We all live in a data-driven world, where businesses generate approximately 328.77 million terabytes of data every day. This is a huge amount of data that can be really overwhelming for any business if not properly indexed. Inside this huge ocean of information that is constantly growing, Data Analysts are time savers, as they are able to action all raw data into insights that guide important day-to-day business decisions.
Why is it important to keep refining data analysis skills? For established Data Analysts, foundational skills are just the beginning. To stay up to date in this fast-moving and evolving role, it’s so important to continually learn and develop your skill sets.
Let’s explore the core skills needed for a Data Analyst to master in order to create a meaningful impact.
1. Skills in advanced statistical analysis
If you ever wondered what is the core of data analysis, you’ll find that statistical analysis is at the heart of it, by providing the analytical detail needed to interpret complex datasets accurately.
A strong understanding of advanced statistical methods is what marks a difference between a junior and a senior Data Analyst. While doing the basic statistics that lay down the groundwork, knowing advanced techniques such as regression analysis, hypothesis testing, and Bayesian inference are needed for extracting more nuanced insights from data. Being skilled in these areas allows analysts to uncover patterns and relationships that are not immediately noticeable, leading to more accurate predictions and results.
2. Being an expert in data visualisations to create creative solutions
Data visualisation goes beyond creating basic charts and general data graphs. Experienced analysts are skilled at using advanced visualisation tools like Tableau, Power BI, and D3.js to create effective and interactive dashboards to track key metrics.
An experienced Data Analyst is able to easily tell a story through data, highlighting key insights in a clear and visual way. Effective visualisation not only aids in data interpretation but also enhances communication with stakeholders who may not have a technical background.
3. Having a strong understanding and experience in machine learning
Machine learning is increasingly becoming a core skill in a growing digital age. Established Data Analysts need to have a solid grasp of machine learning algorithms and frameworks, such as:
- Scikit-learn
- TensorFlow
- and PyTorch
This includes understanding when and how to apply different algorithms, like decision trees, clustering, and neural networks, to solve complex business problems. Machine learning methods allow analysts to build predictive models that can automate decision-making processes. As a result, these processes can help uncover hidden patterns in large datasets which saves time and money.
4. Highly skilled in SQL and data management
Skills needed for a data analyst in this area weigh heavily for maintaining data integrity and accessibility. SQL has always been the backbone of data management. Advanced SQL skills are essential for querying large databases efficiently. Some of the main skills in data management include:
- Complex joins
- Subqueries
- Window functions
- And stored procedures,
Also, understanding database management systems and data warehousing concepts, such as ETL (extract, transform, load) processes are important. As they ensure the process of large volumes of data accurately and efficiently.
While technical and academic expertise is crucial, a great analyst must also be inquisitive and commercially minded. These qualities allow them to translate complex data into actionable business insights. The best analysts balance technical knowledge, business acumen, and adaptability to industry trends. This is particularly relevant to the trend of working with AI and the use of low-code applications.
5. Programming master in Python
Programming skills go hand in hand with data analysis. While Python and R are the most commonly used languages, other languages like Java or Scala can be useful too, especially when working with big data technologies such as Apache Spark or Hadoop. Strong programming skills allow analysts to automate repetitive tasks, manipulate large datasets, and develop custom analytical solutions.
How can you learn Data Analyst skills in programming? There are a number of online resources, like courses, and coding boot camps that can help you build and refine programming skills.
Why continuous learning is essential for career growth as a Data Analyst
The skills required for Data Analysis are constantly evolving. New tools, technologies, and methodologies come to light regularly. This is why even established Data Analysts are considered continuously learning, whether through, online courses, workshops, or self-study. Staying updated with the latest trends and advancements in data science ensures that analysts can leverage the most effective techniques and tools available.
At Legal & General, we support you in your career development with any route in which you want to learn and grow.
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