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How to Become a Data Analyst Without a Degree [2024 Guide]

How to Become a Data Analyst Without a Degree [2024 Guide]

If you are wondering how to become a data analyst without a degree, here is a guide that will help you understand the journey of a data analyst. A degree may only be necessary for some jobs. But instead of a degree, you should have specific qualifications or skills that differentiate you from others. You must focus on your coursework to gain relevant knowledge and skills. 

Earn some certifications from accredited institutions and create an attractive portfolio with the best of your work. This way, you can prove your skills even without a degree and will land a decent job.  

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Focus on an Online Course

Acquiring a degree or formal education from prominent universities or institutes may not be possible for everyone. If you are not pursuing any formal education or degree, you can take an online course and start your journey to data analysis. 

Many online courses are available, such as DP-900T00: Microsoft Azure Data Fundamentals. These courses will help you gain relevant skills and offer great career support. You will learn how to visualize data and use them to derive meaningful information. 

These courses offer you certification for data analytics too and they will help you enter the data analytics field sooner. Working data analyst professionals teach many courses that provide you with relevant guidance. So, choose the course accordingly by doing thorough research about the course and its benefits.

Acquire Relevant Skills for Data Analytics

To obtain the skills required for data analytics, you must be familiar with data analytic tools. These tools must be handled with effectiveness, and one can do so when one knows the basic principles behind them. So, understanding the fundamentals of such tools and the principles associated with them must be the priority of data analyst aspirants. You can build both soft and hard skills for data analytics. 

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Technical Skills 

Following are some relevant technical skills that every data analyst should have if they need to learn how to become a data analyst without a degree:

  • Programming Skills – These are the most important things that are used for data analysis. The two important programming languages are Python and R. Somewhat, Java is also used in advanced programming. If you build your knowledge in this and your strength in the concept, you will easily be able to cope with data working. 
  • SQL – SQL basically stands for Structured Query Language. Its main aim is to maintain and manipulate queries and data stored in large databases. 
  • Data Cleaning and Sorting – This data analysis skill helps in spotting the trends and patterns in data, which can later be used in deriving insights and making predictions. These predictions are used in making accurate and timely decisions. 
  • Data Visualization – Data visualization helps you share your findings with a large audience in an understandable format. These derivations are plotted on graphs and other visualizations. 
  • Advanced Excel – The most commonly required technical skill is to acquire the knowledge of Excel and know how to extract information or values from functions. 
  • Data Warehousing – It will help you understand the workings of storing and managing your data. With data warehousing skills, you will be able to access or retrieve the information or data easily. 

Practical Data Skills

Learning programming can help you in various ways. It will make your analysis process easy. Therefore, it is good to understand the mathematical principles of data analysis and science. 

Statistics

These are some core skills that must be built by data professionals. This field is concerned with collecting, analyzing, interpreting, and presenting data. With a better understanding, you will be able to distinguish between good data and bad data. 

Mathematics

Since mathematics is the most crucial part for any data analyst, it is good to have a strong grasp of maths. Some of the important concepts you need to take care of are as follows:

  • Discrete mathematics
  • Linear algebra
  • Calculus 

Important Soft Skills 

Data analyst roles require routine communication and better interpersonal skills. However, other tech roles do not require any communication skills, but every data analyst must have these types of skills and know how to be polite. Some of the important soft skills are as follows: 

  • Communication – The most important thing is to understand the problem and communicate with your team members. For this, you need to have better communication skills. 
  • Creative Thinking – A mind that thinks like a pro is a must. A creative mindset can help you to bring original ideas and implement those to identify threats and opportunities. 
  • Analytical Mindset – The analytical mindset can help you analyze information and solve various complicated data patterns. 

Learn Fundamentals and Techniques of Data Analytics

Learning the fundamentals of data analytics is very important and helps you understand the concepts more clearly. You must implement that knowledge and gain practical experience to become a successful data analyst. 

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However, to learn the fundamentals, you must first know the four major types of data analysis. 

Descriptive Analysis

This analysis is used to answer all the questions of a data analyst. This kind of analysis is best suited to analyze current as well as historical data. It illustrates the patterns and trends behind it. It is the most common type of data analysis that helps you do descriptive analysis. 

With this type of analytic, you can do various functions such as:

  • Financial statement analysis
  • Demand trends
  • Survey results
  • Progress towards goals 
  • Engagement and traffic results

Predictive Analysis 

This type of data analytics mainly deals with predictive methods. It predicts the future outcomes of historical trends and is more complicated than descriptive analysis. It incorporates the use of artificial intelligence and machine learning. The statistical algorithms can be done through this. 

