Data Analyst Skills Guide: SQL, Excel, Tableau, Python
Data Analyst Skills Guide: SQL, Excel, Tableau, Python
✅ SQL - Must-Know Topics 🗄️
Category | Key Concepts & Commands | |
SQL Basics |
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Data Aggregation |
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Joins |
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Data Filtering | Logical operators ( | |
Window Functions |
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Views & CTEs |
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Data Cleaning |
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Reporting |
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💡 Nice-to-Have Topics for SQL
Data Manipulation:
INSERT,UPDATE,DELETE, TriggersAdvanced Joins: Self Joins and Cross Joins
Optimization: Query Optimization Techniques, Star & Snowflake Schemas
Subqueries: Single-row and Multi-row subqueries
✅ Excel - Must-Know Topics 📊
🛠️ Data Preparation & Cleaning
Tools: Remove Duplicates, Text to Columns, Flash Fill, and Data Validation.
References: Understanding Absolute vs. Relative References (
$A$1,A$1,$A1).Tables: Creating and working with Structured References.
🧪 Essential Formulas & Functions
Text:
LEFT,RIGHT,MID,TRIM,PROPERLogical:
IF,AND,OR,IFERRORDate/Time:
TODAY,NOW,DATEDIF,EOMONTHLookup:
VLOOKUP,INDEX,MATCH,XLOOKUPMath/Stats:
SUMIF,COUNTIF,AVERAGEIF
📈 Visualization & Analysis
Pivot Tables: Creating/Customizing, Calculated Fields, and Items.
Charts: Line, Bar, Pie, etc.
Conditional Formatting: Custom Rules, Color Scales, and Icon Sets.
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💡 Nice-to-Have Topics for Excel
Power Tools: Power Query and Power Pivot.
Automation: Recording Macros (no VBA required) and Basic VBA.
Advanced Analysis: Data Tables, Goal Seek, and Scenario Manager.
✅ Tableau - Must-Know Topics 🎨
🏗️ Dashboard Design & Prep
Storytelling: Creating Dashboards and Stories with Interactive Features (Filters, Actions, Tooltips).
Connectivity: Linking to Excel, SQL, Google Sheets, and Cloud Sources.
Preparation: Data Blending, Joins, Extracts, and Source Filters.
📊 Visualizations & Calculations
Chart Types: Heatmaps, Tree Maps, Dual-Axis, and Combined Charts.
Calculated Fields: Custom logic and Aggregations.
Table Calcs: Running Total, Moving Average.
Filtering: Dimension, Measure, Context, and Cascading Filters.
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💡 Nice-to-Have Topics for Tableau
LOD Expressions:
FIXED,INCLUDE,EXCLUDE.Advanced Analytics: Pareto Charts, Waterfall Charts, Gantt Charts.
Organization: Hierarchies, Groups, Sets, and Bins.
Prep: Using Tableau Prep for heavy data cleaning.
✅ Python - Must-Know Topics 🐍
🐍 The Fundamentals
Python
🐼 Libraries for Data Analysis
Pandas: DataFrames, Series,
merge(),concat(),groupby(),pivot_table().NumPy: Arrays, Mathematical Functions, Reshaping Data.
Visualization: * Matplotlib: Histograms, Scatter Plots.
Seaborn: Heatmaps, Categorical Plots.
🧹 Data Handling & Scripting
Data Cleaning:
dropna(),fillna(),replace(),split().Dates:
Datetimemodule, Parsing, and Formatting.Scripting:
deffunctions,return,lambda, and Error Handling (try/except).I/O: Reading/Writing CSV, Excel, and JSON; APIs (Requests) and Web Scraping (BeautifulSoup).
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💡 Nice-to-Have Topics for Python
Automation:
OpenPyXLorXlsxWriterfor Excel automation.Advanced Viz: Interactive plots with
Plotly.Statistics:
SciPyfor probability distributions and hypothesis testing.
Anything missing? Get in touch