Data Analyst All-in-1: Excel, Python, PowerBI and ChatGPT
Data Analyst All-in-1: Excel, Python, PowerBI and ChatGPT, available at $54.99, has an average rating of 4.65, with 232 lectures, 96 quizzes, based on 36 reviews, and has 1109 subscribers.
You will learn about Gain proficiency in Excel, Python, Power BI, and ChatGPT to prepare for a data analyst career. Utilize ChatGPT for advanced data manipulation, pivot tables, and conditional logic. Apply ChatGPT for predictive analytics, including random forest regressor and other machine learning models. Learn essential facts and theories in data analysis, statistical analysis, hypothesis testing, and machine learning. Explore advanced Excel techniques like PivotTables, Data Analysis ToolPak, and interactive dashboards. Grasp Python basics, including variables, data types, lists, dictionaries, dataframes, and functions. Master Python for data cleaning, manipulation, analysis, transformation, and preprocessing. Use Python for data visualization, exploratory data analysis, statistical analysis, and machine learning. Learn Power BI for data manipulation, analysis, and creating insightful dashboards. Create professional, informative, and visually appealing dashboards in Power BI. This course is ideal for individuals who are Anyone interested to learn data analytics. It is particularly useful for Anyone interested to learn data analytics.
Enroll now: Data Analyst All-in-1: Excel, Python, PowerBI and ChatGPT
Summary
Title: Data Analyst All-in-1: Excel, Python, PowerBI and ChatGPT
Price: $54.99
Average Rating: 4.65
Number of Lectures: 232
Number of Quizzes: 96
Number of Published Lectures: 231
Number of Published Quizzes: 96
Number of Curriculum Items: 336
Number of Published Curriculum Objects: 335
Original Price: $109.99
Quality Status: approved
Status: Live
What You Will Learn
- Gain proficiency in Excel, Python, Power BI, and ChatGPT to prepare for a data analyst career.
- Utilize ChatGPT for advanced data manipulation, pivot tables, and conditional logic.
- Apply ChatGPT for predictive analytics, including random forest regressor and other machine learning models.
- Learn essential facts and theories in data analysis, statistical analysis, hypothesis testing, and machine learning.
- Explore advanced Excel techniques like PivotTables, Data Analysis ToolPak, and interactive dashboards.
- Grasp Python basics, including variables, data types, lists, dictionaries, dataframes, and functions.
- Master Python for data cleaning, manipulation, analysis, transformation, and preprocessing.
- Use Python for data visualization, exploratory data analysis, statistical analysis, and machine learning.
- Learn Power BI for data manipulation, analysis, and creating insightful dashboards.
- Create professional, informative, and visually appealing dashboards in Power BI.
Who Should Attend
- Anyone interested to learn data analytics.
Target Audiences
- Anyone interested to learn data analytics.
Kickstart your career as a Data Analyst with our comprehensive all-in-one course, designed to provide you with a solid foundation and hands-on experience using the top five data analytics tools: Excel, Python, Power BI, and ChatGPT. This course is tailored to equip you with the essential skills and knowledge to excel in the fast-paced world of data analysis.
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Python will be your next tool, where you’ll explore everything from the basics—like variables, data types, and functions—to more advanced concepts like data cleaning, transformation, visualization, and even building machine learning models.
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Our course also introduces the revolutionary capabilities of ChatGPT, where you’ll learn how to leverage artificial intelligence for advanced data manipulation tasks, predictive analytics, and generating valuable business insights. Discover how GPT and other AI tools can be integrated into your data workflows to enhance analysis and decision-making.
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You’ll master Excel, where you’ll learn to clean, manipulate, and analyze data using advanced techniques such as PivotTables, the Data Analysis ToolPak, and interactive dashboards.
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Finally, you’ll harness the power of Power BI to transform raw data into insightful, visually appealing dashboards that tell a compelling story. By the end of the course, you will have completed three capstone projects, including bank churn analysis, sports data analytics, and website performance analysis, to showcase your new skills.
