How to Become a Data Analyst – Salary, Qualifications, skills.
Hello, hope you are doing great.
Let’s start by defining what a data analyst is?
Whether it’s sales figures, audience demographics, and market research, or keeping track of their overheads, every business, regardless of size, records some kind of data. It’s a Data Analysts’ job to interpret this data, find trends, and present this back for this business to act upon.
The task of a Data Analyst is to process the varied data concerning the customers, the products, or the performances of the company, to release indicators useful for the decision-makers. Thus, the information provided by the data analyst enables companies to define the products to be offered to customers according to their needs, the marketing strategy to adopt, or the improvements to be made to the production process.
Data Analyst Qualifications.
For Data Analysts to be truly effective in their role, they need a combination of hard and soft skills which allow them to turn raw data into business-focused solutions.
The three most important skills a Data Analyst needs:
- Technical skills: at the very core of data analysis is the ability to interpret numbers and data, organise datasets in programmes like Excel, perform statistical analysis, and confidently navigate databases and SQL.
- Communication skills: it’s not enough for a Data Analyst to explore their data – they also need to be able to ensure they’re asking the right questions about the data they’re interpreting, and be adept at presenting their findings in a way allowing other team members to absorb and implement solutions.
- Attention to detail: the art of data analysis is very precise. An invaluable Data Analyst is able to examine small details leading to bigger solutions, including granular interpretation of outcomes and using results to hypothesise about future trends and outcomes.
How to stand out as a Data Analyst?
Earn a Bachelor’s degree: It’s a requirement for the majority of entry-level data analysis jobs to have a degree in a field with a focus on statistics or analytics. Relevant disciplines include:
- Computer Science
- Information Management or Business Information Systems
Get relevant work experience: As with most professions, work experience is an invaluable thing to have on your side. Fortunately, in the field of data analysis, there are a number of internship and graduate programmes available to those who are still studying. This gives entry-level Data Analysts an advantage once they leave their studies because they’ll have developed real-world analysis skills that will set them apart.
Take short courses: The field of data analysis is dependent on understanding changing trends and technologies. As a result, the most effective Data Analyst is the one who continues to stay ahead of the curve. One sure-fire way of ensuring you stay relevant in data analysis is to commit yourself to lifelong learning.
Essential Skills for Data Analysts.
SQL, or Structured Query Language, is the ubiquitous industry-standard database language and is possibly the most important skill for data analysts to know.
Almost every organization needs someone who knows SQL—whether to manage and store data, relate multiple databases or build or change those database structures altogether.
2. Microsoft Excel.
When you think of Excel, the first thing that comes to mind is likely a spreadsheet, but there’s a lot more analysis power under the hood of this tool. While a programming language like R or Python is better suited to handle a large data set, advanced Excel methods like writing Macros and using VBA lookups are still widely used for smaller lifts and lighter, quick analytics.
3. Critical Thinking.
Using data to find answers to your questions means figuring out what to ask in the first place, which can often be quite tricky. To succeed as an analyst, you have to think like an analyst. It is the role of a data analyst to uncover and synthesize connections that are not always so clear.
4. R or Python–Statistical Programming.
Like SQL, R and Python can handle what Excel can’t. They are powerful statistical programming languages used to perform advanced analyses and predictive analytics on big data sets. And they’re both industry standard. To truly work as a data analyst, you’ll need to go beyond SQL and master at least one of these languages.
5. Data Visualization.
Being able to tell a compelling story with data is crucial to getting your point across and keeping your audience engaged. If your findings can’t be easily and quickly identified, then you’re going to have a difficult time getting through to others. For this reason, data visualization can have a make-or-break effect when it comes to the impact of your data.
6. Machine Learning.
As artificial intelligence and predictive analytics are two of the hottest topics in the field of data science, an understanding of machine learning has been identified as a key component of an analyst’s toolkit. While not every analyst works with machine learning, the tools and concepts are important to know in order to get ahead in the field. You’ll need to have your statistical programming skills down first to advance in this area, however. An “out-of-the-box” tool like Orange can also help you start building machine learning models.
Typical employers of data analysts are;
- Specialist software development companies,
- Telecommunications companies,
- Public sector organisations,
- Social media specialists,
- Colleges and universities,
- Pharmaceutical companies, and
Do data analysts make good money?
Nationwide, data analysts earn between $47k – $113k with an average salary of $66,906. This high earning potential is the result of the demand for data analysis skills across all job industries. Many companies are also looking to identify existing employees with strong data analytics skills to invest in them.
Both university graduates and school leavers can enter the data analysis profession. For graduates, the usual entry point is a degree in statistics, mathematics, or a related subject involving maths, such as economics or data science. Other degrees are also acceptable if they include informal training in statistics as part of the course, for instance, sociology or informatics.
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