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Keep shoppingData Analysis A-Z: Become Data Analyst in 30 Days
3,876 students
Data Analysis A-Z: Become Data Analyst in 30 Days
Unlock Data Analysis with Python with ChatGPT and Excel. Master Full Work-flow and Become Pro Data Analyst in 30 Days!
Created byAnalytix AI
Last updated 2/2025
English
What you'll learn
- Gain understanding of Python's basic syntax, data types, variables, and operators, enabling you to write simple programs and perform basic operations.
- Learn to utilize control structures like loops and conditional statements such as use if, elif and else to manage program flow effectively.
- Acquire skills in working with fundamental data structures in Python, such as lists, dictionaries, tuples, and sets.
- Learn how to manipulate, access, and modify these structures for diverse programming needs.
- Employ metrics such as counts, percentages, group by, pivot tables, correlation, and regression professionally and realistically.
- Solve more than 20 data analytical questions to practice applying data analysis to various circumstances.
- Emphasize practical application to gain valuable insights from data and create educated judgments and suggestions.
- Master Python for data analysis using industry-standard libraries and tools: pandas, numpy, scipy, scikit-learn etc.
- Master statistical inference (e.g., ANOVA, correlation, regression), draw meaningful findings, and make data-driven decisions.
- Understand and apply techniques for cleaning and preparing raw data in Excel. Learn to identify and handle missing data, outliers, and inconsistencies.
- Utilize Excel functions and tools for data validation and transformation. Explore fundamental statistical concepts and their application in Excel.
- Learn to perform descriptive statistics, inferential statistics, and hypothesis testing using Excel functions and tools.
- Learn to use PivotTables, PivotCharts, and slicers to create dynamic and user-friendly dashboards.
- Explore various data visualization techniques available in Excel, including charts, graphs.
- Design and build interactive dashboards in Excel for effective data visualization.
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Coding Exercises
This course includes our updated coding exercises so you can practice your skills as you learn.
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Course content
31 sections • 87 lectures • 9h 7m total length
Requirements
- No coding Experience is Needed.
- Microsoft Excel
- Mindset to be Successful.
- Dedication to be Data Analyst.
- Intention to Learn Python.
Description
Data Analysis A-Z: Become Data Analyst in 30 Days is an intensive training program designed to equip participants with the essential skills and knowledge required to excel as a data analyst. This comprehensive course covers a wide range of topics, from basic Python programming to advanced statistical analysis techniques using industry-standard tools such as pandas, numpy, and Excel.
Day 1 - 7: Data Analysis with Excel
The week of the bootcamp focuses on data analysis using Microsoft Excel. Participants will learn how to clean and prepare raw data, perform descriptive and inferential statistics, and create dynamic dashboards and visualizations using Excel functions and tools. Topics covered include:
- Cleaning and preparing raw data in Excel
- Handling missing data, outliers, and inconsistencies
- Descriptive and inferential statistics in Excel
- Creating dynamic dashboards with PivotTables and PivotCharts
- Data visualization techniques in Excel (charts, graphs, slicers)
Day 9 - 17: Python Fundamentals
In this week, participants will gain a solid understanding of Python's basic syntax, data types, variables, and operators. They will learn how to write simple programs and perform basic operations using Python. Topics covered include:
- Introduction to Python programming language
- Understanding data types (integers, floats, strings, booleans)
- Working with variables and operators
- Utilizing control structures like loops and conditional statements (if, elif, else)
- Managing program flow effectively with control structures
Day 18 - 21: Working with Data Structures
During this week, participants will delve into fundamental data structures in Python, including lists, dictionaries, tuples, and sets. They will learn how to manipulate, access, and modify these structures for diverse programming needs. Topics covered include:
- Introduction to data structures in Python
- Working with lists, dictionaries, tuples, and sets
- Accessing and modifying elements in data structures
- Applying data structures to solve practical programming problems
Day 22 - 30: Data Analysis with Python
In this week, participants will learn how to perform data analysis tasks using Python and industry-standard libraries such as pandas, numpy, and scipy. They will acquire skills in working with dataframes, performing data manipulation, and employing metrics such as counts, percentages, group by, pivot tables, correlation, and regression. Topics covered include:
- Introduction to data analysis with Python
- Working with pandas dataframes
- Data manipulation and cleaning
- Exploratory data analysis techniques
- Statistical inference techniques (ANOVA, correlation, regression)
Throughout the bootcamp, participants will engage in hands-on exercises and real-world data analysis projects to reinforce their learning and apply their newfound skills in practical scenarios. By the end of the program, participants will have the confidence and proficiency to work as data analysts and make data-driven decisions effectively.
Who this course is for:
- Individuals looking to kickstart their career in data analysis.
- Students and recent grads in data analysis or related fields.
- Professionals aiming to boost their analytical skills.
- Decision-makers want to understand and leverage data analysis.