A data analyst refers to a professional who specializes in collecting, organizing, and analyzing industry data, and conducts industry research, evaluation, and forecasting based on the data. Typically, they create reports using the collected data, summarize patterns, and provide solutions to stakeholders in the form of updated systems, procedures, or protocols.

They know how to ask the right questions, excel in data analysis, utilize data visualization tools and presentations, and assist in business decision-making to reduce costs, increase revenue, improve products, retain customers, and discover new business opportunities, among other tasks.

The common responsibilities of a data analyst include:

1. Collaborating with stakeholders to determine the overall team objectives of the company.

2. Cleaning and selecting relevant information while eliminating irrelevant data.

3. Summarize trends and correlations found in the data.

4. Identifying potential opportunities for process improvement and providing feasible recommendations.

5. Providing accurate data analysis and visual analytic reports.

If you want to enter the field of data analysis, you need to familiarize yourself with several database languages right away, even if you are not proficient in them.

The first and most important among them is Structured Query Language or SQL. It is a widely adopted industry-standard database language and possibly the most crucial analytical skill that a data analyst needs to understand. Created in 1970, SQL remains the standard method for querying and processing data in relational databases even today.

Almost every organization requires personnel who understand SQL, whether it's for managing and storing data, linking multiple databases (like Amazon's database used for recommending products of interest to you), or building and modifying these database structures. Every month, thousands of job postings require SQL skills, and individuals with advanced SQL abilities can efficiently retrieve specific information, perform aggregations and join tables, which often leads to lucrative salaries. While even non-technical professionals can benefit from learning this tool, if you aim to work with big data, learning SQL is the first step.

When it comes to Microsoft Excel, you might perceive it as somewhat less advanced compared to other sophisticated data analysis skills. However, this tool possesses a plethora of analytical capabilities. While programming languages like R or Python are better suited for handling large datasets, Excel has its programming language called VBA (Visual Basic for Applications), which allows for creating macros or pre-recorded commands. When deployed effectively, VBA can save analysts a considerable amount of time on repetitive tasks in areas such as accounting, payroll, or project management.

If you work in a lean or startup company, your database's first version might even be an Excel version. Over the years, Excel has remained a cornerstone for businesses across various industries, and it offers a wealth of free learning resources. Moreover, structured data analysis courses are available for those who wish to explore its functionalities more deeply. Therefore, learning Excel is crucial and holds significant value for professionals in the field of data analysis.

According to Ajay Sarangam, a technical writer in "Analytic Training," data cleaning and preparation account for approximately 80% of the workload for data professionals. This indicates the utmost importance of this skill. Why is it so crucial? Because there is a simple truth in machine learning: better data surpasses more sophisticated algorithms.

A properly cleaned dataset can yield extraordinary insights even with simple algorithms. Data analysts often need to retrieve data from one or multiple sources and prepare it for numerical and categorical analysis. Data cleaning also involves resolving missing and inconsistent data that could potentially impact the analysis.

In data analysis, data cleaning may not always be the most exciting task, but when viewed as an exercise in problem-solving, preparing data can become intriguing and challenging. FineBI, as an all-in-one data processing and analysis platform, empowers business users with different skill levels due to its user-friendly features.

It allows users to clean and transform data to ensure its accuracy and consistency. Users can apply various data cleaning techniques, such as removing duplicates, filling missing values, standardizing data formats, and correcting data errors. FineBI also supports data transformation operations, such as splitting columns, merging datasets, and creating calculated fields. These transformations help users reshape their data to meet specific analysis requirements.

As we all know, statistics is the cornerstone of data analysis. Once you study statistics, you will discover that many analyses are not as accurate as they seem. For example, many people tend to analyze the results of a phenomenon using the mean, but this often proves to be crude. Statistics, on the other hand, allows us to approach data from a more scientific perspective, gradually getting closer to the "truth" behind the data.

Most data analyses incorporate the following statistical concepts, which are essential areas to focus on in your learning journey:

- Basic statistical measures: Mean, median, mode, variance, standard deviation, percentiles, etc.

