Data Analyst in Python, Tableau, SQL ChatGPT with Projects

Data Analyst in Python, Tableau, SQL & ChatGPT with Projects, available at $44.99, has an average rating of 4.68, with 199 lectures, 10 quizzes, based on 84 reviews, and has 1052 subscribers.

You will learn about Understand MySQL and its role in data storage and analysis Gain hands-on experience in using Python libraries such as Pandas and Matplotlib for data manipulation, cleaning, visualization, and data analysis Learn how to effectively use ChatGPT as an assistant in writing code and analyzing data Study the Tableau topics required for the Tableau Certified Data Analyst and Certified Desktop Specialist exams and prepare with hands on tutorials Build your own interactive dashboard in Tableau Engage in practical projects involving SQL, Python, ChatGPT, and Tableau, gaining hands-on experience in each technology. Prepare yourself for an exciting career in Data Analytics This course is ideal for individuals who are Anyone with an interest in Data Analysis or Students looking to learn and practice the topics required for the Tableau Certified Data Analyst exam or Students who want to get a head start in using AI tools such as ChatGPT as an assistant in analyzing data It is particularly useful for Anyone with an interest in Data Analysis or Students looking to learn and practice the topics required for the Tableau Certified Data Analyst exam or Students who want to get a head start in using AI tools such as ChatGPT as an assistant in analyzing data.

Enroll now: Data Analyst in Python, Tableau, SQL & ChatGPT with Projects

Summary

Title: Data Analyst in Python, Tableau, SQL & ChatGPT with Projects

Price: $44.99

Average Rating: 4.68

Number of Lectures: 199

Number of Quizzes: 10

Number of Published Lectures: 199

Number of Published Quizzes: 10

Number of Curriculum Items: 209

Number of Published Curriculum Objects: 209

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will Learn

Understand MySQL and its role in data storage and analysis

Gain hands-on experience in using Python libraries such as Pandas and Matplotlib for data manipulation, cleaning, visualization, and data analysis

Learn how to effectively use ChatGPT as an assistant in writing code and analyzing data

Study the Tableau topics required for the Tableau Certified Data Analyst and Certified Desktop Specialist exams and prepare with hands on tutorials

Build your own interactive dashboard in Tableau

Engage in practical projects involving SQL, Python, ChatGPT, and Tableau, gaining hands-on experience in each technology.

Prepare yourself for an exciting career in Data Analytics

Who Should Attend

Anyone with an interest in Data Analysis

Students looking to learn and practice the topics required for the Tableau Certified Data Analyst exam

Students who want to get a head start in using AI tools such as ChatGPT as an assistant in analyzing data

Target Audiences

Anyone with an interest in Data Analysis

Students looking to learn and practice the topics required for the Tableau Certified Data Analyst exam

Students who want to get a head start in using AI tools such as ChatGPT as an assistant in analyzing data

Are you interested in starting a career in data analytics but have no experience? Become proficient in essential tools such as Python, SQL, Tableau, and ChatGPT with this beginner data analyst course, designed to prepare you with the skills necessary to tackle real-world challenges as a data analyst. Whether you are looking for a career change to a data analyst, or have some experience and want to build on it, this step by step course will help guide you.

What You Will Learn:

Python for Data Analysis: Start from the basics and advance to complex data manipulations with Pandas and Matplotlib.

SQL: Learn to manage and query databases efficiently, from simple commands to complex queries.

ChatGPT for Productivity: Enhance your coding and analysis efficiency with optimized ChatGPT prompts.

Tableau for Visualization: Create impactful visualizations and prepare to become a Tableau Certified Data Analyst.

Hands-On Projects: This is a project-based data analyst course – from analyzing LA crime statistics to exploring sales trends, apply what you’ve learned in practical projects.

Why This Course?

Comprehensive & Practical: Gain hands-on experience with real-world projects that prepare you for the job market.

Accessible to Beginners: No prior experience? No problem! We cater to all skill levels.

Flexible Learning: Study at your pace with lifetime access to all course materials.

Who Should Enroll?

Aspiring data analysts, career switchers, and professionals aiming to upskill in data analysis and visualization.

