How to Become a Data Analyst: RoadMap

Photo of author
Written By Billy Chan

A UK-based senior data analyst with a journalism background. Passionate about teaching others how to become data analysts.

My transition from journalism to data analysis was a challenging yet rewarding adventure, filled with self-learning, numerous courses, and plenty of trial and error. Now, I am eager to share the most effective strategies I’ve distilled from my experiences.

Through this roadmap, I aim to provide you with a proven, efficient timeline to successfully land a data analyst job within about six months. It may not be the fastest path, but it is one optimized for understanding, proficiency, and success. Let’s embark on this exciting journey together towards mastering data analysis!

Understanding the Role of a Data Analyst

Before plunging in, it’s crucial to comprehend the role of a data analyst. Data analysts interpret complex data, sift through massive datasets, and assist businesses in making informed decisions, spotting trends, and refining strategies. Their toolkit typically includes SQL, data visualization software, and statistical programming languages.

Also check out our complete guide on How to Become a Data Analyst for Free: Tips for Beginners.

#1 Kickstart with the Google Data Analytics Professional Certificate

Course to take: Google Data Analytics Professional Certificate

Duration: 4 months

Fee: $49 per month or free auditing without a certificate

Traditionally, a degree in a field such as statistics, mathematics, economics, or computer science lays the groundwork for data analysis. However, we’re living in an era where the traditional path isn’t the only one to success. A host of data analysts have emerged from varied educational backgrounds, using online courses and bootcamps as stepping stones to master the needed skills.

Fear not if you lack a data-related degree or any degree at all! Self-learning in data analysis can still be your gateway to securing an entry-level data analyst role. If you’re interested, I recommend reading Can You Get a Data Analyst Job Without a Degree?

Your first leap into the field can begin with the Google Data Analytics Professional Certificate. This course maked the beginning of my journey that led to my first entry-level data analyst position. This comprehensive and popular course, which typically takes around 4 months to complete, equips you with an array of soft and hard skills essential for an entry-level data analyst job.

It delves into various areas, including Google Sheets, SQL, Tableau, and R. If you’re unsure whether to pay for the course or audit it for free, consider reading Is Google Data Analytics Professional Certificate Worth It?

Remember, completion here doesn’t just mean watching all the videos and reading the material. It also entails finishing the capstone project. Despite the seeming lack of novelty, as thousands of candidates have done the same tasks and won’t make it unique on your CV, this project is an invaluable tool to consolidate all the skills you’ve learned. I urge you to tackle the project using SQL or Tableau, given that these skills are highly sought-after for an entry-level data analyst position.

#2 Enhance Your Excel Chart Abilities

Course to take: Excel Data Visualization: Mastering 20+ Charts and Graphs

Duration: 1 week

Fee: $39.99 per month or 1 month free trial

Our roadmap’s next step may surprise you – it’s time to improve your Excel skills. Despite having basic data analysis knowledge and being ready for an entry-level job, as suggested by the Google data course, standing out in a competitive job market requires continuous skill improvement.

Don’t underestimate the power of Excel, especially when it comes to job interviews and actual data analysis tasks. It’s common for hiring managers to ask candidates to present data findings, and if there’s no specific tool requirement, Excel is your best ally for datasets under 1 million rows.

Hiring managers often value the ability to extract meaningful insights from data and communicate them effectively over complex SQL code manipulation. So, if you’re given a week to analyze some data before an interview, why not create a clear and compelling PowerPoint presentation with Excel charts? Or if it’s a spontaneous task, quickly draft charts in Excel for immediate presentation.

Of course, you can practice creating charts on Excel or Google Sheets using sample datasets. But I’d recommend the course Excel Data Visualization: Mastering 20+ Charts and Graphs by Chris Dutton. His exceptional way of explaining concepts and creating dynamic, impressive charts with Excel amazed even the most seasoned data analysts I’ve shown his work to.

