Python for Data Analysis: Projects to Power Your Resume

Let's dive into the world of Python development with this lecture focusing on Spyder, a powerful Integrated Development Environment (IDE) for Python. You'll receive a comprehensive introduction to Spyder's interface, features, and tools that are essential for data analysis. This lecture is designed to familiarize you with the IDE, highlighting how to navigate its layout, use its interactive console, and manage scripts effectively.

This lecture serves as an essential primer on Python, guiding you through the language's basics with a hands-on approach. You'll start by understanding the structure of a simple Python script, including comments, and then dive into the core concepts of variables and data types. You will learn how to assign and work with different types of data, such as integers, floating-point numbers, booleans, and strings. The lecture emphasizes practical examples, including displaying output and checking variable types, to build a strong foundational understanding of Python. Ideal for beginners or as a refresher, this session is crucial for anyone looking to gain confidence in Python programming and prepare for more advanced topics in data analysis.

In this lecture, you'll delve deeper into the fundamental aspects of Python programming. The session covers essential concepts such as conditional statements (if-else), lists, tuples, sets, and dictionaries, emphasizing their creation, manipulation, and access techniques. You'll learn how to use conditional logic to control the flow of your programs, manage collections of data in various structures, and understand the properties and applications of each type of collection. This comprehensive overview is crucial for grasping how Python handles data structures and prepares you for more complex programming challenges ahead. Whether you're solidifying your basics or exploring these concepts for the first time, this lecture provides the foundational skills necessary for effective Python programming in data analysis.

Embark on your journey into the world of numerical computing with Python in this lecture dedicated to NumPy, one of the most pivotal libraries for data analysis. This session is designed to introduce you to the core functionalities of NumPy, a library that provides support for large, multi-dimensional arrays and matrices, along with a collection of high-level mathematical functions to operate on these arrays.

Delve into the statistical capabilities of NumPy in this insightful lecture, where you'll learn how to use this powerful library for basic statistical analysis. This session is tailored to provide a comprehensive introduction to essential statistical functions available in NumPy, which is integral for any data analysis task.

Unlock the potential of NumPy arrays with this focused lecture on indexing and slicing, essential techniques for data manipulation in Python. This session is crafted to deepen your understanding of how to access and modify specific elements, rows, and columns within NumPy arrays, a skill fundamental to any data analyst.

Embark on your data analysis journey with this introductory lecture on Pandas, a cornerstone library in Python for data manipulation and analysis. In this session, you'll be introduced to the fundamental concepts and structures of Pandas, primarily focusing on its two primary data structures: Series and DataFrames.

This lecture takes you further into the world of Pandas, focusing on accessing and manipulating data within DataFrames. As a key part of data analysis in Python, understanding how to efficiently work with DataFrame structures is crucial.

In this lecture, you'll delve into the powerful 'groupby' functionality in Pandas, learning how to group data based on one or more columns and apply aggregation functions to these groups. We'll explore how to calculate statistics such as sums, averages, and counts on grouped data, providing insights into trends and patterns.

In this lecture, you will learn the art of merging data using Pandas, an essential skill for combining and analyzing data from multiple sources. Merging data is a fundamental aspect of data analysis, especially when dealing with complex datasets that need to be consolidated for comprehensive insights.

In this lecture we look at our first project - analyzing Amazon Sales Data.

A merchant selling goods on Amazon wants a small analysis to be done. On the amazon portal, they export their sales data called sales_data.xlsx. The datasets can be found in the resources section.

The merchant wants to have a look at the following

• How many sales have they made with amounts more than 1000

• How many sales have they made that belong to the Category "Tops" and have a Quantity of 3.

• The Total Sales by Category

• Average Amount by Category and Status

• Total Sales by Fulfilment and Shipment Type

They would like the last two tables exported to send to their investors.

In this lecture we look at our first project - analyzing Amazon Sales Data. Here we learn how to import, explore and clean data.

Datasets can be found in the resources section in the lecture called "Analyzing Sales Data - Introduction"

In this lecture we look at our first project - analyzing Amazon Sales Data. Here we learn how to aggregate sales data.

