The Great Resignation is the perfect time to leave your unfulfilling career behind to become a data analyst.
Never before has there ever been such a mass exodus of people looking for better opportunities that encompass room for advancement, flexible working conditions, respect in the office, and fair wages.
With so many individuals looking for a fresh start, it’s no wonder that many of them are looking to the alluring world of tech to provide the change they need. Data science, analysis, and engineering are maintaining a foothold as some of the most sought-after jobs for those looking to make a career change.
While you may think that the three disciplines mentioned above are the same, they differ significantly in their qualifications and job duties. For those looking to make a quick switch to a different field without having to spend years studying, becoming a data analyst may be the right fit.
Data analysts are gifted with the ability to tell a story using data. Taking existing data, they use tools such as SQL, Excel, and data visualization tools (such as Tableau) to produce visualizations and reports that describe in laymen’s terms the current landscape of the data. Job duties include determining business problems, pulling data from databases using SQL, conducting analyses and producing visualizations using Excel and visualization tools, and developing reports that present the findings of the analysis for stakeholders. Simple coding skills in R or Python may also be required depending on the job, and simple mathematical skills in arithmetic, algebra, statistics, and probability are standard.
With data analyst positions treading the line between a new challenge without having to spend years in school, it’s a no-brainer for those looking to make a career change.
The Roadmap
Background
Most roadmaps toward any data-focused position or career change are often so extensive that they would take a couple of years of full-time study to complete. The average worker takes 11 months to plan a career change, leaving most of these roadmaps out of touch with what people need. Furthermore, external factors, such as the amount of savings and life commitments, can make it impossible to complete a career change in an amount of time that is feasible for most, leaving many to stay at their previous jobs. Considering all of these factors, this roadmap has fine-tuned the process of becoming a data analyst into seven short phases that can be completed as quickly or as slowly as necessary.
Remember, to make a career change you only need to know enough to get a job. In-depth learning can take place on the job once you have satisfied the basic requirements and can carry out the daily tasks.
The following seven phases should be followed in approximately this order. Within these sections, you will find relevant online courses to get you started, as well as key things to focus on in each phase.
Phase 1: Programming – Excel, SQL, Python/R
Programming requirements for data analysts can vary widely across fields and positions. However, most data analyst positions can be fulfilled with knowledge of Excel, SQL, and some simple Python or R.
While Excel may seem old school, it’s still a much-used and very valuable tool that is easy to learn and produces quick results that explain the current state of the data. Excel is mostly used for analyzing the data to determine summaries and trends. SQL is used to retrieve data for analysis from databases that can then be worked on using Excel or a programming language. Python and R are, essentially, the two go-to data analysis programming languages, with Python being more popular than R due to its ease of use.
Online courses you can take to learn programming skills:
Below is a selection of courses that can be used to quickly learn the fundamentals of the programming required for data analysis. By no means is this an extensive list or the only resources out there for learning how to code, but these resources give a good starting point. Remember, this is a roadmap to get you the basic skills necessary to be able to fulfill a data analyst position and carry out their daily tasks, therefore breadth of programming knowledge is the goal here, not depth. It’s also important to note that most data analysts pick between Python or R and make that their primary programming language. Make your decision based on that which is used most predominantly in your target industry.
End of phase objectives:
At the end of this phase, you should be able to write code that runs in your chosen target languages. Extensive knowledge of each language is unnecessary except for the parts that are necessary to conduct an analysis.
It’s important to note that programming is a task best learned through doing. You can sit and watch these videos and claim to know how to code by the end of them, but there is a distinct difference between watching and doing. Therefore, it’s advised to work along with the videos and then practice writing your own code afterward to see if you can still make it work. Further practice in writing code will occur in later phases.
Phase 2: Mathematics – Arithmetic, Algebra, Statistics, and Probability
If you completed high school/secondary school, or the first couple of years of college or university courses, you will have generally learned all of the mathematics you need to know to become a data analyst (field and position-dependent). Cool, right? Unlike data science where you need to have a deep understanding of linear algebra, calculus, and further mathematical disciplines which can take years to master, most data analyst positions will require just the basic mathematics that you learned early on in your education.