Remember, you don’t need to replicate these complex charts for the interview tasks if you’re short on time. However, this course can significantly enhance your Excel skills, helping you impress in interviews.

#3 Boost your SQL Abilities

Course to take: Level Up: SQL

Duration: 1 week

Fee: $39.99 per month or 1 month free trial

As we move forward on our roadmap, it’s time to level up your SQL skills. This is crucial as you’re likely to encounter technical SQL questions during job interviews. Even though you’re aiming for an entry-level data analyst role, and complex SQL functions like regular expressions or CTE tables may not come up, you still need to confidently demonstrate your proficiency with basic aggregate functions and GROUP BY. Understanding how to use inner join and left join for precise table linking is also a must.

This is where the course Level Up: SQL (formerly known as SQL Code Challenge) comes in handy. Even if you feel comfortable with SQL after completing the Google data course, consider taking this course as a real-world application test. It closely mirrors the basic SQL questions you might encounter in a job interview.

At this point, don’t invest too much time learning advanced SQL functions that you might forget without daily usage. This course, while designed for beginners, is an excellent measure of whether you’re interview-ready or not. Keep following the roadmap, and success will be within reach!

#4 Take on the Tableau Weekly Challenge: Makeover Monday

Course to take: Makeover Monday

Duration: Once a week

Fee: Free

Our roadmap now leads us to Tableau, a vital tool in a data analyst’s toolkit. Unlike programming languages like SQL, R, and Python, Tableau combines technology with creativity, demanding constant updates on visualization trends. Although easy to start, mastering Tableau can be challenging, hence why I recommend participating in the free weekly Tableau challenge known as Makeover Monday.

Hosted by Andy Kriebel and Eva Murray, Makeover Monday is a renowned, community-driven project, designed to enhance your data visualization skills. Every Monday, a new dataset is released on their website, accompanied by a brief explanation and the data source. You’re invited to download this data, analyze it, and create an eye-catching, informative visualization that tells the story within the data.

Makeover Monday urges you to think creatively and go beyond standard charts and graphs. It’s an invaluable platform for learning, sharing, and discussing various visualization approaches within the data community.

Andy devotes about an hour each week to showcase how he would visualize the provided dataset. He shares his screen, walking you through each step on Tableau. Having participated in Makeover Monday for almost a year, I’ve found these sessions invaluable.

The best part? You’ll rapidly build a comprehensive Tableau portfolio by creating visualizations weekly, like I did for myself here. So, keep following this roadmap, and let’s dive into the artistic side of data analysis!

#5 Begin Your Job Hunt

Platform to use: LinkedIn

Duration: 4 months

Fee: Free

On this segment of the roadmap, armed with your SQL and Tableau portfolio projects, it’s time to initiate your job search for a data analyst position.

LinkedIn, the leading platform for professional networking, stands as your gateway to a plethora of data analysis job opportunities. It’s a melting pot of professionals across various sectors, giving you the chance to display your skills, connect with potential employers, and discover job openings crafted for data analysts.

Your job hunt needs a tactical approach, merging your qualifications and skills with efficient job search techniques. Optimize your LinkedIn profile, highlighting your newfound data analysis skills and any relevant experience. Keep tabs on data analyst job listings on LinkedIn and other job boards.

If you’re without a degree, don’t fret. I’ve penned an article titled Can You Get a Data Analyst Job Without a Degree? to guide you. For a more extensive exploration of the data analyst job search, check out my other article offering strategies, interview tips, and industry insights. Keep navigating this roadmap and the journey to your dream data analyst job will be smoother.

#6 Honing Your Interview Skills

Platform to use: Big Interview, Interview Master Class

Duration: 1 week

Fee: For Big Interview, it’s free after completion of Google Data Analytics Professional Certificate. For Interview Master Class, it’s $39.99 per month or 1 month free trial

Preparing for a job interview is a critical step in the roadmap to landing a data analyst job. Your performance in an interview can make the difference between getting the job or being passed over. A well-prepared candidate shows potential employers their eagerness, knowledge, and dedication, demonstrating they’re a perfect fit for the job.