Datasets can be found in the resources section in the lecture called "Analyzing Sales Data - Introduction"

In this lecture we look at our first project - analyzing Amazon Sales Data. Here we learn how to rename columns and export data.

Datasets can be found in the resources section in the lecture called "Analyzing Sales Data - Introduction"

In this lecture we look at our first project - analyzing Amazon Sales Data. Here we learn how to export our code to github.

Datasets can be found in the resources section in the lecture called "Analyzing Sales Data - Introduction"

In this lecture we are doing Project 2 - Analyzing E-commerce orders! The data is in the resources section.

Our client is called Olist, a Brazilian e-commerce store. They would like an analysis on the orders bought by their customers. Olist has three datasets related to the e-commerce store:

• orders.xlsx,

• order_payment.xlsx,

• and customers.xlsx.

They have requested the following as they are worried about too many customers buying on credit

• Join all three datasets and do some light cleaning

• They would like to see the range of payment amounts by payment type like credit card, debit card etc. They want to know if customers paying with credit card, have a higher payment value.

• They would like to see the payment value by payment type for every month year

• They would also like to see the total payment value by month

In this lecture we are doing Project 2 - Analyzing E-commerce orders! The data is in the resources section. In this lecture we look at working directories.

Our client is called Olist, a Brazilian e-commerce store. They would like an analysis on the orders bought by their customers. Olist has three datasets related to the e-commerce store:

• orders.xlsx,

• order_payment.xlsx,

• and customers.xlsx.

They have requested the following as they are worried about too many customers buying on credit

• Join all three datasets and do some light cleaning

• They would like to see the range of payment amounts by payment type like credit card, debit card etc. They want to know if customers paying with credit card, have a higher payment value.

• They would like to see the payment value by payment type for every month year

• They would also like to see the total payment value by month

In this lecture we are doing Project 2 - Analyzing E-commerce orders! The data is in the resources of the lecture called "AnalyzingE-commerce orders". In this lecture we look at loading data and checking quality.

Our client is called Olist, a Brazilian e-commerce store. They would like an analysis on the orders bought by their customers. Olist has three datasets related to the e-commerce store:

• orders.xlsx,

• order_payment.xlsx,

• and customers.xlsx.

They have requested the following as they are worried about too many customers buying on credit

• Join all three datasets and do some light cleaning

• They would like to see the range of payment amounts by payment type like credit card, debit card etc. They want to know if customers paying with credit card, have a higher payment value.

• They would like to see the payment value by payment type for every month year

• They would also like to see the total payment value by month

In this lecture we are doing Project 2 - Analyzing E-commerce orders! The data is in the resources of the lecture called "AnalyzingE-commerce orders". In this lecture we look at handling missing data.

Our client is called Olist, a Brazilian e-commerce store. They would like an analysis on the orders bought by their customers. Olist has three datasets related to the e-commerce store:

• orders.xlsx,

• order_payment.xlsx,

• and customers.xlsx.

They have requested the following as they are worried about too many customers buying on credit

• Join all three datasets and do some light cleaning

• They would like to see the range of payment amounts by payment type like credit card, debit card etc. They want to know if customers paying with credit card, have a higher payment value.

• They would like to see the payment value by payment type for every month year

• They would also like to see the total payment value by month

In this lecture we are doing Project 2 - Analyzing E-commerce orders! The data is in the resources of the lecture called "AnalyzingE-commerce orders". In this lecture we look at duplicate data.

Our client is called Olist, a Brazilian e-commerce store. They would like an analysis on the orders bought by their customers. Olist has three datasets related to the e-commerce store:

• orders.xlsx,

• order_payment.xlsx,

• and customers.xlsx.

They have requested the following as they are worried about too many customers buying on credit

• Join all three datasets and do some light cleaning

• They would like to see the range of payment amounts by payment type like credit card, debit card etc. They want to know if customers paying with credit card, have a higher payment value.

• They would like to see the payment value by payment type for every month year

• They would also like to see the total payment value by month

In this lecture we are doing Project 2 - Analyzing E-commerce orders! The data is in the resources of the lecture called "AnalyzingE-commerce orders". In this lecture we look at filtering data on Python.

