Five core skill areas of a data analyst

This post outlines five core skill areas of a data analyst. Data analytics is “the collection, transformation, and organization of data in order to draw conclusions, make predictions, and drive informed decision making.” A data analyst has three core roles: “The data analyst is generally someone who works with SQL, spreadsheets, and databases, might work as a business intelligence analyst” (Google Data Analytics Professional Certificate, 2021).

Data analytics is becoming one of the fastest-growing and most rewarding career choices in the world. In the next decade, the demand for business analytics skills will probably be higher than the demand for any other career (10.9% vs. 5.2%) (Source: Bureau of Labor Statistics) … And right now, fifty-nine percent of companies have plans to add even more positions requiring data analysis skills (Source: SHRM).

Google Data Analytics Professional Certificate, 2021

Google Data Analytics Professional Certificate Course 1 Foundations: Data, Data, Everywhere – Cliffs Notes

How the Demand for Data Science Skills is Disrupting the Job Market (Burning Glass, 2017)

You may also be interested in What is the Google Data Analytics certification?

Five core skill areas of a data analyst

1. #Excel
2. #SQL/SSMS
3. #Tableau/Power BI
4. #R/Python for data analysis
5. #SAS/SPSS

1. Microsoft Excel

1.1. Minimum beginner skills

Data filters, functions, formulas, charts and visualizations, pivot table, transpose, vlookup, vba macros, conditional formatting

1.2. Training and resources

Google Sheets Training and Help

https://support.google.com/a/users/answer/9282959

<Learn even more ways to move, store, and analyze your data with the Google Sheets Training and Help page, located in the Google Workspace Learning Center. This hub offers an expanded list of tips, from beginner to advanced, along with cheat sheets, templates, guides, and tutorials.>

Microsoft Excel for Windows Training

https://support.microsoft.com/en-us/office/excel-video-training-9bc05390-e94c-46af-a5b3-d7c22f6990bb

<everything from a quick-start guide and introduction to tutorials and templates, you will find everything you need to know, all in one place.>

2. SQL/SSMS

2.1. SQL Basics

Installing SQL Server Management Studio (download SSMS +SQL2019-SSEI-Expr); Select + From Statements; Where Statements; Group By + Order By Statements

The syntax of a SQL query:

Use SELECT to choose the fields you want to return.

Use FROM to choose the tables where the fields you want are located.

Use WHERE to filter for certain information.

<you’re SELECTing some fields FROM some table WHERE you want only those rows that match a criteria (condition).>

2.2. Intermediate SQL skills

Inner/Outer Joins; Unions, Union Operator; Case Statement; Having Clause; Updating/Deleting Data; Aliasing; Partition By

2.3. Advanced SQL skills

CTE (Common Table Expression); Temp Tables; String Functions

2.4. Training and resources

SQL Cheat Sheet

https://towardsdatascience.com/sql-cheat-sheet-776f8e3189fa

<This starter guide for standard SQL syntax used in PostgreSQL offers videos, activities, and readings on SQL.>

W3Schools SQL Tutorial

https://www.w3schools.com/sql/default.asp

<If you would like to explore a detailed tutorial of SQL, this is the perfect place to start. This tutorial includes interactive examples you can edit, test, and recreate. Use it as a reference or complete the whole tutorial to practice using SQL.>

An introduction to relational database theory (video)

https://youtube.com/watch?v=CmPTvMhqUG0%3Ffeature%3Doembed

3. Tableau/Power BI

3.1. Minimum beginner skills

Connecting to a data source; doing calculations; using popular visualizations (charts, scatter plots)

3.2. Project-based learning

Tableau Project (Sales Insights) Playlist

https://youtube.com/watch?v=videoseries%3Flist%3DPLeo1K3hjS3usDI9XeUgjNZs6VnE0meBrL

Sales Insights Power BI Project Playlist

https://youtube.com/watch?v=videoseries%3Flist%3DPLeo1K3hjS3uva8pk1FI3iK9kCOKQdz1I9

4. R/Python for data analysis

Minimum skills: for loops; functions; data types; NumPy, Pandas (Data Manipulation and Analysis libraries); Matplotlib (Data Visualization library)

More “data sciency” type libraries:

Scikit-learn – Machine Learning and Data Mining

StatsModels – Statistical Modeling, Testing, and Analysis

Seaborn – Statistical Data Visualization

5. SAS/SPSS

Learn SAS on your own time with these simple steps

https://blogs.sas.com/content/academic/2016/10/20/learn-sas-on-your-own-time-with-these-simple-steps/

Getting started with SPSS

https://www.open.edu/openlearn/society-politics-law/sociology/getting-started-spss/content-section-0

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