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Data Analyst Job description: Roles and Responsibilities

Many companies are now talking about ‘data’ and using it to make decisions, this means that data is becoming the key. To make it easier to see how data is changing the world, just look at how major tech companies, especially social media platforms, are using data. 

Statistics predict that we create roughly 2.5 quintillion bytes of data each day. And with the growing popularity of IoT (Internet of Things), this data creation rate will grow even higher and exponentially.

The fast rate at which big data – large volumes of varied data generated – is being churned out has opened new multiple opportunities for companies to generate valuable insights to inform their decision-making. 

This ever-growing trend has in turn increased the demand and appetite for skilled data analysts across many industries and professions – think about the medical research world, IoT, Agriculture, Electric vehicle manufacturing, and many more. Data Analysts and Data Scientists are therefore highly sought after and frequently feature in most in-demand jobs and careers of the future. 

In simple words, a Data Analyst’s job description is to deal with massive data volumes and glean meaningful insights from them. They do this by interpreting statistical data, translating and simplifying this into useful information and visuals that businesses and organizations can use for critical decision-making. For example, a manufacturing firm will use data to precisely determine which products to make, in what quantities, what markets to enter, which investments to make, or which customers to target by which specific product(s). they also use data to identify and do projections of the weak areas in the business that need to be addressed.

Data Analysts’ tasks and responsibilities: 

Data Analysts’ tasks and responsibilities

Here’s a short list of Data Analyst’s duties on a day-to-day basis: 

  1. Identify Data: An analyst will be required to scope the data needed for analysis and the intended purpose or goal of this data.
  2. Data mining or gathering: Analysts are often required to collect data themselves. This can include targeted channels such as running and conducting surveys, tracking visitor behavior, entry and exit characteristics on a website, or landing page, or buying a specific type of datasets from data collection entities or persons.
  3. Clean data: From the raw data gathered, an analyst will then clean it by removing duplicates, errors, or outliers. By cleaning this gathered data, an analyst will ensure that they maintain the quality of data in a spreadsheet or workbook or a programming language such as Python to ensure the resultant interpretations won’t be wrong or skewed. 
  4. Model and Analyze data: This entails the creation and design of the structures of a database. Here, an analyst might choose what types of data to collect, store or establish how data categories are related to each other.
  5. Interpret data: This is almost the last leg of data analysis and is probably the most visible. In this stage, an analyst will interpret the data by finding patterns or trends that will help the stakeholders answer the questions at hand. Here, an analyst is also required to research new ways of making use of available data.
  6. Presentation: This is the last leg and that involves the less complicated yet most difficult job of documenting and communicating the findings or results of the analysis. It is a very key part of a Data Analyst’s role. Here, he or she will present visualizations – for example, charts and graphs, write reports and explanations of scenarios – and lastly, present information to interested parties in a simple to understand manner. A skilled data analyst not only knows how to untangle data but how to present it as well to any audience without losing them or making it too cumbersome to understand.

Key skills required to become a Data Analyst 

Key skills required to become a Data Analyst

Now that we know what a Data Analyst does on a day-to-day basis, let us delve into the key skills for the role. We shall break them into technical and soft skills.

Data Analyst technical and hard skills: 

Database tools:  A good analyst should be able to use spreadsheets such as Microsoft Excel and database tools such as SQL. Excel or other spreadsheets are good and ubiquitous across industries, while SQL is a necessity used to handle larger sets of data faster and accurately. 

Programming languages: It is imperative that a Data Analyst learn a statistical programming language, such as Python or R in order to handle large sets of data and perform complex equations.

Data visualization: This is used as we learned above, that’s in the presentation and visualization of findings in a clear and compelling way. Here, one will use tools such as Tableau, Excel, and Jupyter Notebook among others. 

Statistics and math: Data analytics is math and statistics heavy and as such, knowing the workings behind what data tools do in the background of processing your data helps a data analyst produce good work. These skills will also help you catch bugs and errors that may skew your data, and have a better understanding of the results.

Data analyst soft or workplace skills:

Communication: Knowing how to properly communicate your ideas to other people is a skill that will take an analyst far in their career. A good analyst will therefore learn or strive to have strong written and speaking skills.

Industry knowledge: Here, it is very important to know more insights about the industry you work in – for example if it’s Business, Manufacturing, Finance, or Healthcare. This will give you an edge and industry advantage in your work and results in producing better analytics.

Problem-solving: When working with large swathes of data, there are many problems as well as short timelines, demands, and pressure from your managers. A data analyst should therefore have a good insight to identify problems fast and their workarounds. Having the critical thinking skills will enable one to target the right data types, identify the most revealing methods of analysis, and not miss the gaps.

What qualifications do you need to become a Data Analyst?

qualifications needed to become a Data Analyst

A Data Analyst does not actually require a degree to qualify. However, one needs to have the right combination of both hard and soft skills to be considered for a role in data analysis. A good project portfolio is a good proof of work done that can increase one’s chances of getting a job.

