Data Analyst Road Map
Data Analyst Roadmap for Beginners 2024
Following is the roadmap to learn Data Analyst skills for a total beginner (no coding or computer science background
needed). It includes FREE learning resources for technical skills (or tool skills) + soft (or core) skills + Practice + Showcasing your work to get interview calls + Cracking Interview.
Data Analyst Core & Tool Skills
Core Skills:
- Business, Math, Statistics
- Analytical Mindset
- Communication
- Business Understanding
Tool Skills
- Excel
- SQL
- PowerBI/Tableau
- Python
Business Math/Statistics
Topics:
- Business Math
- Arithmetic, Percentages
Basic Statistics
- Mean, Median, Standard Deviation, Bell Curve, Percentile
Excel
Topics:
- Basic Formulas: SUM, AVERAGE, PRODUCT, MEAN, MEDIAN, IF, SUMIF
- Advanced Formulas: VLOOKUP, MATCH, INDEX
- Pivot Tables
- Basic Charting, Filters, Sorting
- Power Query
Project Management
- Scrum:
https://scrumtrainingseries.com/ - Kanban:
https://youtu.be/jf0tlbt9lx0
Leveraging ChatGPT
4 Technical Areas where you can leverage AI:
- Data collection
- Data Cleaning
- Data Transformation
- Data Visualizations and providing insights
BI tools (Power BI or Tableau)
Power BI Topics
- Connecting to different data sources
- Data transformation in Power Query
- Creating metrics using DAX & Data Modelling
- Creating visuals
- Dashboarding
- Publishing to Power BI Service
Interview Prep & Practice
- Create a professional-looking LinkedIn profile
- Learn business concepts from ThinkSchool and other YT Case Studies
- Learn presentation skills
- ATS Resume Preparation
- Portfolio Building
- Assignment
- Interview Preparation
- Interview questions and answers
SQL
Topics:
- Basics of relational databases
- Basic Queries: SELECT, WHERE LIKE, DISTINCT, BETWEEN, GROUP BY, ORDER BY
- Advanced Queries: CTE, Subqueries, Window Functions
- Joins: Left, Right, Inner, Full
- Stored procedures and functions
Python and Pandas
Topics
Variables, Lists, Dictionaries, Tuples, If condition, for loops, functions, modules, file handling, classes and objects, exception handling
Pandas
- Dataframe basics
- Reading data from csv/excel files
- Handling missing data
- Group by, Concat, Merge