Our client is called Olist, a Brazilian e-commerce store. They would like an analysis on the orders bought by their customers. Olist has three datasets related to the e-commerce store:

• orders.xlsx,

• order_payment.xlsx,

• and customers.xlsx.

They have requested the following as they are worried about too many customers buying on credit

• Join all three datasets and do some light cleaning

• They would like to see the range of payment amounts by payment type like credit card, debit card etc. They want to know if customers paying with credit card, have a higher payment value.

• They would like to see the payment value by payment type for every month year

• They would also like to see the total payment value by month

In this lecture we are doing Project 2 - Analyzing E-commerce orders! The data is in the resources of the lecture called "AnalyzingE-commerce orders". In this lecture we look at merging and joining DataFrames.

Our client is called Olist, a Brazilian e-commerce store. They would like an analysis on the orders bought by their customers. Olist has three datasets related to the e-commerce store:

• orders.xlsx,

• order_payment.xlsx,

• and customers.xlsx.

They have requested the following as they are worried about too many customers buying on credit

• Join all three datasets and do some light cleaning

• They would like to see the range of payment amounts by payment type like credit card, debit card etc. They want to know if customers paying with credit card, have a higher payment value.

• They would like to see the payment value by payment type for every month year

• They would also like to see the total payment value by month

In this lecture we are doing Project 2 - Analyzing E-commerce orders! The data is in the resources of the lecture called "AnalyzingE-commerce orders". In this lecture we look at create data visuals.

Our client is called Olist, a Brazilian e-commerce store. They would like an analysis on the orders bought by their customers. Olist has three datasets related to the e-commerce store:

• orders.xlsx,

• order_payment.xlsx,

• and customers.xlsx.

They have requested the following as they are worried about too many customers buying on credit

• Join all three datasets and do some light cleaning

• They would like to see the range of payment amounts by payment type like credit card, debit card etc. They want to know if customers paying with credit card, have a higher payment value.

• They would like to see the payment value by payment type for every month year

• They would also like to see the total payment value by month

In this lecture we are doing Project 2 - Analyzing E-commerce orders! The data is in the resources of the lecture called "AnalyzingE-commerce orders". In this lecture we look at editing and customizing plots in Python.

Our client is called Olist, a Brazilian e-commerce store. They would like an analysis on the orders bought by their customers. Olist has three datasets related to the e-commerce store:

• orders.xlsx,

• order_payment.xlsx,

• and customers.xlsx.

They have requested the following as they are worried about too many customers buying on credit

• Join all three datasets and do some light cleaning

• They would like to see the range of payment amounts by payment type like credit card, debit card etc. They want to know if customers paying with credit card, have a higher payment value.

• They would like to see the payment value by payment type for every month year

• They would also like to see the total payment value by month

In this lecture we are doing Project 2 - Analyzing E-commerce orders! The data is in the resources of the lecture called "AnalyzingE-commerce orders". In this lecture we look at creating a scatter plot in Python.

Our client is called Olist, a Brazilian e-commerce store. They would like an analysis on the orders bought by their customers. Olist has three datasets related to the e-commerce store:

• orders.xlsx,

• order_payment.xlsx,

• and customers.xlsx.

They have requested the following as they are worried about too many customers buying on credit

• Join all three datasets and do some light cleaning

• They would like to see the range of payment amounts by payment type like credit card, debit card etc. They want to know if customers paying with credit card, have a higher payment value.

• They would like to see the payment value by payment type for every month year

• They would also like to see the total payment value by month

In this lecture we are doing Project 2 - Analyzing E-commerce orders! The data is in the resources of the lecture called "AnalyzingE-commerce orders". In this lecture we look at creating a stacked bar chart.

Our client is called Olist, a Brazilian e-commerce store. They would like an analysis on the orders bought by their customers. Olist has three datasets related to the e-commerce store:

• orders.xlsx,

• order_payment.xlsx,

• and customers.xlsx.

They have requested the following as they are worried about too many customers buying on credit

• Join all three datasets and do some light cleaning

• They would like to see the range of payment amounts by payment type like credit card, debit card etc. They want to know if customers paying with credit card, have a higher payment value.

