Data Analysis A-Z: Become Data Analyst in 30 Days

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.