Data Analysis 2 in 1: Excel & Python for A-Z Data Analysis
- Description
- Curriculum
- FAQ
- Reviews
Chapter 1: Introduction
Welcome to the comprehensive and dynamic course, “Data Analysis 2 in 1: Excel & Python for A-Z Data Analysis.” This meticulously crafted program is designed to empower learners with a versatile skill set, encompassing the efficient data manipulation capabilities of Excel, the scalability and coding flexibility of Python, and the intuitive coding assistance from ChatGPT. As technology continues to evolve, proficiency in multiple tools becomes essential. This course aims to provide a holistic understanding of the data analysis workflow, ensuring that learners can seamlessly transition from Excel to Python, while also adding a touch of AI for an enhanced coding experience.
Chapter 2: Excel Mastery
The course kicks off with a deep dive into Excel, teaching you to wield its powerful features for data cleaning, transformation, and visualization. From managing missing data and outliers to leveraging advanced Excel functions and tools for statistical analysis, you’ll gain a solid foundation in Excel’s capabilities. The focus on interactive dashboard creation using PivotTables, PivotCharts, and various visualization techniques will empower you to present insights in a compelling and user-friendly manner.
Chapter 3: Python Basics and Beyond
Building on your Excel skills, the course introduces Python programming basics. You’ll learn the syntax, data types, and control structures, enabling you to construct simple programs. The emphasis is on practical application – generating, copying/pasting, adjusting, and running code with ease. Python’s ability to handle large datasets becomes evident, making it the tool of choice for scenarios where Excel’s limitations are surpassed. This section ensures you’re proficient in both tools, providing adaptability in real-world data analysis scenarios.
Chapter 4: Statistical Analysis and Interpretation
As the course progresses, you’ll delve into fundamental statistical concepts, applying them using both Excel and Python. Descriptive statistics, inferential statistics, and hypothesis testing are covered comprehensively. You’ll learn not just how to perform these analyses but, crucially, how to interpret and communicate the results effectively. This knowledge forms the backbone of making informed decisions and recommendations based on data-driven insights.
Chapter 5: Real-world Application and Problem-solving
The final section of the course is dedicated to real-world application. You’ll tackle over 60+ data analytical questions, honing your skills in solving practical problems. Value counts, percentage calculations, grouping data, and utilizing advanced statistical techniques become second nature. Emphasis is placed on critical thinking and problem-solving, ensuring that you not only understand the tools and techniques but can confidently apply them to various circumstances. By the course’s conclusion, you’ll be equipped to navigate the complete data analysis workflow with mastery and confidence.
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Install Excel Data Analysis Tool pack (If Necessary) -
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Frequency and percentage analysis -
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Practice file - Frequency and percentage analysis
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Descriptive analysis (mean, std. dev., skewness, etc.)
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Practice file - Descriptive analysis
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Group by analysis in excel pivot table
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Practice file - Group by analysis
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Crosstabulation analysis in excel pivot table
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Practice file - Crosstabulation analysis
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Independent sample t-test
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Practice file - Independent sample t-test
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Paired sample t-test
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Practice file - Paired sample t-test
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Analysis of variance (ANOVA)
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Practice file - ANOVA
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Pearson correlation analysis
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Practice file - Correlation analysis
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Multiple linear regression analysis
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Practice file - Regression analysis
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Your First Python Code: Getting Started
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Your first code. -
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Variables and naming conventions
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Working with variables
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Data types: integers, float, strings, boolean
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Type conversion and casting
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Dealing with data types
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Arithmetic operators (+, -, *, /, %, **)
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Arithmetic operations
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Comparison operators (>, =, <=, ==, !=)
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Comparison operations
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Logical operators (and, or, not)
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Logical operations
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Python Programming Basics – Level 1
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Lists: creation, indexing, slicing, modifying
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Creating and slicing list
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Sets: unique elements, operations
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Operating with sets
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Dictionaries: key-value pairs, methods
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Dealing with dictionaries
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Conditional statements (if, elif, else)
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Working under conditions
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Logical expressions in conditions
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Condition with logical expression
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Looping structures (for loops, while loops)
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Working with looping structure
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Defining, Creating and Calling functions
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Working with functions
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Python Programming Basics – Level 2
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Importing dataset into Jupyter Notebook
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Imputing missing values with SimpleImputer
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Identifying number of missing values
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Dealing with missing values
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Finding and dealing with inconsistent data
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Dealing with inconsistent data
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Identify and assign correct dataset
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Dealing with data types
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Dealing with duplicate values
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Removing duplicated values
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Data Cleaning in Python
I believe that you have loaded the required practice data in the same directory within a jupyter notebook as commanded in Lecture 38. If not, please ensure that you have successfully completed the instructions given in the lecture.
Then, you may proceed to take the QUIZ.
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Sorting and arranging dataset
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Sorting and arranging data
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Conditional Filtering of dataset
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Conditional filtering
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Merging extra data with the dataset
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Merging datasets
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Concatenating variables within dataset
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Concatenating datasets
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Data Manipulation in Python
I believe that you have loaded the required practice data in the same directory within a jupyter notebook as commanded in Lecture 38. If not, please ensure that you have successfully completed the instructions given in the lecture. Additionally, you have to complete the QUIZ 3 successfully to complete this QUIZ.
Then, you may proceed to take the QUIZ.