A good Professional Data Analyst Certification can be a great entry and does not require any prior programming or statistical skills and is supposed to be suitable for all types of learners with or without college degrees. An example is the ALX Data Analyst Nano Degree 3-month programme. It’s actually all you need to get started in your career as a Data analyst.

ALX coupled with basic computer literacy, high school math, a liking for working with numbers, a willingness to learn, and a desire to enrich your profile with valuable skills will propel you into a life-changing career path. Learn more here: https://www.alx-t.com/courses/data-analyst/ 

Keep in mind however that traditionally, most entry-level Data Analyst jobs require at least a Bachelor’s degree. Therefore, it is important to get a degree whenever you have the wherewithal. 

Data Analyst salary

Data Analysts’ roles are some of the most highly paid. For example, from recent statistics from tech and fintech job listings, for roles at financial and technology firms, the average annual salary of a data analyst ranged from approximately $62,000 to $140,000. 

Let’s break this down further. An entry-level average salary for a junior Data Analyst is $43,653 per year; the intermediate is $62,382; 2 to 4 years or Senior Data Analyst can earn up to $95,300; while those who’ve been in the profession for 5 to 7 years (Senior Data Analyst IV) can get $128,321. Veterans who’ve been in the profession for over 8 years (or Principal Data Analyst roles) can be paid up to $143,837.

The US Bureau of Labor Statistics reports a median annual salary of $82,326 and a midpoint salary for a data analyst at $106,500, according to human resources consulting firm Robert Half. All this is dependent on many factors such as location and taxes among other considerations.

The Data Analyst role is also an entry or, stepping stone, to more senior data-driven jobs that one can later scale into. These include Analytics Manager, Business Analyst, Senior Data Analyst, Data Scientist, and Group Head of Data, among others. For experienced hires, IBM estimates that the annual salary of data scientists starts at about $95,000, while analytics managers will make nearly $106,000 per year.

Job Outlook for Data Analysts in 2022

In 2017, IBM predicted that the number of jobs for data professionals in the U.S. alone would surge by about 364,000 (to 2,720,000) before the end of 2020. This has further increased and as of 2022, 52% of businesses worldwide consider data analytics and predictive analytics primary parts of their operations. Furthermore, the need for data analysts and experts is only expected to rise as more companies invest in data and digital assets.

The World Economic Forum (WEF) also reported that 96% of companies were putting in huge human resource budgets in anticipation of new hires with relevant skills to fill future big data analytics-related roles. 

So is there a future for Data Analyst jobs? Absolutely yes, in fact, it is one of the most in-demand jobs in 2022. 

FAQs:

Is Data Analytics a stressful job? 

Well any job, actually, every job, can be stressful, time-consuming, and requires you to put in many hours. A data analytics job is no different. As a matter of fact, Data Analysis may be a difficult role, highly technically demanding, and can sometimes be more challenging to learn and enter than other fields in tech. 

If we put in the volume of work, many deadline constraints, and several layers of management and their demands, then a data scientist job can be stressful. However, any job can prove to be stressful if you are not passionate about the role, but If you enjoy working with data and solving complex problems using data analytics techniques, then you will enjoy the job and the salary package. 

Is Data Analytics a good career?

Data Analytics was reported as one of the most in-demand tech talents for 2020 and it’s also key to remember that more and more companies are prepared to pay extremely competitive salaries for this role. If you enjoy working with numbers and thinking analytically to solve problems, then a career as a data analyst is a good fit for you.

How long does it take to become a Data Analyst? 

It requires quite an amount of time to develop the needed skills to earn a job as a Data Analyst. This will depend on what you already know, and if you don’t have any skills yet, your strategy for learning these new skills, and lastly, the type of role you’re applying for within the industry. However, it may not take you quite as long as you may imagine.  

A professional data analyst certificate may be achieved in less than six months. A standard certification like the AXL Data Analyst Nanodegree Programme, if you do not go through university, would take you about 7 to 9 months to complete and be a great data analyst. 

ALX Transforms Tech Nanodegree programme will take you an estimated duration of 3 months, divided into durations of 10 hours per week. You will then build more skills through practical field (and industry) experience in internships, then as you get more confident, build your own portfolio and start earning like a boss within no time. This means high levels of discipline and putting in the time. 

Does Data Analysis require coding?

Well, Data Analysis will need some bit of coding skills, even though you may not be required to code as part of your day-to-day duty and role. It is however important to learn how to write some basic Python or R, and how to write Structured Query Language (SQL) queries to help you better clean, analyze, and visualize data. 

Recap: 

The career outlook for Data Analysts is truly positive. This is due to the fact that many organizations are now looking at building their businesses through decisions that are supported and informed by real data analytics. This, therefore, means that Data Analytics skills are in great demand.

If you want to join the data analysts’ path as a career and enjoy working with data and are willing to put in the hard work and long hours, then this is the career for you. It is quite attractive that even software developers are slowly opting to move careers into this domain. 

With a certification such as the ALX Transforms Tech Nano degree programme where you are better prepared for a career as a Data Analyst, you will easily pick up and improve your skills. 

So what is holding you back? Jump in and begin untangling data today!

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AUTHOR
Manno Notermans

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