Therefore, this phase should just be a review of what you have already learned and possibly knocking the cobwebs off your statistics and probability knowledge. The courses below are from Khan Academy, a favorite for learning math through bite-sized, easily digestible lessons that explain concepts in simple terms for individuals at any level.
Resources you can use to refresh your math skills:
Algebra 1 | Math | Khan Academy
End of phase objectives:
At the end of this phase, you should be able to carry out simple calculations that could be used to find insights into data. For example, you should be able to calculate the mean value of salaries to determine what a company is paying its staff on average. Alternatively, you need to be able to determine who the outliers are in an age group of people who subscribe to a website newsletter. Or, you need to be able to calculate unknown values in a data set that is looking to determine the velocity of sparrows but only gives the distance and time of their flight.
The mathematical skills developed in this phase will be practiced and enhanced in later phases.
Phase 3: Data Analysis – Collecting, Cleaning, Analyzing
Data analysis is the meat and potatoes of what a data analyst does.
Data analysis can be broken down into five steps that are universally recognized.
- Determine the driving question: Recognizing the driving question or overall objective of the data analysis helps set the stage for what data you need and what the result of the analysis should reveal.
- Data collection: Defining the data you need to answer your driving question will help you collect the right data for the job. At the end of the day, it’s better to have more data that gives a clear picture of what you’re looking for than too little data.
- Data cleaning: It’s time to turn the raw data you’ve collected into something you can analyze through the data cleaning process. This involves amending or removing incorrect or irrelevant data, checking for data completeness, removing duplicates, or producing important values, such as means or averages.
- Data analysis: Analyzing the data you’ve cleaned involves looking for insights within the data, including patterns, relationships, and the possibility for predictions.
- Interpretation: This phase involves producing visualizations of the results of your data analysis. These visualizations will tell the story of your data and will answer the question in a manner that’s easy to comprehend and straight to the point.
Resources to help you learn the data analysis process:
End of phase objectives:
By the end of this phase, you should be able to conduct a full data analysis following the steps listed above using the programming and mathematical skills learned previously. Complete fluency in these stages is not required yet as there will be a phase later on where portfolio projects will be created that will enhance the skills learned here.
Phase 4: Data Visualization
While data visualization is taught as part of the five steps of data analysis, it gets its own special phase here because it plays such a large part in the daily tasks of data analysts. Being able to create compelling, accurate visualizations is one of the fundamental cornerstones of this career, therefore special attention should be paid to these skills.
Tableau is one of the most common tools used by data analysts to create visualizations, and resources to learn it will be included below. Other data visualization tools include QlikView, Microsoft Power BI, Datawrapper, Plotly, and more that may be relevant depending on your target field or position. This roadmap will focus on one of the most prominent, Tableau.
Resources to help get you started with Tableau:
End of phase objectives:
By the end of this phase, you should be able to create data visualizations that accurately represent the findings of your data analysis. These visualizations must be eye-catching, easy to understand, and must provide a clear picture of the results without skewing the information visually.
Phase 5: Industry Knowledge
Industry knowledge is the industry-specific tool you will use to solve problems daily.
For example, a background in forestry practices and forest management is important if you are to be conducting data analyses on how many trees should be cut each year to ensure a healthy forest grows. Alternatively, you need to have the business acumen to be able to determine if a company is in trouble financially based on the results of your analysis. Furthermore, if you were to work as an analyst for a hospital that is seeking to find the most efficient way to schedule staff during busy periods, it’s important to have an understanding of the important roles that each person plays in a hospital.
Essentially, you have the skills to conduct data analyses, but now you need to gain the skills and knowledge to be able to apply them to a specific industry. Whether you’re entering a completely different industry or are wanting to return to your current industry as an analyst, it never hurts to brush up on industry knowledge. Studying the latest can help keep you relevant as an analyst who can identify trends, see patterns, and improve the efficiency and effectiveness of those around them.
Gaining industry knowledge can be done by reading journal articles, attending lectures at universities, listening to podcasts, reading newsletters, talking to people in the industry, and more.