In the data analytics field, it’s not just about how well you can crunch numbers, it’s also about how you communicate your insights and how you handle real-world data problems. So, preparation goes beyond refreshing your memory about SQL or Python syntax. You must be ready to showcase your problem-solving abilities and your knack for translating data into actionable insights.

When it comes to interview preparation, I would highly recommend using the platform Big Interview,. After completing the Google data course, you should get free access to this fantastic resource. It’s specifically designed to help you get ready for the kinds of questions you’re likely to face in a data analyst job interview.

In addition, consider taking the course Interview Master Class as a part of your interview preparation roadmap. This course is exceptional. Aimee Bateman, the course instructor, delivers the material with such passion that it feels like she’s talking directly to you. The advice she gives is not only practical but also invaluable for anyone preparing for a job interview.

From the practical question and answer sessions to the application of the STAR (Situation, Task, Action, Result) model, and even guidance on interview etiquette, this course covers all the bases. Being well-prepared will not only boost your confidence but will also ensure that you leave a lasting impression on the interviewers.

Remember, an interview is not just about showcasing your technical skills, it’s also about demonstrating your passion for data analysis, your curiosity, and your ability to learn and grow. So, invest your time in good preparation, and it will undoubtedly pay off in your data analyst job hunt.

#7 Keep Enhancing Your Portfolio

Platform to use:, Kaggle, Google BigQuery

Duration: Until you land your first job

Fee: Free

As we continue along this roadmap, before you secure your first job, I suggest consistently expanding your portfolio. This can increase your chances of getting shortlisted for an interview and sometimes make you stand out during the interview itself.

In your portfolio projects, focus on unearthing insights and suggesting actionable steps from a dataset, instead of diving too deep into the technical code. Remember, many hiring managers are more interested in your ability to apply analytical skills to solve business problems than in scrutinizing your code.

Align your projects with the industry you’re targeting. For instance, if you’re eyeing the media industry, explore website or streaming platform data. If it’s the hotel industry, delve into hotel booking data. This will allow you to discuss your relevant analysis during an interview.

Aim to create SQL and Tableau projects that showcase your abilities. Having at least three of each would put you in a good position. But, more is always better as it gives you a wider range to showcase when faced with an unexpected question. Keep enriching your portfolio and your path on this roadmap to a data analyst job becomes clearer.

Example of Industry-Focused Portfolio Project

1. E-commerce Industry – Customer Segmentation & Sales Performance

Dataset: Online Retail II Data Set from UCI Machine Learning Repository

The Online Retail II dataset contains all the transactions occurring for a UK-based online retailer from 01/12/2010 and 09/12/2011. The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers.

Project Idea:

You can perform a RFM (Recency, Frequency, Monetary) analysis using SQL or Tableau to segment customers into different groups and then devise tailored marketing strategies for each group.

Another possible analysis is studying sales performance, identifying top-selling products, peak sales periods, and geographies with high sales volume. The analysis could drive insights like when to ramp up advertising for specific products, inventory management based on peak periods, or focus on expanding in regions with high sales.

2. Healthcare Industry – Hospital Readmissions Analysis

Dataset: Hospital Readmission Reduction Program from

This dataset includes data on excess readmission ratios for Medicare beneficiaries for each hospital.

Project Idea:

A data analyst could examine factors contributing to hospital readmissions and identify patterns or trends. This could be used to reduce readmissions, improve patient care, and potentially save the hospital money.

You can create dashboards in Tableau to visualize the patterns and present actionable insights. This could involve a correlation analysis between readmission rates and various factors such as diseases, patient age, length of initial hospital stay, etc.

3. Transportation Industry – NYC Taxi Trips Analysis

Dataset: NYC Taxi and Limousine Commission (TLC) Trip Record Data

The New York City Taxi & Limousine Commission has released a comprehensive dataset detailing every taxi trip in the city over the past several years.

