Junior Data Analyst Skills and Career Path

Junior Data Analyst Skills and Career Path
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Junior data analysts are the first stepping stone in your data analysis career. Learn everything about the skills you need and your (possible) career path.

Data analytics offers plenty of opportunities if you have a knack for numbers. Uncovering insights from data makes you slightly aroused? No need to be ashamed of that; we all have our kinks.

Generally, data analytics is seen as an entry field for data science. Today, we’ll have a look at the junior data analyst role – the entry-level within an entry-level. You’ll learn what you’d do as a junior data analyst, what skills you need for that, and how your career can pan out.

Who is a Junior Data Analyst?

A junior data analyst is an entry-level data analyst who assists in collecting, cleaning, analyzing, and visualizing data.

They do all that to help companies make better business decisions. The junior data analyst’s work is performed under the supervision of more senior data analysts and data scientists.

Importance of Junior Data Analysts in Entry-Level Roles

Junior data analysts lay the groundwork for the senior data analysts to do more advanced stages of data analysis. It’s better to delegate data quality checks, initial analyses, and simpler reports to a junior data analyst. This frees senior data analyst’s time to deal with more complex and creative segments of analysis.

At the same time, junior data analysts start learning from the simplest tasks and prepare for senior positions.

Understanding the Role of a Junior Data Analyst

To better understand a junior data analyst’s role, let’s first go into more detail about what their responsibilities are. Then, we’ll compare that to the senior data analyst’s responsibilities and see how important junior positions are when starting your data analyst career.

Responsibilities of a Junior Data Analyst

There are four main areas of a junior data analyst’s responsibilities.

Responsibilities of a Junior Data Analyst

But do junior data analysts do all that? Yes, they do, but on a more starter level. You can think of a junior data analyst as an assistant in these tasks.

1. Defining a Business Problem

Junior data analysts assist senior data analysts in identifying key metrics and performance indicators. They also participate in meetings, preparing minutes, data summaries, and visualizations that clarify the business problem to be solved by the further stages of data analysis. In other words, they assist senior data analysts in stakeholder communication.

2. Data Collection and Management

This responsibility generally involves four tasks.

  1. Identifying data sources: Junior data analysts identify data sources – databases, spreadsheets, APIs – from which the relevant data can be extracted.
  2. Data extraction: Using API calls and web scraping data is quite often reserved for senior data analysts, even though sometimes junior analysts can also be tasked with that. However, what you’ll for sure do as a junior data analyst is write queries (usually SQL) and use spreadsheet tools (Excel or Google Sheets) to get data from the databases and spreadsheets.
  3. Data cleaning and preparation: This task is usually solely the responsibility of a junior analyst. You will remove errors, inconsistencies, duplicate and irrelevant data and ensure that data is suitably formatted for analysis. Again, most often, you’ll do that via Excel, SQL, and Python.
  4. Preliminary data management: As a junior data analyst, you will assist in organizing and structuring data for databases and data warehouses. This usually involves creating and maintaining data documentation and metadata.

3. Data Analysis

Junior data analyst is directly involved in the preliminary stages of data analysis. This means they are expected to know basic statistical techniques for summarizing data and identifying some elemental trends. Most often, this means you calculate central tendency measures (mean, median, and mode), variability measures (variance, standard deviation, interquartile range), and do some simple correlation analyses.

4. Data Visualization and Reporting

On top of the data analysis that you did, you will create reports that present your findings in a nice and tidy way. In most cases, this means creating data visualizations and dashboards.

Junior vs Senior Data Analyst

Another helpful way of understanding the junior data analyst role is to compare it with the senior data analyst.

Go through the comparison below, and the role of a junior data analyst will be much clearer.

Junior vs Senior Data Analyst

Additional resource for understanding everything about this role is the What is a Data Analyst article.

Why Are Junior Positions Important in a Data Analytics Career?