If you’re ready to start your journey in data analysis, enroll now and begin transforming data into insights!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Download Course Files Here

Chapter 2: Getting Started with Data Analysis in Python

Lecture 1: Download Anaconda

Lecture 2: Introduction to Jupyter Notebook

Lecture 3: Import Pandas and Matplotlib

Lecture 4: Loading CSV Files into Jupyter Notebook

Lecture 5: Markdown and Commenting Out Code

Lecture 6: Introducing Our Datasets

Lecture 7: Essential Python Methods to Understand Datasets

Lecture 8: Renaming Columns in Pandas

Lecture 9: Selecting Specific Columns

Lecture 10: Performing Basic Calculations

Lecture 11: Understanding Standard Deviation and Count

Lecture 12: Descriptive Statistics with the Describe Method

Lecture 13: Analyzing Frequency with Value Counts

Chapter 3: Python – Indexes, Sorting Data, and Introduction to Plotting

Lecture 1: Working with Indexes in Pandas

Lecture 2: Sorting Values in DataFrames

Lecture 3: Accessing Data with Loc and Iloc

Lecture 4: Advanced Indexing: Multi-Indexes and Slices

Lecture 5: From DataFrames to Plots

Chapter 4: Data Cleaning and Filtering Techniques in Pandas using Python

Lecture 1: Handling Missing Values: Dealing with NULLs

Lecture 2: Unique and Nunique Methods

Lecture 3: Filtering Data with the Between Method

Lecture 4: Applying Multiple Filter Conditions

Lecture 5: Advanced Filtering with Isin and Or

Lecture 6: Understanding the Tilde (~) Operator for Bitwise Negation

Lecture 7: Creating and Dropping Columns in DataFrames

Lecture 8: Generating Dynamic Columns

Chapter 5: Date Handling, Grouping and Reshaping Data in Pandas using Python

Lecture 1: Working with Dates: Converting to Datetime

Lecture 2: Replacing Values in a DataFrame

Lecture 3: Type Conversion using astype

Lecture 4: Extracting Date Components with .dt

Lecture 5: Grouping Data using Groupby

Lecture 6: Aggregation with .agg Method

Lecture 7: Reshaping DataFrames with Stack and Unstack

Chapter 6: Visualizing Data with Matplotlib using Python

Lecture 1: Introduction to Matplotlib

Lecture 2: Creating Bar Plots and Adding Annotations

Lecture 3: Visualize Data Distribution with Histograms

Lecture 4: Display Data with Pie Charts

Lecture 5: Explore Relationships with Scatter Plots

Lecture 6: Scatter Plots using World Happiness Dataset

Lecture 7: Visualize Trends with Line Plots

Lecture 8: Structuring Visualizations with Figures

Lecture 9: Organize Plots using Subplot

Lecture 10: Visualizing Sales Data – Part One

Lecture 11: Visualizing Sales Data – Part Two

Lecture 12: Formatting Sales Plots – Part One

Lecture 13: Formatting Sales Plots – Part Two

Chapter 7: Python Project – LA Crime

Lecture 1: Getting Dataset and Importing to Jupyter Notebook

Lecture 2: Understand and Prepare Data – Part One

Lecture 3: Understand and Prepare Data – Part Two

Lecture 4: Feature Understanding & Analysis – Part One

Lecture 5: Feature Understanding & Analysis – Part Two

Lecture 6: Feature Understanding & Analysis – Part Three

Lecture 7: Time Taken to Report Crime

Lecture 8: Average Victim Age per Crime

Lecture 9: Areas with Most Crime

Lecture 10: Crime Count by Time of Day

Chapter 8: SQL

Lecture 1: Downloading MySQL Workbench for Windows

Lecture 2: Downloading MySQL Workbench for Mac

Lecture 3: Introduction to MySQL Workbench

Lecture 4: What is SQL and Data Definition Language (DDL)

Lecture 5: Introduction to SQL: Insert Into Statements

Lecture 6: Importing our Databases

Lecture 7: Understanding the Data in the Database

Lecture 8: Select Data from the Database

Lecture 9: Filtering the Output with Where

Lecture 10: Applying Multiple Filters with In and Not In

Lecture 11: Finding Unique Values using Distinct

Lecture 12: Searching for Text Patterns in Strings using the Like Operator

Lecture 13: An Introduction to Joins

Lecture 14: Combining Tables using Inner Join

Lecture 15: Combining Tables using Left Join

Lecture 16: Combining Tables using Right Join

Lecture 17: Joining a Table to Itself

Lecture 18: Examples and Use Cases of Joins

Lecture 19: Combining Data with Union and Union All

Lecture 20: Introduction to Aggregate Functions

Lecture 21: Grouping Aggregated Data using Group By

Lecture 22: Using Subqueries in SQL

Lecture 23: Practice and Use Cases of Sub Queries

Lecture 24: Grouping Data using a Case Statement

Lecture 25: Transform Data using a Case Statement

Instructors

Graeme Gordon
Data & Insights | SQL | Tableau | Excel | Generative AI

Rating Distribution

1 stars: 0 votes

2 stars: 1 votes

3 stars: 3 votes

4 stars: 26 votes

5 stars: 54 votes

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!