• They would like to see the payment value by payment type for every month year

• They would also like to see the total payment value by month

In this lecture we are doing Project 2 - Analyzing E-commerce orders! The data is in the resources of the lecture called "AnalyzingE-commerce orders". In this lecture we look at creating boxplots.

Our client is called Olist, a Brazilian e-commerce store. They would like an analysis on the orders bought by their customers. Olist has three datasets related to the e-commerce store:

• orders.xlsx,

• order_payment.xlsx,

• and customers.xlsx.

They have requested the following as they are worried about too many customers buying on credit

• Join all three datasets and do some light cleaning

• They would like to see the range of payment amounts by payment type like credit card, debit card etc. They want to know if customers paying with credit card, have a higher payment value.

• They would like to see the payment value by payment type for every month year

• They would also like to see the total payment value by month

In this lecture we are doing Project 2 - Analyzing E-commerce orders! The data is in the resources of the lecture called "AnalyzingE-commerce orders". In this lecture we look at creating subplots.

Our client is called Olist, a Brazilian e-commerce store. They would like an analysis on the orders bought by their customers. Olist has three datasets related to the e-commerce store:

• orders.xlsx,

• order_payment.xlsx,

• and customers.xlsx.

They have requested the following as they are worried about too many customers buying on credit

• Join all three datasets and do some light cleaning

• They would like to see the range of payment amounts by payment type like credit card, debit card etc. They want to know if customers paying with credit card, have a higher payment value.

• They would like to see the payment value by payment type for every month year

• They would also like to see the total payment value by month

In this lecture we analyze Pizza sales. The data is in the resources section.

Our client is a Pizza takeout. They had a part time data analyst who cleaned the data. Unfortunately, the analyst is leaving and has supplied you with a list of tasks you need to do it order to clean the data. The data is below.

• pizza_sales.xlsx

• pizza_size.csv

• pizza_category.csv

You must do the following

• Import the files and describe the data

• Do row filtering to only include unit prices greater than 35

• Create a discount column which is calculated at 10% of the price

• Drop a row with index label 13341 as the pizza id is incorrect.

• Drop multiple rows by index labels with id 2614 and 2813 as dates are null

• Drop unit price

• drop order_time, pizza_id

• Combine the size and category data

• Add the other data which was inputted manually when the system was down. It is sitting in an excel sheet called another_pizza_sales

• Add the pizza sales voucher data

• Create pizza full name column which is pizza name and ingredients

• Add one row of data at the end which was forgotten

• Add one row of data at the beginning which was forgotten

• Change text to title case for ingredients and replce feta cheese with mozzarella

• Create a box plot showing total price by ctaegory

• Export the final table and box plot

In this lecture we explore the data and look at descriptive statistics. The data is in the resources section.

Our client is a Pizza takeout. They had a part time data analyst who cleaned the data. Unfortunately, the analyst is leaving and has supplied you with a list of tasks you need to do it order to clean the data. The data is below.

• pizza_sales.xlsx

• pizza_size.csv

• pizza_category.csv

You must do the following

• Import the files and describe the data

• Do row filtering to only include unit prices greater than 35

• Create a discount column which is calculated at 10% of the price

• Drop a row with index label 13341 as the pizza id is incorrect.

• Drop multiple rows by index labels with id 2614 and 2813 as dates are null

• Drop unit price

• drop order_time, pizza_id

• Combine the size and category data

• Add the other data which was inputted manually when the system was down. It is sitting in an excel sheet called another_pizza_sales

• Add the pizza sales voucher data

• Create pizza full name column which is pizza name and ingredients

• Add one row of data at the end which was forgotten

• Add one row of data at the beginning which was forgotten

• Change text to title case for ingredients and replce feta cheese with mozzarella

• Create a box plot showing total price by ctaegory

• Export the final table and box plot

In this lecture we deal with rows and columns in Pandas. The data is available in the resources section of the lecture "Analyzing Pizza Sales and Importing Data|

In this lecture we look at Understanding Indexing in DataFrames. The data is available in the resources section of the lecture "Analyzing Pizza Sales and Importing Data|

In this lecture we look at truncating DataFrames and Series. The data is available in the resources section of the lecture "Analyzing Pizza Sales and Importing Data"

