When aspiring to break into the world of data, the journey can be daunting. Many ask, What skills do I need to land my first data job? and Where do I even begin? Over the years, I’ve distilled the core advice for aspiring data analysts, scientists, and engineers into four foundational pillars that support a thriving data career:
Spreadsheets (Excel)
SQL
Visualization Tools (Tableau or Power BI)
Scripting Languages (Python or R)
Whether you're self-taught or transitioning into data, this guide is designed to provide clarity on the foundational skills you need for entry-level analyst roles. While advanced roles like data scientist or data engineer typically require more specialized knowledge, these four pillars are the cornerstone of nearly every role in data.
Excel has been a data powerhouse for decades. Despite countless “Excel killer” claims, it remains indispensable. Even with modern dashboards and advanced tools, stakeholders will inevitably ask, Can I export this to Excel?
Essential Excel Skills:
Basic Functions: Navigation, formulas, and conditionals like IF, COUNTIFS, SUMIFS
Data Organization: Logical spreadsheet layouts, filtering, sorting
Joining Datasets: VLOOKUP, XLOOKUP, and INDEX-MATCH
Visualizations: Chart creation and pivot tables
Power Query: For working with structured and tabular data
If you’re ambitious, delve into Power Query for data transformations. It's not only a gateway to more advanced tools like Power BI but also a valuable skill for handling structured data.
Pro Tip:
Start with Excel, even if you’ll eventually work with Google Sheets—it’s easier to transition from Excel to Sheets than the other way around.
SQL is the universal language for managing and querying relational databases. However, outside a real-world production environment, practicing SQL’s nuances can be challenging since most training datasets are far cleaner than real-world data.
Essential SQL Skills:
Core Syntax: Select, where, and from statements
Aggregations: GROUP BY, HAVING, and aggregate functions
Joins: Multi-table joins and avoiding common pitfalls
CTEs and Subqueries: To structure complex queries
Dimensional Modeling: Understand table relationships for reporting and analysis
Choosing a SQL Dialect:
While dialects like Postgres, T-SQL, and MySQL have slight differences, mastering one makes adapting to others relatively simple. Start with Postgres or T-SQL for broad applicability.
Data visualization tools like Tableau and Power BI are essential for creating interactive dashboards and conveying insights effectively. While Excel offers basic charting capabilities, specialized tools are better suited for enterprise-grade visualizations.
Essential Visualization Skills:
Chart Mastery: Create bar charts, line graphs, scatter plots, and more
Data Preparation: Ensure data is clean and structured before importing
Basic Dashboarding: Build intuitive, interactive dashboards
Choosing a Tool:
Power BI: Ideal if you’re already familiar with Excel.
Tableau: Preferred for more design flexibility and aesthetics.
Pro Tip:
Pick one tool to focus on and master. Learning advanced functionalities often happens organically once you’re working with real-world datasets.
Scripting languages like Python and R help analysts go beyond the limits of point-and-click tools. While not always required for entry-level roles, they’re increasingly valued, especially for data manipulation and automation.
Python vs. R:
Choose Python: It’s versatile, widely used, and has a larger ecosystem of libraries like Pandas and NumPy.
Stick with R: If you’re already familiar, especially for statistical analyses common in academia and biotech.
Essential Scripting Skills:
Variables and basic operations
Conditional logic (if statements)
Loops and functions
Basic data structures: lists, dictionaries, and tuples
Pandas (Python): For data manipulation and analysis
Certifications can help but are not essential for breaking into entry-level roles. Their impact often depends on the region or market. For example:
Valuable: Certifications from major providers like Microsoft, Tableau, or Snowflake.
Marginal Impact: Free certifications like Google’s Data Analytics program—great for learning but not standout credentials.
Pro Tip:
Focus on building a portfolio showcasing your skills. Practical projects often outweigh certifications in the eyes of hiring managers.
Show Your Process: Hiring managers value candidates who think critically and explain their thought process.
Be Honest About Skills: Overstating abilities can backfire. For example, if you claim to be intermediate in Python, be prepared to write functions or solve basic problems.
Practice Problem-Solving: Live coding exercises and SQL challenges are common. Familiarize yourself with foundational problems like identifying duplicates, creating aggregations, and filtering datasets.
The four pillars—Excel, SQL, data visualization, and scripting—are the foundation of any successful data career. Entry-level analysts should prioritize Excel and SQL, as these skills are indispensable and frequently required in job descriptions.
Top Resources for Learning:
Excel: Udemy courses by Tom Hinkle, Oz Du Soleil’s YouTube channel
SQL: Practice on datasets like IMDB’s database or Microsoft Contoso
Visualization: Tableau’s official training materials
Python: Courses like The Python Bible or Complete Pandas Bootcamp
With persistence and the right approach, you’ll be ready to land your first role in the data field.