This course is perfect for aspiring data analysts, professionals looking to upskill, or anyone interested in leveraging the power of ChatGPT and other tools such as, Excel, Python and Power BI in data analysis to drive business success.
Course Curriculum
Chapter 1: Data Analysis, Statistics & Machine Learning Basics
Lecture 1: Data analysis definition, types and examples
Lecture 2: Key components of data analysis
Lecture 3: Connect with my youtube channel
Lecture 4: Get my special handbooks
Lecture 5: Various sources of collecting data
Lecture 6: Population v/s sample and its methods
Lecture 7: Why you cannot ignore cleaning your data
Lecture 8: Various aspects of data cleaning
Lecture 9: Various aspects of Joining datasets
Lecture 10: Adding extra data with concatenation
Lecture 11: EDA for generating significant insights
Lecture 12: Methods of exploratory data analysis Part 1
Lecture 13: Methods of exploratory data analysis Part 2
Lecture 14: Methods of exploratory data analysis Part 3
Lecture 15: The application of statistical test
Lecture 16: Types of statistical data analysis
Lecture 17: Inferential statistics Part 1 – T-tests and ANOVA
Lecture 18: Inferential statistics Part 2 – Relationships measures
Lecture 19: Inferential statistics Part 3 – Linear regression
Lecture 20: Hypothesis testing for inferential statistics
Lecture 21: Selecting statistical test and assumption testing
Lecture 22: Confidence level, significance level, p-value
Lecture 23: Making decision and conclusion on findings
Lecture 24: Complete statistical analysis and hypothesis testing
Lecture 25: Transforming data for improved analysis
Lecture 26: Techniques for data transformation Part 1
Lecture 27: Techniques for data transformation Part 2
Lecture 28: ML for data analysis and decision-making
Lecture 29: Widely used ML methods in the data analytics
Lecture 30: Steps in developing machine learning model
Lecture 31: Visualizing data for the best insight delivery
Lecture 32: Several methods of data visualization Part 1
Lecture 33: Several methods of data visualization Part 2
Lecture 34: Several methods of data visualization Part 3
Chapter 2: Python for A-Z Data Analytics & Machine Learning
Lecture 1: Extra note on python data analysis
Lecture 2: Resources used in the course
Lecture 3: Installing Python and Jupyter Notebook – Mac
Lecture 4: Installing Python and Jupyter Notebook – Windows
Lecture 5: More alternative methods – Check the article
Lecture 6: Getting started with first python code
Lecture 7: Assigning variable names correctly
Lecture 8: Various data types and data structures
Lecture 9: Converting and casting data types
Lecture 10: Starting with Variables to Data Types
Lecture 11: Arithmetic operators (+, -, *, /, %, **)
Lecture 12: Comparison operators (>, <, >=, <=, ==, !=)
Lecture 13: Logical operators (and, or, not)
Lecture 14: Operators in Python Programming
Lecture 15: Lists: creation, indexing, slicing, modifying
Lecture 16: Sets: unique elements, operations
Lecture 17: Dictionaries: key-value pairs, methods
Lecture 18: Several data structures
Lecture 19: Conditional statements (if, elif, else)
Lecture 20: Nested logical expressions in conditions
Lecture 21: Looping structures (for loops, while loops)
Lecture 22: Defining, creating, and calling functions
Lecture 23: Conditionals Looping and Functions
Lecture 24: Preparing notebook and loading data
Lecture 25: Identifying missing or null values
Lecture 26: Method of missing value imputation
Lecture 27: Exploring data types in a dataframe
Lecture 28: Dealing with inconsistent values
Instructors
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Analytix AI
Unleashing the Power of Data with AI for Informed Insights.
Rating Distribution
- 1 stars: 0 votes
- 2 stars: 0 votes
- 3 stars: 1 votes
- 4 stars: 6 votes
- 5 stars: 29 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!
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