In this lecture we look at filtering DataFrames. The data is available in the resources section of the lecture "Analyzing Pizza Sales and Importing Data"

In this lecture we look at working with missing data. The data is available in the resources section of the lecture "Analyzing Pizza Sales and Importing Data"

In this lecture we look at deleting specific rows and columns in a DataFrame. The data is available in the resources section of the lecture "Analyzing Pizza Sales and Importing Data"

In this lecture we look at sorting DataFrames. The data is available in the resources section of the lecture "Analyzing Pizza Sales and Importing Data"

In this lecture we look at grouping on Python. The data is available in the resources section of the lecture "Analyzing Pizza Sales and Importing Data"

In this lecture we look at merging and concatenating. The data is available in the resources section of the lecture "Analyzing Pizza Sales and Importing Data"

In this lecture we look at changing cases in Python. The data is available in the resources section of the lecture "Analyzing Pizza Sales and Importing Data"

In this lecture we look at replacing text in Dataframe columns. The data is available in the resources section of the lecture "Analyzing Pizza Sales and Importing Data"

In this lecture we look at removing whitespaces from columns. The data is available in the resources section of the lecture "Analyzing Pizza Sales and Importing Data"

In this lecture we look at generating a boxplot. The data is available in the resources section of the lecture "Analyzing Pizza Sales and Importing Data"

In this lecture, we start our loan analysis. The data can be found in the resources section.

As a data analyst for LendingClub you are tasked with the goal of building a simple model to predict whether or not a borrower will pay back their loan in full. This prediction will be based on various details about the loan and the borrower such as the purpose of the loan, interest rate, FICO score, debt-to-income ratio, and other factors. You’re also responsible for developing some visualizations.

There are two data sets available

• loandataset.xlsx

• customer_data.csv

In this lecture, we start our loan analysis. The data can be found in the resources section.

As a data analyst for LendingClub you are tasked with the goal of building a simple model to predict whether or not a borrower will pay back their loan in full. This prediction will be based on various details about the loan and the borrower such as the purpose of the loan, interest rate, FICO score, debt-to-income ratio, and other factors. You’re also responsible for developing some visualizations.

There are two data sets available

• loandataset.xlsx

• customer_data.csv

In this lecture, we join our data. The data can be found in the resources section of the lecture called "Loan Analysis Overview - Introduction".

As a data analyst for LendingClub you are tasked with the goal of building a simple model to predict whether or not a borrower will pay back their loan in full. This prediction will be based on various details about the loan and the borrower such as the purpose of the loan, interest rate, FICO score, debt-to-income ratio, and other factors. You’re also responsible for developing some visualizations.

There are two data sets available

• loandataset.xlsx

• customer_data.csv

In this lecture, we clean our data. The data can be found in the resources section of the lecture called "Loan Analysis Overview - Introduction".

As a data analyst for LendingClub you are tasked with the goal of building a simple model to predict whether or not a borrower will pay back their loan in full. This prediction will be based on various details about the loan and the borrower such as the purpose of the loan, interest rate, FICO score, debt-to-income ratio, and other factors. You’re also responsible for developing some visualizations.

There are two data sets available

• loandataset.xlsx

• customer_data.csv

In this lecture, we are introduced to functions. The data can be found in the resources section of the lecture called "Loan Analysis Overview - Introduction".

As a data analyst for LendingClub you are tasked with the goal of building a simple model to predict whether or not a borrower will pay back their loan in full. This prediction will be based on various details about the loan and the borrower such as the purpose of the loan, interest rate, FICO score, debt-to-income ratio, and other factors. You’re also responsible for developing some visualizations.

There are two data sets available

• loandataset.xlsx

• customer_data.csv

In this lecture, we create a function. The data can be found in the resources section of the lecture called "Loan Analysis Overview - Introduction".

As a data analyst for LendingClub you are tasked with the goal of building a simple model to predict whether or not a borrower will pay back their loan in full. This prediction will be based on various details about the loan and the borrower such as the purpose of the loan, interest rate, FICO score, debt-to-income ratio, and other factors. You’re also responsible for developing some visualizations.