Some examples of this type of analysis are as follows:

  • Prediction of equipment failures
  • Future resources requirements
  • Population trends
  • Optimization distribution
  • Consumer behavior

Diagnostic Analysis 

To understand how to become a data analyst without a degree, you must know this diagnostic analysis first. This analysis is used to uncover the root cause of the problem. Once you find the core issue, you will be able to curate a target solution in a more effective way. So, using diagnostic analysis will help you in any kind of cyber attack, data breaches, or other marketing campaigns. 

Some of the examples of diagnostic analysis are:

  • Helps to find the root cause of the problem
  • Finding the source of lost revenue
  • Analyzing the risk factors for any data breaches
  • Evaluating the most successful marketing program

Prescriptive Analysis 

This analysis is all about what data analysts should do. This helps them analyze various graphs and proceed with simulation, complex event processes, heuristics, recommended engines, machine learning, and neural networks. 

Some of the common examples of this type of data analysis are: 

  • Content suggestions 
  • Fraud detections 
  • Product development 
  • Scheduling
  • Investment decisions

Get Hands-On Experience on Projects and Case Studies

Data analysis knowledge can help you get an exclusive grasp of concepts and techniques. So, look for some projects that give you hands-on experience. You can start by analyzing case studies in diverse fields like government, healthcare, business, etc. Moreover, by examining the case studies, you will understand the role of data analytics in identifying advanced metrics and steering growth. 

Once you grasp concepts well, you can start working on live projects. Some sites like GitHub, Our World in Data, and Kaggle are a few open-source datasets that can help in building your own projects. Working on real projects can help you understand the problems and the derivation of their solutions. So, start analyzing case studies and get to know the real-life concept issues, which will help you work in a real-time environment. 

Earn Certification 

Now that you have practical experience and a brief understanding of the concepts and degrees and especially if you don’t have a degree, you must get relevant certifications and improve your chances of getting selected by valuable companies. Some of the companies like SAS, Microsoft, and Cloudera offer various relevant certifications. The certifications of these companies are very valuable and recognized globally. Some of the certifications you can get are as follows: 

Create a Strong Data Analytics Portfolio 

Once you get a certification, you will be able to create a strong data analytic portfolio, allowing you to land a better job. Your portfolio is the most important aspect to build to get a real edge. Employers, while hiring, always prefer to see the portfolio to evaluate the necessary skills and knowledge you have. 

So, by applying with a portfolio, you can extract valuable insights from raw data and derive solutions. If you have prior experience in the industry, you will have many past experiences to add to the portfolio. 

Some of the phases of data analysis are as follows:- 

  • Scraping data 
  • Cleaning data 
  • Performing all four different types of analysis
  • Creating data visualizations 
  • Writing a story or report to convey your findings or insights
  • Collaborating with others 
  • Using tools like SQL, R, Tableau, etc. 

Participate in Competitions 

Participating in competition is the other best way to add experience and achievements to your portfolio. These things create a weightage in the portfolio and increase your chances for selection. You can also create your competition and help your community participate, which will allow you to demonstrate leadership skills and techniques. 

Build Your Network

To land a perfect job, you would require a network or connection of people already in this field. It will help you keep your profile updated, and you will be able to analyze and catch the opportunities. There are various social sites like LinkedIn where you can build your connections. You should let your friends, family, and professional circle know that you are looking for a job. Many online communities are there which are full of professional data analysts. You can learn from their experience and get guidance from them. 

Which Tools Should You Be Familiar with for Data Analytics? 

Many tools are there that you need to be familiar with. To be updated in the industry, it is essential to be familiar with these technologies. Here are some tools necessary for an analytics job: 

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Tableau 

Tableau is one of the most powerful data analytics tools that can help you work with data using interactive dashboards and maps. These tools will assist you in analyzing the data in a better way. So, it would help if you also learned to analyze and visualize data using Tableau

It also has an interactive UI, which helps users navigate better and create and explore patterns in a much simpler way. Tableau offers a wide range of services, including mobile and desktop applications. 

SAS

SAS, or statistical analytics software, is a great tool for analyzing statistical data. This tool also allows you to automate SQL functioning and thus allows you to resolve queries faster. While SAS is a proprietary software, platforms like R and Python are open source. 

You can also analyze big data using SAS datasets without any knowledge of any programming language. You also get a built-in error detection mechanism with SAS that can help you debug any errors that you come across while working on it. 

Power BI 

Power BI powered by Microsoft is also one of the critical data analytics tools that you need to keep with yourself while doing data analysis. This tool will help you insightfully visualize your data. 

You can share this visual analysis with organizations you are working with and help them understand data better. The tool contains various components that can offer you various customizations that you can do with your data. 

Its free version allows you to:- 

  • Create charts and graphs from the data
  • Build data models
  • Create and customize a dashboard
  • Sharing visual reports across platforms 

However, if you get the premium version, then you can:- 

  • Integrate with Microsoft Azure
  • Form workspaces to work in collaboration
  • Share your data and customized dashboard with your colleagues on the BI platform. 
  • Embed visuals by Power BI in the Power BI applications. 

