Data analysis has made a positive impact on the life of businesses and organizations. It is crucial as it helps organizations make better decisions, improve revenue, increase their performance and reduce their loss. It helps answer questions like how to increase sales, who to target as customers, which employees to keep, how to reduce failure among students e.t.c.

The data analyst role is one of the most sought-after jobs in the world today. Data professionals are lucky as their jobs have good career prospects and they do not necessarily need a professional certificate in order to practice. They work in several industries including health, agriculture, education, finance, entertainment, and transportation.

A Data analyst’s job begins with sifting through ‘raw data.’ Raw data sometimes called source data are disordered and—without context—essentially meaningless. e.g a list of every purchase of soap at a store for a month but with further analysis. We can only obtain helpful information from them once we have brought order to chaos but convert the raw data into structured data. As such, collecting, cleaning, and organizing data are all parts of the data analytics process.

Companies, organizations, and industries are employing the services of data analysts both in remote and physical roles to assist in the curation and dissemination of insights from their data. Most organizations now understand how valuable their data are and how they can improve their operations, minimize cost and maximize profit by making good use of their data.

In this article, we offer a guide on how to become a Data analyst. We’ll cover all the required knowledge needed to achieve this feat.

Who is a data analyst?

A data analyst is a professional who gathers data, cleans it, and analyzes it in order to solve a problem. A data analyst is an individual that is responsible for organizing raw data into useful information that will assist stakeholders such as individuals, businesses, and organizations in making more informed and data-driven decisions.

What are the responsibilities of a data analyst?

1. Data mining: Data analysts are in charge of gathering data from primary or secondary sources, then transforming the data into an easily understandable format.
2. Data Cleaning: This involves approaches and methods used to refine the data from its raw form to a more appreciable form. Data cleaning consists in fixing errors, imputing missing values, removing irrelevant data, and removing outliers from a dataset.
3. Data analysis: This involves analyzing the data using the correct tools and methods to identify relationships, trends, and patterns that exist within the data. It involves carrying out descriptive, inferential, diagnostic, predictive, and prescriptive analyses of the data.
4. Result Interpretation: After applying different statistical techniques to data, data analysts are in charge of interpreting the result and explaining the trend and patterns that are included in the data.
5. Report writing: Data analysts prepare reports that communicate all the findings, trends, and patterns that exist in the data to the stakeholders. They are presented in text and visual form.

Data analyst vs Data scientist

It is important to state that there are some functions that can be done by either of these two types of data professionals however there are some distinction in their duties.

A summarized version of the distinction between their role is, a data analyst makes sense out of historical data, whereas a data scientist works on new ways of capturing and analyzing data.

Here are a few notes on the similarities and differences of these data professionals;

A data analyst is involved with collecting, analyzing, and summarizing data in order to draw conclusions and make better decisions while a data scientist is involved with data modeling, creating algorithms, automating systems, and building frameworks in order to solve complex problems.

A data analyst often spends time on routine analysis, providing regular reports. A data scientist designs the way data is stored, manipulated and analyzed.

A data analyst typically works on answering specific questions about the organization’s business. A data scientist works on developing new ways of asking and answering important questions.

A data analyst focuses more on relational database software, and business intelligence tools whereas a data scientist use more of programming languages such as python, java and also use machine learning to carry out organizational duties.

What are the skills needed to become a data analyst?

So to answer the burning question, we are going to examine the needed skill set needed to become a data analyst in the 21st century. The skillset are broken down into two broad categories;

• Technical skill
• Soft skill

Let us examine these necessary skills one step at a time;

Technical Skills

Technical skills are the knowledge and abilities needed to perform several tasks that involve the use of technology and certain techniques. Technical skills are also referred to as hard skills. Here are a few of them that are required of a data analyst;

