Companies are collecting large amounts of data and analyzing it to make strategic decisions in various processes within their businesses, which is why there is an increasing demand for data analysts. Additionally, data analysis technology and tools are constantly evolving, which makes data analysts' work more efficient.
A data analyst collects, cleans, and interprets data sets in order to answer a question or solve a problem.
Six steps of data analysis:
- Business Question: Define what problem you want to solve.
- Get Data: Collect the data required for analysis.
- Explore Data: Explore data with visual exploration to understand what is in a dataset.
- Prepare Data: Data cleaning, calculated fields and data validation.
- Analyze Data: Use data analysis techniques to understand, interpret, and derive conclusions based on the requirements.
- Present Findings: Share insights with stakeholders.
Statistical foundations are crucial for a data analyst because they form the basis of the data analysis process. Through statistical methods, a data analyst can use a scientific approach to understand, interpret, and report on data results.
- Statistics for Data Science | Probability and Statistics | Statistics Tutorial | Ph.D. (Stanford)
- Statistics - A Full University Course on Data Science Basics
- Statistics Course Overview | Best Statistics Course | MarinStatsLectures
- Crash Course: Statistics
- Statistics for Data Science Course | Probability and Statistics | Learn Statistics Data Science
- How I use Math as a Data Analyst?
- Statistics Cheatsheet - CME 106, Stanford University
- Statistics Cheat Sheet, MIT
- Descriptive Statistics Cheat Sheet, DataCamp
- Think Stats, by Allen B. Downey
- Statistics in Plain English, by Timothy C. Urdan
- Computer Age Statistical Inference, by Bradley Efron and Trevor Hastie
Excel is a commonly used tool for data analysis and is important for data analysts because it helps to organize, analyze, visualize, and manipulate data. Additionally, it is user-friendly and accessible to most data analysts.
- Microsoft Excel Tutorial for Beginners - Full Course (freeCodeCamp)
- Excel Tutorials for Data Analysts (Alex The Analyst)
- Beginner to Pro FREE Excel Data Analysis Course
- Microsoft Excel Tutorial for Beginners | Edureka
- Advanced Excel Full Course 2023 | Simplilearn
- How I use Excel as a Data Analyst
- Excel Quick Reference (CustomGuide)
- Excel Basics Cheat Sheet (DataCamp)
- Hackr Excel Cheat Sheet
- Exceldemy Excel Functions List
- Microsoft Excel Manual, University of Mary Washington
- Excel Fundamentals, St. George’s University of London
SQL is a programming language used by database management systems for storing, querying, organizing, and managing data. Knowing SQL is important for a data analyst because it is a tool used for querying, filtering, joining, and analyzing data. SQL is used by data analysts for managing and analyzing data, and it helps make analysis processes more efficient and effective.
- SQL for Data Analytics - Beginners Guide (Stanford-linked note)
- SQL With Practice Exercises
- SQL Server Execution Plans
- SQL Full Course In 10 Hours (Edureka)
- SQL Full Course 2023 (Simplilearn)
- SQL Tutorial - Full Database Course for Beginners
- PostgreSQL Tutorial Full Course
- MySQL Full Course for Free (2023)
- Khan Academy — Intro to SQL
- Codecademy - Learn SQL
- Danny Ma – 8 Week SQL Challenge
- Kaggle - Intro to SQL
- SQLCourse
- edX – SQL Courses
- SQLZoo
- SQLBolt
- PostgreSQL Tutorial
- SQLTutorial.org
- freeCodeCamp – Relational Database
- RIP Tutorial – SQL
- W3Schools SQL
- Mode SQL Tutorial
- TutorialsPoint – SQL
- bipp SQL Tutorial
- Codewars
- HackerRank – SQL
- LeetCode – SQL Study Plan
- SQL Murder Mystery
- Advanced SQL Puzzles
- SQL Fiddle
- SQL Practice
- Postgres Online (ExtendsClass)
- SQL Basics Cheat Sheet (LearnSQL)
- SQL Cheat Sheet (SQLTutorial.org)
- SQL Basics Cheat Sheet (DataCamp)
- Learn SQL in 10 Minutes (freeCodeCamp)
- SQL Commands Cheat Sheet (IntelliPaat)
- Mosh Hamedani SQL PDF
- Advanced SQL Cheatsheet
BI (Business Intelligence) tools are important for data analysts because they are used to analyze, visualize and make sense of data. These tools help data analysts to speed up their workflow and better understand data. BI tools offer different visualization techniques and graphs to analyze data and visualize it for management decisions. This enables data analysts to better understand data, interpret results more effectively and create a better foundation for business decisions.
- Microsoft Learn — Power BI
- Power BI Community
- Power BI Beginner Tutorial (CodeBasics)
- Power BI Full Course (Edureka)
- Hands-On Power BI — Beginner to Pro
- Guy in a Cube
- SQLBI
- Tableau Community
- Tableau Public
- Official Free Training Videos
- Tableau Full Course (Edureka)
- Crash Course to Dashboard
Python is important for data analysts because it is considered a programming language that can be used for many data analysis processes. Python can be used for many data analysis processes, such as analyzing large datasets, data manipulation, data visualization, machine learning modeling, data mining processes, and data cleaning processes. Additionally, Python's open-source nature, free availability, and ease of learning are advantages for a data analyst. Therefore, knowing Python as a data analyst creates a versatile tool for data analysis processes and can help make data analysis processes faster, more efficient, and more accurate.
- Python.org
- Python Docs (Codecademy)
- LearnPython.org
- Learning Python: From Zero to Hero (freeCodeCamp)
- Google's Python Class
- TutorialsPoint — Python
- W3Schools — Python
- DataCamp — Intro to Python
- Simplilearn — Python Tutorial
- Coursera — Programming for Everybody
- Udemy — Python for Beginners (free)
- Python Full Course (freeCodeCamp)
- Python Full Course for Beginners (Mosh/FreeCodeCamp)
- Data Analysis with Python (NumPy, Pandas, Viz)
- How I use Python as a Data Analyst?
- Alex The Analyst
- Ken Jee
- Thu Vu data analytics
What are the soft skills that a Data Analyst should have?
- Analytical Thinking: Understand, manage and interpret data using logical and critical thinking.
- Communication Skills: Explain complex data clearly to non-technical audiences and collaborate with teams.
- Problem Solving: Identify issues from data and develop effective strategies to solve them.
- Teamwork: Work cross-functionally (marketing, product, engineering) and contribute in team settings.
- Business Understanding: Tie insights to KPIs, revenue, costs, and strategy.
- Storytelling: Present insights with narrative, context, and actionable recommendations.
- There is nothing called PERFECT resume, so keep learning and updating!
- Prepare one-page resume and use a professional template.
- Based on above learnings and projects update your resume.
- Add relevant courses/certificates.
- Tailor your resume to each role/company.
Free resume template websites:
Interview Preparation
- Start applying once you have a couple of solid projects — interviewing is part of learning.
- Know your resume inside out (even minor details).
- Watch mock interviews / podcast discussions on YouTube.
- Read Q&A on: LinkedIn, Indeed, Glassdoor.
Ta ta ta ta, congratulations! You are now a Data Analyst and you can apply for your dream job and company. 🥳🎉
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