Hello everyone, today, we will exploring what does a data Analyst actually do, necessary qualifications and skills, as well as the expected salary. This profession is heart of many organizations, harnessing the power of data to turn it into valuable insights.
Let’s start by breaking down the actual responsibilities of a data analyst.
1. Defining the problem
The standard journey of a data analyst begins will pinning down what the client actually needs—it could be a dashboard for data visualization, insightful reports or product-focused analysis to guide strategic decisions. After establishing the end goal, the analyst builds a detailed plan outlining data sources, the timeline for data collection, transformation and analysis.
2. Data Collection
The next step is gathering the required data, which can originate from various sources— databases (SQL or NoSQL), flat files (excel, csv, xml etc…..) or APIs.
3. Establishing an ETL Process
ETL (Extract, Transform, Load) pipeline, a critical component within the data analyst workflow. This process necessarily involves collaboration with programmers or data engineers.
The extract phase involves pulling data from the identified sources which can range from SQL databases, flat files, or APIs. This is followed up by the transform phase where business rules are created to shape the raw data into a format suitable for analysis. This may include operations such as filtering, aggregating, or cleaning the data.
Finally, during the load phase, the transformed data is placed into the analytics system. This ETL process often needs to be automated to handle new or updated data efficiently and regularly. This is known as an ETL pipeline, which automates the repetitive task of data preparation, consequently saving valuable time and effort.
This automation is particularly helpful when dealing with recurring data flows, ensuring the reliable and consistent gathering and preparation of data. It’s the data analyst’s job to oversee the successful creation and management of this pipeline enabling them to focus more on data analysis rather than data preparation.
4. Conducting Data Validation
Data validation is essentially quality assurance, which involves reviewing the data and running queries to verify correctness and completeness of the dataset.
7. Data Set-Up For Reporting and Visualization
Setting up data for reporting and visualization involves creating views which combine different tables into one and selecting a subset of the data required for reporting. The formatted data can then be used directly in reports or data visualization tools like Qlik Sense, Qlikview, Tableau or PowerBI.
8. Creating Reports and Visualizations
The final task is creating the required reports or visualizations using the tools montioned below or other. Automation is key here, ensuring reports are updated regularly and sent out as per schedule without manual intervention.
Qualifications and Skills
Generally, for a Data Analyst role, a bachelor’s degree in a field such as computer science, statistics, or mathematics is required. While a master’s degree is not always necessary, it is often desirable. However, these requirements aren’t absolute. It’s certainly possible to become a Data Analyst without these specific degrees, provided an individual acquires the necessary skills.
As far as skills are concerned, proficiency in SQL, Python or R for analysis, data visualization tools like Qlik, Power BI or tableau, statistical tools like SAS or SPSS, Excel and knowledge of cloud platforms like AWS or Azure is highly advantageous.
Salary Expectations
The salary scale for data analysts can vary significantly based on their level of experience. Those in entry-level positions can generally expect an annual salary ranging from €33,000 to €40,000. Moving into mid-level roles, the salary typically increases to between €40,000 and €45,000. Senior-level positions often command an annual salary exceeding €45,000. However, please note that likely salary ranges can depend heavily on the specific location and country.
Becoming a data analyst involves learning quite a bit and it might seem overwhelming initially. However, the good news is that there are plentiful resources available online for free or at an affordable price through platforms like Udemy, Coursera, or edX. It just depends on your commitment and interest.
Invest in broadening your skills, learn continuously and confidently step into the data-driven world of a data analyst. The possibilities are boundless!