When you build a career in data analytics, it’s important not to skip any of the important steps. Otherwise, it will leave you with the gaps in your knowledge. Papering over the cracks becomes increasingly difficult the further you go in your career.

So, you should be patient and take your junior data analyst position very seriously, as it helps you with the following.

Importance of Junior Data Analyst Position in Your Career

1. Hands-On Experience With Data and Tools

No matter your education, practical experience is crucial. While you might have experience with various data types and tools in your education, you still haven’t used them to solve actual business problems. In a junior data analyst position, you’ll have the opportunity to work with real-world datasets, encounter numerous problems in doing that – yes, you’ll make mistakes (a crucial part of learning!) – and try to solve them using different analytical tools and techniques.

Speaking of tools, working as a junior data analyst will make you proficient in Excel, SQL, Python, Tableau, PowerBI, or any other tool the organization uses.

2. Exposure to the Analytical Workflow

As a junior data analyst, you will participate in all stages of data analysis under the supervision of senior analysts. That way, you’ll slowly be eased into the data analysis process. You’ll be able to understand the purpose of each data analysis stage and how they are connected.

In other words, you’ll learn the best practices of data analysis, regardless of the tools used.

3. Learning From Senior Analysts And Mentors

I already mentioned that having practical experience is invaluable. You don’t have it, but you need it. Senior analysts have it and don’t lose anything by sharing it. Even better: in most companies, mentoring junior analysts (you!) is one of the core tasks of a senior data analyst. Their experience might include spending many years in the company and knowing all its ins and outs. Or they already worked in several companies and brought the best mix to their current job.

There’s no better thing than learning from industry experts! So, be open to hearing their feedback and advice. Even ask for it when needed. This will accelerate your learning curve to an unimaginable level. While learning from your own mistakes is incredibly efficient, try not to overdo it; otherwise, very soon, you won’t have a job anymore. Instead, try to learn from the mistakes senior analysts made when they were in your position.

4. Developing Analytical And Problem-Solving Skills

What is a junior analyst without analytical skills called? Junior. I know, it’s a terrible joke. But the point here is that it would be really bizarre if junior data analysts didn’t possess analytical skills. The whole point of this job is embodied in the word ‘analyst’.

Developing Analytical And Problem-Solving Skills in a Junior Data Analyst

The best way to develop these skills is to tackle real-world problems. That way, you will learn how to identify trends, patterns, and anomalies in data, interpret data, and make recommendations.

5. Building a Professional Network

As a junior data analyst, you’ll collaborate with data experts, managers, and other stakeholders. Again, you can learn from all these people, be it technical, analytical, or soft skills. Building connections with them can give you the opportunity to collaborate on interesting projects that suit your interests, where you’ll be exposed to new ideas and technologies.

Additionally, building a solid professional network improves your chances of getting great future career opportunities.

6. Preparation for Senior Roles

Everything I mentioned above will make you ready for more senior roles. This is the whole point; nobody wants to stay a junior data analyst forever. However, using this position to the maximum advantage is crucial to building a successful data analytics career.

Essential Skills for Junior Data Analysts

Let’s now talk about the skills you need to start your career as a junior data analyst.

Essential Skills for Junior Data Analysts

Technical Skills

The technical skills of junior data analysts encompass these three areas.

Technical Skills for Junior Data Analysts

1. Databases & Data Warehousing

Junior data analysts need to understand database and data warehousing principles because they are used for storing, retrieving, and managing data.

The knowledge of databases includes:

Regarding data warehousing, this is what you must be familiar with:

2. Data Analysis Tools

One of the very important tools you need to know is Excel (or Google Sheets) for entering data, creating pivot tables, performing basic statistical analyses, and creating charts and graphs.

Also, you’ll use programming languages. The two of the most commonly used ones in data analysis are SQL and Python.

With SQL, you’ll query, insert, update, and delete data from relational databases. This will be done via one of the most popular RDMBs mentioned above. Some SQL concepts you’ll need are joining tables, filtering, aggregating and grouping data, and creating temporary tables.