- Probability distributions: Geometric distribution, binomial distribution, Poisson distribution, normal distribution, etc.

- Population and sample: Understanding fundamental concepts and the concept of sampling.

- Confidence intervals and hypothesis testing: How to perform validation analysis.

- Correlation and regression analysis: The basic model for general data analysis.

After learning the principles of statistics, you may still feel uncertain about implementing analyses using tools. In that case, you need to seek relevant practical methods and further learning. A solid foundation in statistics is crucial for data analysts to comprehend the principles of probability, hypothesis testing, and inferential statistics. It enables them to apply appropriate statistical techniques to derive meaningful conclusions from data, assess the reliability of results, and provide data-driven recommendations.

Data visualization is the process of presenting any information or data in graphical form. It involves taking data from specific sources and transforming it into visual charts, graphs, tables, dashboards, and other formats to present the data in a more intuitive way for users. This is especially beneficial for individuals who may not have received formal data analysis training. Through data visualization, data analysts can help business decision-makers (who may lack advanced analytics training) fully explore the information hidden within vast datasets, making it easier to identify patterns and comprehend complex ideas at a glance. This capability empowers data analysts to gain a better understanding of a company's situation, communicate valuable insights to team leaders, and even make better-informed company decisions.

FineBI is a user-friendly data visualization tool that enables zero-code visualization. It offers a wide range of visual chart types, including charts, graphs, tables, and dashboards, allowing users to explore and analyze data from various perspectives and achieve stunning visual effects with simple drag-and-drop actions. It comes equipped with features like data integration, visual data processing, exploratory analysis, data mining, and visualization analysis reporting.

As a data analyst, you need to empower others to make critical decisions using data. By learning to create dashboards and reports, and transforming complex data into easily understandable visualizations, you can eliminate technical barriers and enable others to access, comprehend, and apply data effortlessly for key decision-making.

This could take the form of simple charts and tables with date filters or complex dashboards with hundreds of interactive data points. This effective data presentation enhances communication efficiency, fosters collaboration, and helps organizations better understand business trends and opportunities. Almost every data analyst's job involves generating reports or building dashboards, undoubtedly optimizing the data analysis process and making data-driven decisions more widespread and sustainable.

FineBI can help you effortlessly create efficient collaborative BI dashboards, supporting multiple users to access and edit the same dashboard simultaneously. Additionally, features like public links, platform sharing, and directory publishing make it easy to share the dashboards with stakeholders.

Feel free to make an appointment for a live demo with our product experts. We will be more clear about your needs and see how FineBI can help you and your organization transform data into value.

Data analysts need to learn R and Python data programming because both languages are popular and powerful tools in the field of data science. R and Python offer a wide range of data processing, statistical analysis, and machine learning libraries, enabling analysts to efficiently handle large and complex datasets.

Mastering R and Python data programming allows data analysts to explore data more flexibly, customize analysis workflows, and implement advanced algorithms and models. This enables them to extract deeper insights from raw data and make more accurate predictions and decisions.

When choosing which programming language to learn, R and Python are both excellent options. Employers generally do not mind which one an employee chooses as long as their analyses are accurate. However, due to its specialized focus on data analysis, some analysts prefer using R for data exploration and ad hoc analysis.

R provides rich statistical analysis and data visualization libraries, making data processing and analysis simple and efficient. Analysts with a preference for data exploration and statistical methods may find R more appealing.

On the other hand, Python is a more versatile programming language, suitable not only for data analysis but also for web development, machine learning, and other domains. Thus, analysts seeking to expand their skills across multiple domains may lean towards learning Python.

Due to the popularity of artificial intelligence and predictive analytics in the field of data science, understanding machine learning has been identified as a key component of the analyst's toolkit. While machine learning is not a skill like data cleaning or learning programming languages, mastering it enables analysts to automate model training and prediction, helping them gain a better understanding of the data. In the data-driven era, possessing machine learning skills is crucial for enhancing the professional competitiveness of data analysts.