There are two data sets available

• loandataset.xlsx

• customer_data.csv

In this lecture, we learn about conditional statements. The data can be found in the resources section of the lecture called "Loan Analysis Overview - Introduction".

As a data analyst for LendingClub you are tasked with the goal of building a simple model to predict whether or not a borrower will pay back their loan in full. This prediction will be based on various details about the loan and the borrower such as the purpose of the loan, interest rate, FICO score, debt-to-income ratio, and other factors. You’re also responsible for developing some visualizations.

There are two data sets available

• loandataset.xlsx

• customer_data.csv

In this lecture, we create a practical function with conditions. The data can be found in the resources section of the lecture called "Loan Analysis Overview - Introduction".

As a data analyst for LendingClub you are tasked with the goal of building a simple model to predict whether or not a borrower will pay back their loan in full. This prediction will be based on various details about the loan and the borrower such as the purpose of the loan, interest rate, FICO score, debt-to-income ratio, and other factors. You’re also responsible for developing some visualizations.

There are two data sets available

• loandataset.xlsx

• customer_data.csv

In this lecture, we work with conditional statements and averages in functions. The data can be found in the resources section of the lecture called "Loan Analysis Overview - Introduction".

As a data analyst for LendingClub you are tasked with the goal of building a simple model to predict whether or not a borrower will pay back their loan in full. This prediction will be based on various details about the loan and the borrower such as the purpose of the loan, interest rate, FICO score, debt-to-income ratio, and other factors. You’re also responsible for developing some visualizations.

There are two data sets available

• loandataset.xlsx

• customer_data.csv

In this lecture, we look at classes in Python. The data can be found in the resources section of the lecture called "Loan Analysis Overview - Introduction".

As a data analyst for LendingClub you are tasked with the goal of building a simple model to predict whether or not a borrower will pay back their loan in full. This prediction will be based on various details about the loan and the borrower such as the purpose of the loan, interest rate, FICO score, debt-to-income ratio, and other factors. You’re also responsible for developing some visualizations.

There are two data sets available

• loandataset.xlsx

• customer_data.csv

In this lecture, we create data visualizations. The data can be found in the resources section of the lecture called "Loan Analysis Overview - Introduction".

As a data analyst for LendingClub you are tasked with the goal of building a simple model to predict whether or not a borrower will pay back their loan in full. This prediction will be based on various details about the loan and the borrower such as the purpose of the loan, interest rate, FICO score, debt-to-income ratio, and other factors. You’re also responsible for developing some visualizations.

There are two data sets available

• loandataset.xlsx

• customer_data.csv

In this lecture, we look at subplots. The data can be found in the resources section of the lecture called "Loan Analysis Overview - Introduction".

As a data analyst for LendingClub you are tasked with the goal of building a simple model to predict whether or not a borrower will pay back their loan in full. This prediction will be based on various details about the loan and the borrower such as the purpose of the loan, interest rate, FICO score, debt-to-income ratio, and other factors. You’re also responsible for developing some visualizations.

There are two data sets available

• loandataset.xlsx

• customer_data.csv

In this lecture, we start our Sentiment Analysis project. The data can be found in the resources section.

A new AI company wants to do some competitor research on ChatGPT. They have managed to scrape data on X.com (Twitter). The dataset provides a glimpse into the online conversation surrounding the ChatGPT language model. The AI company wants to know if the sentiment of ChatGPT is positive or not.

This dataset contains a collection of tweets with the hashtag #chatgpt. It is called chatgpt1.csv

The tweets were scraped from Twitter and cover a range of topics related to the ChatGPT language model. The dataset includes the following information for each tweet:

• Tweet text

• User information (username, user ID, location, etc.)

• Tweet timestamp

• Retweet and favorite count

• Hashtags used in the tweet

• URLs

In this lecture, we load and review the data. The data can be found in the resources section.

A new AI company wants to do some competitor research on ChatGPT. They have managed to scrape data on X.com (Twitter). The dataset provides a glimpse into the online conversation surrounding the ChatGPT language model. The AI company wants to know if the sentiment of ChatGPT is positive or not.