Python is essential for data cleaning, statistical analysis, and data visualizations. It’s also extensively used in building ML models, but you’ll be involved in this only to a certain extent. What adds to Python’s popularity is the number of libraries for all these tasks. Here are some of the more popular ones:

You won’t necessarily need all these libraries. But knowing each from each category will help you a lot.

R is another programming language, but in most cases, it’s optional for data analysts. It’s designed for statistical analysis and data visualization. However, you should be OK with only SQL and R.

3. Business Intelligence (BI) Tools

Working with BI tools is essential for any data analyst, including a junior one. With BI tools, you can create advanced data visualizations, reports, and dashboards and share them across the organization.

Some of the more popular BI tools include:

Analytical Skills

Moving on to the next skill set, these are the two most critical analytical skills for junior data analysts.

Analytical Skills For Junior Data Analysts

1. Data Analysis and Interpretation

I’ll now expand on what skills interpreting and analyzing data sets encompass.

Data Analysis and Interpretation Skills For Junior Data Analysts

Understanding data context: All data analysts must understand the context (business processes, goals, business problems…) in which data is used. Only by knowing the context can your data analysis and interpretation be correct.

Data exploration: You need to use Exploratory Data Analysis (EDA) to understand the data you have – its patterns, anomalies, and structure.

Statistical analysis: I already mentioned that you will perform some basic statistical analysis. This is to understand relationships between variables and make inferences that will, in the end, lead to data-supported decisions. Skills here include calculating measures of central tendency (mean, median, mode), dispersion (variance, standard deviation, interquartile range), and correlation analysis.

Hypothesis testing: You will use hypothesis testing (e.g., t-tests, chi-square tests, ANOVA…) to validate your assumptions.

2. Problem-Solving Skills

Problem-solving skills boil down to four distinct skills.

Problem Solving Skills for Junior Data Analysts

Identifying trends: You’ll use time-series analysis, moving averages, and exponential smoothing to identify trends. These trends will be used in making predictions and strategic decisions.

Root cause analysis: This analysis identifies the causes of trends and anomalies in data. Some known root cause analysis techniques are fishbone diagrams, the 5 Whys method, and regression analysis.

Drawing meaningful conclusions: Once you’re done with the core of your data analysis, you need to draw some conclusions from it. Ideally, they would be meaningful. You know, the point is for the decision-makers to, ahem, make decisions. To make them do so, you need to put your conclusions in the business context and summarize them, which is usually done via data storytelling, visualizations, and reports.

Problem-Solving Frameworks: These frameworks help you solve problems by giving you a structured approach. Some examples are:

Soft Skills

Finally, soft skills are the third important skill set for junior data analysts.

Soft Skills for Junior Data Analyst

1. Communication Skills

The first important part of soft skills is communication skills.

Communication Skills for Junior Data Analysts

Clarity in communication: The point of communication is for people to understand you. As a junior data analyst, you’ll be addressing a whole range of experts. In doing so, you need to speak a commoner’s language.

Communication clarity means you can translate technical jargon into layman’s terms and highlight your findings' relevance to the business. Otherwise, your analysis will be useless.

There’s nothing sadder than an analysis with brilliant insights that is not acted upon simply because non-technical stakeholders don’t understand its importance. It’s up to you to make them realize it!

Data storytelling: This skill will help you add more clarity to your communication. You shouldn’t just show dry numbers and graphs but wrap them in a story that engages the stakeholders. Use anecdotes, structure your presentation like a story, and make people remember the key points of your presentation.

Presentation skills: You should use tools like PowerPoint or Tableau to create visually appealing presentations. These will support your data storytelling and clear communication. Some people are more natural in this than others. But don’t be discouraged. This is also a skill that can be learned.

2. Attention to Detail

The attention to detail is extremely important in data analysis and encompasses these three skills. All this makes sure that both data and data analysis can be trusted.

