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Data Manipulation with Python, Pandas, R ,SQL and Alteryx

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Data Manipulation with Python, Pandas, R ,SQL and Alteryx, available at $34.99, has an average rating of 3, with 156 lectures, based on 2 reviews, and has 1010 subscribers.

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You will learn about 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. This course is ideal for individuals who are Beginners who want to start a career in data analysis or data science. or Data professionals looking to expand their skill set by learning new tools and techniques. or Anyone interested in learning how to manipulate and analyze data using Python, R, SQL, and Alteryx. It is particularly useful for Beginners who want to start a career in data analysis or data science. or Data professionals looking to expand their skill set by learning new tools and techniques. or Anyone interested in learning how to manipulate and analyze data using Python, R, SQL, and Alteryx.

Enroll now: Data Manipulation with Python, Pandas, R ,SQL and Alteryx

Summary

Title: Data Manipulation with Python, Pandas, R ,SQL and Alteryx

Price: $34.99

Average Rating: 3

Number of Lectures: 156

Number of Published Lectures: 156

Number of Curriculum Items: 156

Number of Published Curriculum Objects: 156

Original Price: $59.99

Quality Status: approved

Status: Live

What You Will 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.

Who Should Attend

  • 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.

Target Audiences

  • 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.

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.

Course Curriculum

Chapter 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

Chapter 2: Python Environment Setup

Lecture 1: Introduction to Jupyter Notebook

Lecture 2: Installing Jupyter Notebook

Lecture 3: Running Jupyter Notebook Server

Lecture 4: Common Jupyter Notebook Commands

Lecture 5: Jupyter Notebook Components

Lecture 6: The Notebook Dashboard

Lecture 7: Notebook User Interface

Lecture 8: Creating a new Notebook

Chapter 3: Python Fundamentals

Lecture 1: Python Expressions

Lecture 2: Python Statements

Lecture 3: Python Comments

Lecture 4: Python Data Types

Lecture 5: Casting Data Types

Lecture 6: Python Variables

Lecture 7: Python List

Lecture 8: Python Tuples

Lecture 9: Python Dictionaries

Lecture 10: Python Operators

Lecture 11: Python Conditional Statements

Lecture 12: Python Loops

Lecture 13: Python Functions

Chapter 4: Python and Pandas for Data Manipulation

Lecture 1: Python for Data Manipulation

Lecture 2: Introduction to Pandas

Lecture 3: Introduction to Pandas Library

Lecture 4: Tabular Data

Lecture 5: Exploring Pandas DataFrame

Lecture 6: Manipulating Pandas DataFrame

Lecture 7: What is data cleaning

Lecture 8: Data Cleaning process

Lecture 9: Series and DataFrame

Lecture 10: Loading Data into DataFrame

Lecture 11: Data Manipulation with Pandas

Lecture 12: Data Cleaning with Pandas

Lecture 13: Data Wrangling and Transformation

Lecture 14: Aggregation and Grouping

Lecture 15: Merge, Join, and Concatenate

Chapter 5: R for Data Manipulation

Lecture 1: Basics for Data Manipulation

Lecture 2: Introduction to R and RStudio

Lecture 3: Installing R on Windows

Lecture 4: Installing R on Macs

Lecture 5: Installing R Studio on Windows

Lecture 6: Installing R Studio on Macs

Lecture 7: Exploring R Studio Interface

Lecture 8: Creating a new project in R Studio

Lecture 9: Real-world Data Manipulation Project Using Python and Pandas

Lecture 10: What are packages

Lecture 11: How to install Packages

Lecture 12: Loading Packages

Lecture 13: Importing data into R Studio

Lecture 14: Reading CSV data with R

Lecture 15: Selecting a subset of data

Lecture 16: Performing multiple operations with Pipe Operator

Lecture 17: Cleaning Columns

Lecture 18: Creating new columns from existing Columns

Lecture 19: Create another R Project

Lecture 20: Load data into new project

Lecture 21: What is data wrangling

Lecture 22: Perform data wrangling

Lecture 23: Create a scatter plot

Lecture 24: Create a bar graph

Lecture 25: Basic Data Types in R

Lecture 26: Control Structures and Functions in R

Lecture 27: Data Manipulation with dplyr in R

Lecture 28: Introduction to dplyr package

Lecture 29: Filtering and Selecting Data with dplyr

Lecture 30: Arrange, Mutate, Summarize and Group By functions

Lecture 31: Data Manipulation with tidyr in R

Lecture 32: Introduction to tidyr package

Lecture 33: Reshape data with gather and spread functions

Lecture 34: Unite and separate columns

Chapter 6: MySQL Database Server Server

Lecture 1: What is MySQL

Lecture 2: MySQL Installation on Windows

Lecture 3: What is MySQL Workbench

Lecture 4: MySQL Installation on Macs

Lecture 5: MySQL Workbench installation on Macs

Lecture 6: Basic Database Concepts

Lecture 7: What is a Schema

Lecture 8: Database Schema

Lecture 9: MySQL Data Types

Lecture 10: Real-world Data Manipulation Project Using R

Chapter 7: SQL for Data Manipulation

Lecture 1: SQL Basics

Lecture 2: Introduction to SQL

Lecture 3: SQL Operators

Lecture 4: CREATE Database

Lecture 5: CREATE Table

Lecture 6: INSERT Data into Table

Instructors

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Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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