Project Idea:

Using SQL or Tableau, you could analyze the data to find popular pick-up or drop-off locations, peak travel times, average fares, and tips. These insights could help a transportation company optimize its routes and pricing strategy.

For instance, an actionable insight could be the identification of high-demand locations and times, which could lead to increased taxi availability in those areas or dynamic pricing implementation.

Remember, for each project, it’s essential to ask the right questions, clean and process your data accordingly, perform in-depth analysis, and then communicate your findings effectively. The final step should always involve translating your findings into potential action items or recommendations for the industry.

Learn Advanced Skills (Optional)

The roadmap above should guide you to land an entry-level data analyst job. But, if you have extra time, or aim for a more demanding job at a high-paying company, or if you don’t have a degree, you might want to show off more advanced data skills.

These advanced data skills fall mainly into three areas: SQL, Python, and statistics.

#8 Advanced SQL

Course to take: Intermediate SQL for Data Scientists, Advanced SQL for Data Scientists

Duration: 2 weeks

Fee: $39.99 per month or 1 month free trial

In the world of advanced SQL, knowing how to use CTE, nested queries, partitions, and regular expressions well can set you apart. These are crucial skills for a data analysis career. Your future boss will likely value these advanced SQL skills more than any online machine learning skills you might have, as these are more relevant to everyday data analysis.

The best courses for advanced SQL are Intermediate SQL for Data Scientists and Advanced SQL for Data Scientists taught by Dan Sullivan. When I started with SQL, these courses were a revelation. They cover practical topics useful in the real world of analysis, even though they’re designed for data scientists. Both courses will take your data cleaning and manipulation skills to a new level.

#9 Python: Numpy, Pandas and Pyplot

Course to take: Python Data Analysis, Practical Python for Data Professionals

Duration: 4 weeks

Fee: $39.99 per month or 1 month free trial

It’s no secret that Python is a key player in the data industry. While Python might not be critical for an entry-level data analyst job, it’s a beneficial skill to add to your repertoire, especially once you’re comfortable with SQL and Tableau and have time to broaden your skills.

The Google Data Analytics Professional Certificate doesn’t touch on Python, so my first stop for Python training was the popular course – Python for Everybody, taught by the well-respected Dr. Charles Russell Severance. I spent 4 months completing the entire course. It’s in-depth, with lots of coding and homework. However, Dr. Charles makes learning fun with intriguing examples. I personally like how he always arbitrarily named his colleagues and friends when talking about the most abstract concepts.

Despite its reputation, this course doesn’t focus on data analysis. It doesn’t cover Numpy, Pandas, or Pyplot, which are vital libraries for data analysis with Python. So, as a data analyst, you might find it hard to apply these skills immediately.

That’s why I’d suggest two other courses I’ve taken: Python Data Analysis and Practical Python for Data Professionals. They’re shorter, with each lasting about two hours, and they focus solely on data analysis. Both are well-explained and very practical. I recommend starting with these two introductory courses and then moving onto more advanced Python as your career progresses. This way, you can ensure your learning roadmap suits your career progression.

#10 Essential Statistics

Course to take: Build Your Analytical Skills with Statistical Analysis

Duration: 2 weeks

Fee: $39.99 per month or 1 month free trial

As a part of our roadmap, learning statistics can make you stand out even if it’s not usually required for entry-level data analyst jobs. Knowing the basics will help you both in interviews and once you start working.

Learning statistics may seem scary, especially if you don’t have a background in science or math. I was the same way, having not used math much in my career before becoming a data analyst. But this learning journey includes four easy-to-follow introductory statistics courses.

Eddie Davila, the course instructor, does an excellent job of simplifying complex concepts using many examples. As you grow in your data analysis career, you’ll encounter topics like probability, normal distribution, and hypothesis testing. These courses lay a solid foundation, allowing you to slowly incorporate statistical concepts into your work. This knowledge could also impress interviewers if you can apply it to interview questions.