1. Statistical analysis: This helps analysts gather, analyze and summarize data to draw conclusions. It is used by industries and businesses to answer business questions, predict outcomes, and make valid inferences. In line with this, knowledge of statistical software is recommended. Software used for statistical applications includes R, Python, Stata, SPSS, Microsoft Excel, SAS, etc.
2. Microsoft Excel: Microsoft Excel is a spreadsheet tool used for calculation, organizing data, and performing quantitative analysis. It is a very common skill that most employers look out for in their employees. it is used for data entry, data analysis, data visualization, and data storage.
3. Structured Query Language (SQL): SQL is considered the most important skill a data analyst should possess. SQL is a programming language used for manipulating data in relational databases. SQL can be used to access a large dataset. It is used to access, read, manipulate and analyze data (of any size, small or large datasets) to derive insights.
4. Data Visualization: Data visualization is the art of representing data in form of visuals, and charts. These visuals communicate the findings to the audience in a way they can quickly and easily understand. A competent data analyst doesn't only care about deriving insights from data but also ensures to tell a compelling story with the data. This is why data visualization is a necessary skill for a data analyst. Tools such as Power BI, Tableau, Microsoft Excel are used to accomplish this feat.

Next on the bucket list of skills needed by a data analyst is soft skill. Let us examine the nuances of this skill set;

Soft Skills

Soft skills are interpersonal skills that enhance relationships and complement hard skills in the workplace. Soft skills are important skills that one should develop as they go a long way in adorning one’s hard skills.

1. Critical thinking: Data analysts are expected to think critically as this will help them frame questions in the right manner and as well think of ways to proffer solutions to the organization’s problem using analytics of data.
2. Problem-solving: Problem-solving is a key skill that a data analyst should have. This skill will help the data analyst decide the possible and available means of tackling a business problem.
3. Team player: A data analyst should understand that they will have to collaborate with other people, this could be teammates or other stakeholders in other to accomplish projects or set out goals the business wishes to achieve.
4. Effective communication: Data analysts are expected to be able to communicate effectively their findings to the stakeholders and decision-makers. They should be able to communicate in a language that the technical and non-technical users will understand.

The duo of technical and soft skills are very essential for one to thrive in an organization. Usually, technical skills are the skills that make you qualify for the job, while soft skills will help you further maintain your position in the organization. As simple as soft skills may appear failure to possess them can result in you losing your position in an organization.

Common tools used by data analysts

Note: Although the type of tool that will be used frequently in your role as a data analyst will depend largely on the tools used in the organization you find yourself in. The below listed tools is not an exhaustive list as some tools are used more frequently than other tools within an organization.

These are some the tools that data analysts use in order to perform analytical processes and help organizations derive insights from their data.

• Microsoft Excel
• Microsoft Power BI
• QlikView
• R
• Stata
• Tableau
• SAS
• Python
• SQL

How to become a data analyst?

Are you a student, professional, or researcher who is aspiring to become a data analyst or needs to hone some analytical skills, search no more as Resagratia has got you covered.

Resagratia is a platform that produces courses in data analysis and business intelligence. It provides high-quality online experience for its learners which makes learning interesting. It offers you the chance to learn at your own pace within the comfort of your home or office.

Here are some of the services provided that can help you in your data analyst career;

Technical skills are what make an effective data analyst. Being able to analyze data effectively with the right tool in a short period makes an effective data analyst

Are you in search of where to acquire these skills? Resagratia has got you covered with amazing and easy-to-understand courses. You can sign up now to be part of this valuable learning experience.

1. Work on real-life projects

Learning alone does not qualify you to be a data analyst. You have to work on real-life case scenarios. These will build your skills better and allow you to apply your learned skills and intuition to solve problems.

2. Build a portfolio of your projects

To show the recruiting managers and employers what you are capable of doing, it is necessary to build a project portfolio that will have all of your projects inside it. This will show the recruiters the level of the skills you possess and how valuable you will be to their firm.

3. Earn a certification

Certified data analysts are given more recognition than those without any certification. These certifications are for all levels; beginner, intermediate and advanced. This means that you do not necessarily have to be an expert before being certified.

Conclusion

It is important to understand that there are many opportunities for data analysts in the world today. Even though some persons are into data analytics without having bagged a degree in a quantitative field, it is however not the best approach. Ensure to acquire the right skills needed and network with people in the field of data analytics so as to be updated about the latest happenings in the data world.

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