End of phase objectives:
By the end of this phase, you want to be able to conduct analyses that are relevant and provide insightful conclusions to problems in your target industry. For those looking to work in business, this means conducting analyses to determine the cost-benefit structure of a company decision. For those looking to work in the sciences, this means being able to disseminate conclusions from piles of evidence that may or may not support a hypothesis. For those looking to work in healthcare, this means being able to conduct analyses that provide insight into the working conditions required to maintain a happy staff. The point is that you need to be comfortable analyzing the data of a particular industry and be able to provide conclusions to the problems you are trying to solve.
Phase 6: Portfolio
Building your personal portfolio is the fun part of this roadmap.
Here, you get the chance to build personal data analysis projects that showcase your skills and prove to employers that they want you on their team. These projects should include all of the skills you’ve learned thus far and should be relevant to your target industry. This means that they use the tools of the industry, draw meaningful conclusions, and perhaps even add to the knowledge base of the industry.
These projects can be unique, can be inspired by those seen on the internet, or can include pro bono work done for a small company in your area. A quick browse of the internet can give you a bunch of ideas and datasets you can use to create your own projects.
Resources to get you started in building your data analysis portfolio:
End of phase objectives:
By the end of this phase, you should have a portfolio that is full of data analysis projects that can be shared with potential employers. This portfolio should showcase not only your skills but also your best work. Portfolios are great tools for employers to see what you can do for their company, and can also provide beneficial talking points during interviews. Not only that, but portfolios give you the chance to put all of your skills together to produce projects that yield results.
Phase 7: Networking, Job Applications, and Technical Interview Prep
The final phase of our roadmap brings us to networking, job applications, and technical interview prep.
You have the skills.
You’ve built your portfolio.
Now you just need to tie it all together and get that job!
Networking will involve attending conferences and industry events, talking to recruiters, and rubbing elbows with people who are currently analysts who may be able to put in a good word for you. While no one likes networking, it’s one of the surest ways to get a job in the industry without prior experience. If a recruiter or hiring manager can meet you in person, the chances are high that they will hire you on attitude and teach you the rest later.
Job applications will come next, followed by preparing for technical interviews. While these two aspects are beyond the scope of this article, several resources will be listed below to give you a start in the right direction. In terms of both, it’s better to over-prepare so that on the day of, you can relax and enjoy the experience. Job applications and technical interviews are great ways to find out more information about the position, what skills you’re lacking, and the types of technical interview questions you should be prepared to answer. While tedious, this phase is a great learning experience that will further cement everything you have already learned.
Resources to get you prepared for networking, job applications, and technical interviews:
Anaconda | Networking in Tech: Find Your Dream Data Science Job
10 Tips for Completing a Job Application to Get an Interview
End of phase objectives:
By the end of this phase, you should, ideally, have a new job as a data analyst!
All of your hard work should be paying off and the light at the end of the tunnel should be shining.
It’s important to remember that it’s normal to have to go through several interviews with many different companies when trying to break into a new role you’ve never done before. The trick is to keep moving forward no matter the results and try to learn what you can from each experience. The difference between those who get data analyst jobs in less than a year and those that don’t is that successful individuals keep pushing forward no matter how many rejections they suffer. At the end of the day, you know that you’re capable of becoming a data analyst, you just need to find the right company that agrees with you.
Final thoughts
Carrying out a career change into a new field that you have no previous experience in is an impressive endeavor.
While it no doubt seems intimidating, the roadmap toward becoming a data analyst is more manageable than most. Without having to worry about abstract mathematics, incomprehensible programming, and difficult concepts such as machine learning and Artificial Intelligence, you open yourself up to instantly becoming a storyteller and a key figure in the decision-making process of companies.
As mentioned previously, this is by no means a comprehensive list of resources that you can use in becoming a data analyst. The goal of this roadmap is to give the bare bones approach that can be completed through part-time study in a short amount of time to facilitate a quick career change. Some individuals may require more resources and time while others require less. However, by following this roadmap, you can begin to form your path toward becoming a data analyst.
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