Extra Reading: Examples of Applying Statistics to Data Analysis

  1. Probability:
    Suppose you are a data analyst for an e-commerce platform. You have been asked to forecast the potential sales for a new product. You might use probability to predict the likelihood of a customer buying the new product. By analyzing the past buying behavior, you can calculate the probability of a customer making a purchase.
    Example: If 100 out of 1000 customers bought similar products in the past, you could estimate there’s a 10% (100/1000) probability that a given customer will buy the new product.
  2. Normal Distribution:
    Let’s say you work as a data analyst for a manufacturing company and you’re interested in quality control. You might look at the lengths of an item the company produces, which we’ll assume follows a normal distribution (this is often the case in manufacturing). The normal distribution, with its mean and standard deviation, will allow you to identify outliers and see if the production process is under control.
    Example: If the mean length of the item is 5 cm with a standard deviation of 0.5 cm, and you find an item that is 7 cm long, it would fall outside the typical range (mean ± 2 standard deviations, or 4-6 cm), indicating a possible issue with the production process.
  3. Hypothesis Testing:
    If you are a data analyst for a software company that has just launched a new feature, you might want to determine if the new feature is increasing user engagement.
    Example: You could set up a hypothesis test where the null hypothesis is that the new feature has no effect on user engagement, and the alternative hypothesis is that the new feature increases user engagement. You would collect data on user engagement both before and after the feature was implemented, and use statistical methods to determine if there’s a significant increase.
    If the result is statistically significant, you could reject the null hypothesis and conclude that the new feature likely has a positive impact on user engagement.

Networking Versus Skill Acquisition

There’s a common misconception that networking, in the context of the data world, is a silver bullet for landing your first job. Beginners are often advised to reach out to data professionals, attend meetups, and actively engage on LinkedIn, hoping these connections will open doors to their dream job. But is it always the best use of your time, particularly when you’re starting from scratch?

Unfortunately, the answer is not as straightforward as one might hope. Yes, networking can be beneficial in certain situations, but not always for beginners with no prior experience. Data professionals, just like professionals in any other field, are more inclined to connect and collaborate with individuals who can offer reciprocal value. This may sound harsh, but it’s a practical aspect of professional networking. 

For a beginner with no experience, reaching out to seasoned professionals expecting job offers or meaningful engagements may be akin to putting the cart before the horse. You could end up spending hours curating personalized messages, attending networking events, and maintaining these relationships, but with little to no return on investment.

Here’s a fresh perspective: As a beginner, focus on what you can control, i.e., building your competencies. Investing your time in learning data analysis skills and obtaining recognized certifications will yield a better return in the early stages of your career. Certificates from reputable platforms not only equip you with the necessary skills but also serve as evidence of your commitment and competence in data analysis. 

Instead of knocking on doors with nothing but a name and a desire to connect, imagine knocking with a portfolio demonstrating your understanding of Python, SQL, or your ability to derive insights from a complex dataset. That’s a game-changer. Professionals and potential employers will be more receptive because you’re not just asking for opportunities; you’re showcasing what you bring to the table.

Don’t get us wrong; we’re not dismissing networking altogether. Networking has its place and can be beneficial, especially as you grow in your career. But as a beginner, it’s essential to prioritize what will move you forward fastest. In this case, it’s acquiring relevant skills and certifications. Networking can wait. Your growth cannot. 

So, go forth and conquer your learning journey. With time, a solid skillset, and the right certification, you’ll find that people—yes, even data professionals—are more than willing to network with you because you’re no longer just a beginner; you’re a budding data analyst. And that makes all the difference.


The path to becoming a data analyst is a journey of constant learning and development. It may seem challenging initially, but remember, every expert was once a beginner. By following this roadmap, you are taking the first step towards a rewarding career in data analysis. So take the plunge, stay curious, and enjoy the ride! Remember, the field of data analysis isn’t just about numbers and code, it’s about translating these numbers into insights that can make a difference. Your future as a data analyst is waiting. Start building it today!

Leave a comment