables

Lecture 22 Python List

Lecture 23 Python Tuples

Lecture 24 Python Dictionaries

Lecture 25 Python Operators

Lecture 26 Python Conditional Statements

Lecture 27 Python Loops

Lecture 28 Python Functions

Section 4: Python and Pandas for Data Manipulation

Lecture 29 Python for Data Manipulation

Lecture 30 Introduction to Pandas

Lecture 31 Introduction to Pandas Library

Lecture 32 Tabular Data

Lecture 33 Exploring Pandas DataFrame

Lecture 34 Manipulating Pandas DataFrame

Lecture 35 What is data cleaning

Lecture 36 Data Cleaning process

Lecture 37 Series and DataFrame

Lecture 38 Loading Data into DataFrame

Lecture 39 Data Manipulation with Pandas

Lecture 40 Data Cleaning with Pandas

Lecture 41 Data Wrangling and Transformation

Lecture 42 Aggregation and Grouping

Lecture 43 Merge, Join, and Concatenate

Section 5: R for Data Manipulation

Lecture 44 Basics for Data Manipulation

Lecture 45 Introduction to R and RStudio

Lecture 46 Installing R on Windows

Lecture 47 Installing R on Macs

Lecture 48 Installing R Studio on Windows

Lecture 49 Installing R Studio on Macs

Lecture 50 Exploring R Studio Interface

Lecture 51 Creating a new project in R Studio

Lecture 52 Real-world Data Manipulation Project Using Python and Pandas

Lecture 53 What are packages

Lecture 54 How to install Packages

Lecture 55 Loading Packages

Lecture 56 Importing data into R Studio

Lecture 57 Reading CSV data with R

Lecture 58 Selecting a subset of data

Lecture 59 Performing multiple operations with Pipe Operator

Lecture 60 Cleaning Columns

Lecture 61 Creating new columns from existing Columns

Lecture 62 Create another R Project

Lecture 63 Load data into new project

Lecture 64 What is data wrangling

Lecture 65 Perform data wrangling

Lecture 66 Create a scatter plot

Lecture 67 Create a bar graph

Lecture 68 Basic Data Types in R

Lecture 69 Control Structures and Functions in R

Lecture 70 Data Manipulation with dplyr in R

Lecture 71 Introduction to dplyr package

Lecture 72 Filtering and Selecting Data with dplyr

Lecture 73 Arrange, Mutate, Summarize and Group By functions

Lecture 74 Data Manipulation with tidyr in R

Lecture 75 Introduction to tidyr package

Lecture 76 Reshape data with gather and spread functions

Lecture 77 Unite and separate columns

Section 6: MySQL Database Server Server

Lecture 78 What is MySQL

Lecture 79 MySQL Installation on Windows

Lecture 80 What is MySQL Workbench

Lecture 81 MySQL Installation on Macs

Lecture 82 MySQL Workbench installation on Macs

Lecture 83 Basic Database Concepts

Lecture 84 What is a Schema

Lecture 85 Database Schema

Lecture 86 MySQL Data Types

Lecture 87 Real-world Data Manipulation Project Using R

Section 7: SQL for Data Manipulation

Lecture 88 SQL Basics

Lecture 89 Introduction to SQL

Lecture 90 SQL Operators

Lecture 91 CREATE Database

Lecture 92 CREATE Table

Lecture 93 INSERT Data into Table

Lecture 94 SELECT Statement

Lecture 95 UPDATE Statement

Lecture 96 DELETE Statement

Lecture 97 SQL Indexes

Lecture 98 Introduction to MySQL Table Joins

Lecture 99 MySQL INNER Join

Lecture 100 MySQL LEFT Join

Lecture 101 MySQL RIGHT Join

Lecture 102 MySQL SELF Join

Lecture 103 Introduction to MySQL Views

Lecture 104 Creating Views

Lecture 105 Querying Views

Lecture 106 Modifying Views

Lecture 107 Dropping Views

Lecture 108 Introduction to Sub queries

Lecture 109 Nested Sub queries

Lecture 110 What are derived Tables

Lecture 111 Grouping Rows of Data using GROUP BY Clause

Lecture 112 Filtering Groups of Data using HAVING Clause

Lecture 113 Sorting Data using ORDER BY Clause

Lecture 114 Filtering Rows of Data using WHERE Clause

Lecture 115 Introduction to aggregate Functions

Lecture 116 The AVG Function

Lecture 117 The COUNT Function

Lecture 118 The SUM Function

Lecture 119 The MIN Function

Lecture 120 The MAX Function

Lecture 121 Filtering data using BETWEEN Command

Lecture 122 Filtering data using IN Command

Lecture 123 Filtering data using LIKE Command

Lecture 124 Filtering data using UNION Command

Section 8: Alteryx for Data Manipulation

Lecture 125 Introduction to Alteryx

Lecture 126 Overview of Alteryx

Lecture 127 Creating a basic workflow

Lecture 128 Connecting to Data

Lecture 129 Viewing Data

Lecture 130 Real-world Data Manipulation Project Using SQL

Lecture 131 Transforming Data

Lecture 132 Adding a new column

Lecture 133 Filtering Data

Lecture 134 Blend and Analyze Data

Lecture 135 Alteryx User Interface and Workflow Canvas

Lecture 136 Splitting Columns

Lecture 137 Analyzing data based on a column

Lecture 138 Data Import and Export in Alteryx

Lecture 139 Reading Data into Alteryx from Various Sources

Lecture 140 Writing Data from Alteryx to Various Formats

Lecture 141 Data Manipulation with Alteryx

Lecture 142 Data Preprocessing and Cleaning in Alteryx

Lecture 143 Handling Missing Data

Lecture 144 Formatting Data

Lecture 145 Data Wrangling with Alteryx

Lecture 146 Data Transformation in Alteryx

Lecture 147 Pivoting and Unpivoting Data

Lecture 148 Data Binning and Grouping

Lecture 149 Creating Calculated Fields

Lecture 150 Predictive Analysis Tools in Alteryx

Lecture 151 Spatial and Time Series Analysis

Lecture 152 Creating Macros in Alteryx

Lecture 153 Real-world Data Manipulation Project Using Alteryx

Section 9: Data Manipulation Pipeline with Python, Pandas, R, SQL, and Alteryx

Lecture 154 Designing Effective Data Manipulation Pipelines

Lecture 155 Use Case: Integrating All Tools in a Single Workflow

Section 10: Capstone Project: Full Integration

Lecture 156 Real-World Project requiring the use of Python, Pandas, R, SQL, and Alteryx

Beginners who want to start a career in data analysis or data science.,Data professionals looking to expand their skill set by learning new tools and techniques.,Anyone interested in learning how to manipulate and analyze data using Python, R, SQL, and Alteryx.