This dataset contains a collection of tweets with the hashtag #chatgpt. It is called chatgpt1.csv

The tweets were scraped from Twitter and cover a range of topics related to the ChatGPT language model. The dataset includes the following information for each tweet:

• Tweet text

• User information (username, user ID, location, etc.)

• Tweet timestamp

• Retweet and favorite count

• Hashtags used in the tweet

• URLs

In this lecture, we detect languages and use try and except. The data can be found in the resources section of the lecture called "Sentiment Analysis - Introduction".

A new AI company wants to do some competitor research on ChatGPT. They have managed to scrape data on X.com (Twitter). The dataset provides a glimpse into the online conversation surrounding the ChatGPT language model. The AI company wants to know if the sentiment of ChatGPT is positive or not.

This dataset contains a collection of tweets with the hashtag #chatgpt. It is called chatgpt1.csv

The tweets were scraped from Twitter and cover a range of topics related to the ChatGPT language model. The dataset includes the following information for each tweet:

• Tweet text

• User information (username, user ID, location, etc.)

• Tweet timestamp

• Retweet and favorite count

• Hashtags used in the tweet

• URLs

In this lecture, we clean text data. The data can be found in the resources section of the lecture called "Sentiment Analysis - Introduction".

A new AI company wants to do some competitor research on ChatGPT. They have managed to scrape data on X.com (Twitter). The dataset provides a glimpse into the online conversation surrounding the ChatGPT language model. The AI company wants to know if the sentiment of ChatGPT is positive or not.

This dataset contains a collection of tweets with the hashtag #chatgpt. It is called chatgpt1.csv

The tweets were scraped from Twitter and cover a range of topics related to the ChatGPT language model. The dataset includes the following information for each tweet:

• Tweet text

• User information (username, user ID, location, etc.)

• Tweet timestamp

• Retweet and favorite count

• Hashtags used in the tweet

• URLs

In this lecture, we develop the sentiment function. The data can be found in the resources section of the lecture called "Sentiment Analysis - Introduction".

A new AI company wants to do some competitor research on ChatGPT. They have managed to scrape data on X.com (Twitter). The dataset provides a glimpse into the online conversation surrounding the ChatGPT language model. The AI company wants to know if the sentiment of ChatGPT is positive or not.

This dataset contains a collection of tweets with the hashtag #chatgpt. It is called chatgpt1.csv

The tweets were scraped from Twitter and cover a range of topics related to the ChatGPT language model. The dataset includes the following information for each tweet:

• Tweet text

• User information (username, user ID, location, etc.)

• Tweet timestamp

• Retweet and favorite count

• Hashtags used in the tweet

• URLs

In this lecture, we create a wordcloud. The data can be found in the resources section of the lecture called "Sentiment Analysis - Introduction".

A new AI company wants to do some competitor research on ChatGPT. They have managed to scrape data on X.com (Twitter). The dataset provides a glimpse into the online conversation surrounding the ChatGPT language model. The AI company wants to know if the sentiment of ChatGPT is positive or not.

This dataset contains a collection of tweets with the hashtag #chatgpt. It is called chatgpt1.csv

The tweets were scraped from Twitter and cover a range of topics related to the ChatGPT language model. The dataset includes the following information for each tweet:

• Tweet text

• User information (username, user ID, location, etc.)

• Tweet timestamp

• Retweet and favorite count

• Hashtags used in the tweet

• URLs

In this lecture, we create a countplot for the sentiment. The data can be found in the resources section of the lecture called "Sentiment Analysis - Introduction".

A new AI company wants to do some competitor research on ChatGPT. They have managed to scrape data on X.com (Twitter). The dataset provides a glimpse into the online conversation surrounding the ChatGPT language model. The AI company wants to know if the sentiment of ChatGPT is positive or not.

This dataset contains a collection of tweets with the hashtag #chatgpt. It is called chatgpt1.csv

The tweets were scraped from Twitter and cover a range of topics related to the ChatGPT language model. The dataset includes the following information for each tweet:

• Tweet text

• User information (username, user ID, location, etc.)

• Tweet timestamp

• Retweet and favorite count

• Hashtags used in the tweet

• URLs

Thanks so much for watching my course!