Apache Spark 

This Apache Spark tool is a very flexible tool used for large-scale data processing. This machine learning tool needs more computing power and is used in many industries like tech, banks, or telecommunications. You can take this Apache Spark for Data Scientists course from CCSLA to get certified in data analytics.

How Much Salary Does a Data Analyst Expect? 

A data analyst’s salary may vary according to the positions and job profiles. An entry-level data analyst usually gets an average salary of $57,492, and a senior-level data analyst with 10 to 19 years of experience expects to earn $73,452. 

Job Profiles for Data Analysts 

The job profiles vary according to the experience and qualifications. Some common job profiles that you can get as an entry-level data analyst are as follows: 

  • Junior data analyst
  • Operations Analyst
  • Business analyst
  • Quantitative analyst
  • Analytics consultant 

Sectors in Which You May Work as a Data Analyst 

There are various places and sectors with a high demand for data analysts. Some of these sectors are as follows: 

  • Consulting firms – These firms need data analysts to make informed derivation of data so that they can communicate with clients in the most optimum way. 
  • Technology firms – The tech companies are the biggest hub for data analyst employment. Big companies like Google, Meta, Apple, etc., need data analysts to work in their organizations and curate meaningful results. 
  • Financial institutions – Many financials and investment firms require data analysts to utilize their talents and mitigate their risks or threats. 
  • Healthcare organizations – There is a massive demand for data analysts who can improve patient care by analyzing the patterns in data and developing new treatments and techniques based on those patients’ data. 
  • Government agencies – Several government organizations employ data analysts and scientists to work and improve their services by analyzing crime data and developing various social welfare programs. 

Conclusion 

These are some necessary steps you need to take if you don’t know how to become a data analyst without a degree. Though this path is not easy, it is doable. So, give this pathway a try and conquer the industry of data analytics. With such knowledge and experience, you will be able to handle large data sets and perform statistical analysis on large chunks of data. 

You can join CCSLA’s Data Analytics & Engineering boot camp which is a 12-week immersive training program suitable for recent graduates, transitional veterans, and early professionals. This meticulously designed boot camp offers an in-depth curriculum and hands-on experience to guide and support you on your journey toward a successful career as a data analyst.

FAQs

Q1: Is it possible to become a Data Analyst without a degree?

Answer: Yes, it is possible to become a Data Analyst without a traditional degree. Many employers value practical experience and specific skill sets in data analysis, which can be acquired through self-study, online courses, bootcamps, and certifications.

Q2: What are the essential skills needed to become a Data Analyst?

Answer: Essential skills for a Data Analyst include proficiency in Excel, a good understanding of SQL for database management, knowledge of a programming language like Python or R, basic statistical knowledge, and experience with data visualization tools like Tableau or Power BI.

Q3: How can I learn the necessary skills for data analysis on my own?

Answer: You can learn data analysis skills through online platforms like Coursera, edX, Udemy, and Khan Academy, which offer courses in data analysis, statistics, and programming. Additionally, practicing with real datasets and participating in online forums or communities can enhance your learning.

Q4: What kind of projects should I work on to build a portfolio as a Data Analyst?

Answer: Work on projects that involve collecting, cleaning, and analyzing data sets. This could include data from public datasets, sports statistics, financial data, or even your own experiments. Present your findings with clear visualizations and concise reports to showcase in your portfolio.

Q5: How important is practical experience in landing a job as a Data Analyst?

Answer: Practical experience is very important. Employers often look for candidates who have hands-on experience with data analysis projects. Internships, freelance projects, or contributions to open-source projects can provide valuable practical experience.

Q6: Can I start a career in data analysis through internships or entry-level positions?

Answer: Yes, internships or entry-level positions in data-related fields can be a great starting point. Roles such as junior data analyst, data technician, or business intelligence analyst can provide on-the-job training and experience in data analysis.

Q7: What is the role of networking in building a career as a Data Analyst?

Answer: Networking is crucial in building a career as a Data Analyst. Attend industry meetups, join relevant LinkedIn groups, participate in online forums, and connect with professionals in the field. Networking can lead to job opportunities and valuable insights into the industry.

Q8: How do I stay updated with the latest trends and tools in data analysis?

Answer: Stay updated by following relevant blogs, subscribing to data analysis newsletters, participating in webinars and workshops, and joining professional groups or communities focused on data analysis.

Q9: Can mentorship help in becoming a Data Analyst without a degree?

Answer: Yes, mentorship can be extremely helpful. A mentor who is experienced in data analysis can provide guidance, recommend learning resources, help with networking, and offer advice on building a career in this field.

Q10: What challenges might I face when entering the data analysis field without a degree?

Answer: Challenges may include competing with degree holders, proving your skills and knowledge to potential employers, and the need for continuous learning and skill development to stay relevant in the ever-evolving field of data analysis.

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