Essential Technical Skills for Data Analysts

DATA ANALYTICS TRENDSFEATURED

Vanessa Gottardi

4/3/20234 min read

Data analyst's tecnical skills
Data analyst's tecnical skills

Discover the essential core skills every data analyst needs to excel in today's data-driven world. From SQL to business intelligence tools, this blog post highlights the key tools and technologies that empower data analysts to extract insights, visualize data, automate tasks, and leverage the power of the cloud. Unlock your potential as a data analyst by mastering these core skills and staying ahead in the rapidly evolving field of data analysis.

In today's data-driven world, organizations across industries are relying heavily on data to make informed decisions. This has led to a significant rise in the demand for skilled data analysts who can extract meaningful insights from complex datasets.

While the field of data analysis encompasses a wide range of skills and tools, this blog post will focus on the core skills that every data analyst should possess. We will explore the importance of SQL, business intelligence tools like Tableau and PowerBI, Excel, Python, and cloud platforms such as AWS, Google Cloud Platform (GCP), and Azure.

There are so many different skills you can learn for data analytics, however, there a few core ones I recommend you to start out. Below a list in order to learn that I believe to be the simples way to crack the data analysis code:

  1. SQL (Structured Query Language):

    Even if you do not use SQL, you will probably use some variation of it if your company has a large amount of data set. It is used to query and retrieve data from a database. So, if your company collects data, which every company should do, they have to store it somewhere, which is usually in a database and SQL is how you get the data from the database.

    SQL is the foundation of data analysis and is considered a must-know skill for any aspiring data analyst. It is a programming language used to manage and manipulate relational databases.

    SQL allows analysts to retrieve specific data subsets, perform aggregations, join tables, filter data, and create new tables/views for analysis. Proficiency in SQL enables analysts to query databases efficiently and extract relevant information from large datasets.

    Another reason to learn SQL first, is the fact that a lot of companies interview or have a technical interview on SQL. Knowing this skill might be a crucial part of you getting a data analyst job. 

  2. Business Intelligence Tools (Tableau and PowerBI):

    There are so many different BI tools in the market, but learning Tableau or PowerBI is pretty transferable to almost all other BI tools as they are faily similar in how they do things, show and display the data.

    Business Intelligence (BI) tools like Tableau and PowerBI empower data analysts to visualize and communicate data effectively. These tools provide intuitive drag-and-drop interfaces to create interactive dashboards, reports, and visualizations.

    Data analysts can leverage these tools to explore data, identify trends, perform ad-hoc analysis, and share insights with stakeholders. Proficiency in BI tools enhances data storytelling and helps in the effective communication of complex information.

    When it comes to interview, companies probably will not ask you to build a dashboard on the spot, but the combination of SQL where you can query your data, and build a visual with the data in a BI tool, is a perfect fit,

  1. Excel:

    Most people have used Excel in their daily basis, however, it can also be used as a tool for data analysis. Excel is just a fundamental skill that every company will expect you to know.

    Excel is a ubiquitous tool that has long been a staple in data analysis. While it may not be as powerful as SQL or specialized BI tools, Excel remains valuable for quick data manipulation, basic analysis, and data cleaning tasks.

    Understanding functions, formulas, pivot tables, and data validation in Excel can significantly speed up data processing tasks and provide a familiar environment for less technical stakeholders.

  2. Python:

    It is a fantastic tool you can use to manipulate your data, create data visualizations among other functions. For this reason, many people might have Python higher up on their list to learn first, however, it can be hard to learn in comparison to SQL and BI tools, and you may not use as often.

    Python has emerged as a popular programming language for data analysis due to its simplicity, versatility, and extensive libraries such as Pandas, NumPy, and Matplotlib.

    Python allows analysts to automate repetitive tasks, clean and transform data, conduct statistical analysis, build models, and create visualizations. Python's flexibility makes it an invaluable tool for data analysts, especially when handling large datasets and performing complex analytical tasks.

  3. Cloud Platforms (AWS, GCP, and Azure):

    There is no denying that these platforms have played a huge impact on how we use data in the data analyst industry but they can be hard to learn if you are not using them in an actual job.

    The rapid adoption of cloud computing has revolutionized how data analysts work with data. Cloud platforms like AWS, GCP, and Azure offer scalable and cost-effective solutions for storing, processing, and analyzing vast amounts of data.

    Data analysts need to be familiar with cloud concepts, understand how to work with cloud storage services, set up data pipelines, and leverage cloud-based analytics tools. Proficiency in cloud platforms equips data analysts with the ability to handle big data and take advantage of distributed computing capabilities.

Data analysis is a multifaceted discipline that requires a diverse skill set. In this blog post, we explored the core skills that every data analyst should possess such as SQL, business intelligence tools like Tableau and PowerBI, Excel, Python, and cloud platforms such as AWS, GCP, and Azure.

Mastering these skills will empower data analysts to extract actionable insights, communicate findings effectively, automate processes, and leverage cloud capabilities to analyze large datasets. By continuously honing these core skills, data analysts can stay ahead in the ever-evolving landscape of data analysis and deliver value to organizations in today's data-driven world.

If you like this content and want to learn more, you might also read:

Transforming Data into Insights

The Power of Marketing Analysis: Unlocking Success for Your Business

SQL Fundamentals for Data Analysis: A Hands-On Learning Series

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