Attention to Detail Skills For Junior Data Analysts

Data quality assurance: The GIGO (garbage-in-garbage-out) principle applies heavily to data analysis. No matter what you do, you can’t base a good analysis on garbage data. So, you need to be very detail-oriented when cleaning your data, validating it, and finding and correcting errors and inconsistencies. Don’t even think about approaching this stage cursorily.

Thoroughness in analysis: Attention to detail also applies when performing an analysis. You must be very rigorous in constantly checking your approach, calculations, and assumptions. Don’t ignore any result that’s even a bit strange (also, pay attention to the results that are suspiciously not strange) because it could blow up in your face later on. Also, I meticulously document every step of the analysis process. The point is that you can always explain every detail you’ve done. And also, your analysis can be repeated and confirmed that you did everything right.

Consistency in data management: This applies to having consistent rules and methods when entering and managing data. You need to have your formats, naming conventions, and coding practices standardized across the various data sources. Again, documenting all these rules helps in maintaining consistency.

3. Time Management and Organizational Skills

The third aspect of soft skills is how you manage your time and organize your work.

Time Management and Organization Skills For Junior Data Analysts

Task prioritization: You’ll have to handle multiple tasks at the same time. And nobody will organize them for you. You need to recognize which tasks have priority (and be assertive enough to find out if it’s not clear) so you don’t get overwhelmed and miss important deadlines. Some techniques, such as the Eisenhower Matrix or ABC analysis, can help you with task prioritization.

Project management: Junior data analysts are usually not left alone on big projects. However, you could get some simpler projects to lead and learn how it’s done. If that’s the case, then you will have to learn how to efficiently set timelines, allocate tasks to project participants, and track the project's progress. Some tools that can help you with this are Asana, Trello, or Microsoft Project.

Workflow management: In simple terms, this skill means being aware of which tasks precede and which follow your tasks to complete a specific business process. Meaning, you need to be aware of the dependencies between the various tasks within a process. Tools like Gantt charts and Kanban board can help you visualize the workflow in order to remove bottlenecks and improve efficiency.

Adaptability: Junior data analysts need to be adaptable and flexible. Working in organizations can sometimes be chaotic – priorities and requirements change, and projects get hastily introduced or removed. And you get irritated by that. Which is quite normal, but don’t let the irritation consume you. You must not take changes personally; accept them and adapt quickly to new circumstances.

Educational Background and Certifications

In this part, we’ll see what educational options are available for all the junior data analyst wannabes.

Typically, a Bachelor’s Degree is required to start a career in data analytics. Most commonly, the degrees are in mathematics, statistics, or computer science.

However, the degree can also be in any other relevant quantitative field, such as economics, engineering, physics, or data science.

If the field requires some specific expert knowledge, data analysts can also have a background in sociology, psychology, anthropology, or any other expert background.

Master’s Degrees and Ph. Ds are not required, but they won’t do you harm. Also, don’t be discouraged if you don’t have a degree. First, an increasing number of companies are ditching academic requirements for data analytics positions.

Second, there are a vast number of valuable certifications that also do the same thing as a degree—provide you with knowledge and serve as a testament to it.

Here are some certifications that I would consider as a junior data analyst.

  1. IBM Data Analyst Professional Certificate – Excel, SQL, Python, Cognos Analytics, Tableau
  2. Google Data Analytics Professional Certificate  – data cleaning, analysis, visualization, data-driven decision-making, R, Tableau
  3. MicroMasters Program in Data Science (UC San Diego) – probability, statistics, machine learning, data visualization, big data
  4. Professional Certificate in Data Science (Harvard University) – R, statistical fundamentals, ML
  5. SAS Certified Specialist: Base Programming Using SAS 9.4 – SAS programming for data manipulation and analysis
  6. Data Science Certification – advanced analytics, ML, and data management using SAS tools
  7. Microsoft Certified: Power BI Data Analyst Associate – using Power BI to build scalable data models, clean and transform data, and create reports
  8. Microsoft Certified: Azure Data Scientist Associate – data science and ML using Azure Machine Learning
  9. Tableau Desktop Specialist – using Tableau Desktop for data visualization
  10. Tableau Certified Data Analyst – preparing, analyzing, and visualizing data using Tableau
  11. Google Analytics Individual Qualification (GAIQ) – web analytics, data collection, processing, configuration, and analysis using Google Analytics