Data visualization and presentation skills go hand in hand. Data analysts need to learn presentation skills because effective communication and presentation abilities allow them to convey complex data and analysis results concisely and understandably to non-technical audiences, helping them grasp data insights and trends. However, mastering professional presentation skills is not an easy task; even experienced speakers may sometimes feel nervous. You can try deliberate practice and learn some communication techniques to apply in your own presentations. Relax and focus on the audience's goals and needs, as this helps you capture their attention effectively.

Why do data analysts need to learn critical thinking? Data analysts need to find correlations and draw conclusions from seemingly unrelated large amounts of data, which necessitates the development of critical thinking. It is a key skill for analyzing and solving problems.

This type of thinking helps analysts discern biases and misleading factors, leading to more comprehensive and objective analyses. Dealing with vast amounts of information can easily overwhelm analysts, causing overreliance and a lack of independent thinking. Possessing critical thinking skills enables them to better organize their thoughts, infer new solutions, and tackle challenges in fast-paced and complex environments.

Data analysts employ these and other skills to assist organizations in making decisions. If you are willing to invest time and effort in learning these skills, it will pave the way for a successful career as a data analyst. Here are some introductory techniques:

• Learn the fundamentals: Begin by understanding the basics of statistics, mathematics, and programming languages such as Python or R commonly used in data analysis.
• Practice with real data: Apply your knowledge and gain practical experience by working with real datasets. Platforms like Kaggle, UCI Machine Learning Repository, and data.gov offer publicly available datasets for practice.
• Online courses and tutorials: Enroll in online courses or follow tutorials on data analysis platforms like Coursera, Udemy, or DataCamp for structured learning and guidance.
• Analyze projects and case studies: Study data analysis projects and case studies to see how professionals tackle real-world problems, identify patterns, and derive insights from data.
• Join data communities: Participate in data analysis forums, attend meetups, and join online communities like Stack Overflow or Reddit to interact with others, share knowledge, and seek help when needed.
• Learn data visualization: Understand how to visually present data using tools like Tableau, FineBI, or Seaborn to effectively communicate insights.
• Stay updated: Keep an eye on data science blogs, industry news, and research publications to stay informed about the latest trends, tools, and technologies in data analysis

If you possess any of the skills of a data analyst mentioned above, it is essential to promptly showcase your qualifications and expertise to potential employers in your resume. Here are some tips on how to effectively list data analyst skills on your resume:

1. Create a "Skills" section: Dedicate a specific section in your resume to highlight your data analyst skills. This can be placed either after your professional summary or work experience section. For example, include skills related to data analysis, programming languages, SQL, etc. However, more importantly, tailor the skills section according to the job description. Be sure to carefully review the job description of the position you are applying for and customize your skills accordingly.

• Use bullet points: List your data analyst skills in bullet points to make them easily scannable for recruiters and hiring managers. You can use the following format:
• Data Visualization: Proficient in creating compelling visualizations using Tableau to communicate complex findings.
• Data Cleaning: Skilled in data cleaning and preparation to ensure data accuracy and reliability.

2. Programming Languages: Advanced proficiency in Python for data manipulation and analysis. Be specific: Mention the specific tools, software, and programming languages you are proficient in. For example, instead of saying "Data Analysis Tools," mention specific tools like "Excel," "Tableau," "Power BI," etc.

3. Showcase your proficiency level: If you have different levels of proficiency in various skills, consider adding that information. For example, you can mention "Advanced" for skills you excel in and "Intermediate" for those you are still developing.

4. Highlight achievements: Whenever possible, try to connect your data analyst skills with achievements from your previous roles. This can be done in the work experience section, where you can demonstrate how you used specific skills to accomplish tasks or solve problems.

Remember to be honest about your skills and only include those you are genuinely proficient in. It's also a good idea to be prepared to discuss and elaborate on your listed skills during an interview.

Here, we recommend using user-friendly and powerful data analysis software (such as FineBl) to create your own data stories. FineBl empowers you to unearth the productivity hidden within your data!