Many of these certifications are targeting the use of specific tools. Knowing which tools the companies you want to work for are using can help you choose the right certifications.

However, don’t over-obsess about it, especially if you already have a degree. No matter which tool you choose, you will learn about data analysis, so that’s good. While each tool has its specifics, once you’re familiar with one, it’s much easier to move to another one.

Also, you can start with certifications once you settle into your job and are introduced to the specific tools. That way, you’ll be able to draw from practice, and learning through courses and getting a certification will be much easier.

Resources for Skill Development and Networking

Having a degree or certification is a significant step in your career. However, this is not the be-all and end-all of your data analysis skills. There are many resources available that can help you hone your skills and gain some practical experience. (If you already are a data analyst, you can basically skip this part – working in data analytics is the best learning resource.)

Skill Development Resources For Junior Data Analysts

Practical experience: How can a person applying for an entry position have practical experience? Don’t worry, this is not a requirement for junior data analysts. However, if you do have practical experience, it can set you apart from other candidates. Internships, co-op programs, and project work during your education count as such.

Online learning platforms: Look for courses, bootcamps, and workshops where you live. Attend them. For that, I can’t give you resources. However, online resources are another matter.

Portfolio development: Doing data analysis projects is also one of the very good ways of building your skills. Choose various projects where you work with various types of real-life data. Also, choose projects that focus on the skills relevant to you as a junior data analyst. With that, you’ll improve your skills and have something nice for your future employers to prove your skills.

Here are some good resources of data for your projects:

Networking: This is important not only because you connect with professionals and experts in search of career opportunities. You do that because this is an opportunity for you to learn by joining professional organizations and online communities or attending industry conferences.

Here are some resources I’d suggest you look into.

Career Path for Junior Data Analysts

The general overview of how your career in data analysis can develop is given below.

Junior Data Analyst Career Path

You typically start as a junior data analyst or data analyst. These are usually entry-level positions; the name only depends on the company hierarchy. With several years of experience, you can move to mid-level positions.

From there, your career can take on two directions: you either go into management (less technical roles) or, if you’re not into managing people, dedicate yourself to specializations that advanced technical roles offer.

It should be mentioned that choosing one of these paths doesn’t mean you have to forget about the other one completely. Yes, data scientists can become directors of data analytics or data science. Also, managers can decide they had enough of dealing with other people and want to go back to their technical expertise.

Conclusion

The importance of a junior data analyst position as a starting point in your data analytics or data science career can’t be overstated. That’s why I dedicated such a long article to it.

While it’s a junior position, you still need to have some technical, analytical, and soft skills to land this job. In the article, I also gave you plenty of ideas on acquiring these skills. What I didn’t talk about is a data analyst salary.

I already linked it there, but let me do it explicitly now: StrataScratch is an excellent source for aspiring junior data analysts. We have thousands of coding interview questions, where you can practice coding in SQL, Python, and R. These are actual interview questions, so you’ll be preparing for the job interview as you learn and practice coding skills. The same applies to a range of technical topics we cover in the non-coding interview questions.

Also, don’t forget about our data projects, where you can practice your data analysis skills. We’ve also been working on our StrataScratch blog for years, so I’m sure you’ll find plenty of interesting articles there. One suggestion is Data Analyst Interview Questions and Answers.

June 19th, 2024

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Junior Data Analyst Skills and Career Path
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