6 Steps to become a data analyst and a data scientist - A Guide for Beginners

If you find yourself in a position where you lack relevant experience in data science and lack a degree in closely related fields like mathematics, statistics, computer science, or their counterparts, don't despair. There's a path for you to embark on a journey toward securing a job as a data analyst or data scientist. These are the steps I've traversed in my journey (currently at step 5), created specifically for individuals like me who are starting with no prior experience and are unsure where to begin. My hope is to save you time on researching the intricate details and steps that you need to navigate. Keep in mind that everyone's journey may vary, so feel free to explore shortcuts or additional steps that resonate with you.

Step 1. Understand the Data Analytics/Science Job

Start your journey by gaining a comprehensive understanding of what data analytics and data science entail. This foundational step is crucial to avoid wasting time and effort. Before immersing yourself in this field, ensure you grasp the core concepts of the profession, understand your daily responsibilities, anticipate your future employer's expectations, and align your personality with this career path.

I recommend beginning with the YouTube channel of "Alex The Analyst," a well-known data analyst. His explanations are tailored for beginners, making complex concepts easy to understand. His journey is relatable, given that he did not start in a data-related field but eventually became a beloved data analyst, inspiring many like us. Watch his videos on "Top 5 Reasons Not to Become a Data Analyst," "What Does a Data Analyst Actually Do?" and "Data Scientist vs Data Analyst | Which Is Right For You?"

Explore Alex The Analyst's YouTube Channel

Step 2. Begin Your Learning Journey

With a fundamental understanding in place, you're now ready to delve into the world of data science. You may be wondering where to start, what books to read, which courses to take, or even whether to apply to a school. Rest assured, I've been in your shoes. Initially, I purchased a Python book from a bookstore, only to later discover numerous free online resources that can teach me the basics of Python.

If you're considering graduate school for a data science major, take note that some level of programming experience and background knowledge is often required.

Here are some excellent online academic resources that cover various aspects of data analysis and data science:

Coursera : https://www.coursera.org/

Coursera is an online learning platform offering a wide array of courses and educational programs from renowned universities, colleges, and organizations worldwide. Qualified instructors guide these courses, with assignments, quizzes, and exams to ensure your understanding. You can retake exams and courses if needed.

These courses were particularly beneficial in my journey:

• Google Data Analytics: Google provides a certificate upon course completion.

• Python for Everybody: This is one of Coursera's most popular Python courses, taught by a professor from the University of Michigan. It's beginner-friendly, with practical assignments.

• Learn SQL Basics for Data Science: A fundamental course for analytics.

For those interested in data science, consider these two relatively short courses:

• Essential Linear Algebra for Data Science

• Getting Started with R

If you're not familiar with Excel, you may want to consider the course:

• Excel Fundamentals for Data Analysis

DataCamp

DataCamp is another online platform focused on data science and analytics education. It offers various courses and interactive coding exercises to develop skills in data analysis, machine learning, and programming in various languages. The platform's step-by-step approach, with short video lessons followed by immediate practice, was especially effective for my learning journey.

I believe that these additional skills, Machine Learning and Power BI, will have further equipped me as a good candidate for a data scientist.

Step 3. Transitioning to Real-World Application

As you shift from learning to practical application, it's not uncommon to encounter frustration and uncertainty, a path I myself traversed. When I endeavored to build my portfolio, the programming skills acquired and certifications earned from platforms like Coursera and DataCamp often fell short in the competitive job market. Insights from seasoned data analysts and hiring managers emphasized the need for personal projects, driven by genuine passion rather than guided exercises. As I embarked on developing my own projects, I faced the task of installing and mastering various programs commonly used by data analysts and scientists. Remember, when you encounter challenges, don't give up—instead, utilize error messages as opportunities for learning and growth, gradually honing your developer skills. Below, I've curated a list of tools you'll likely need for your projects.

GitHub: GitHub, a web-based platform, not only provides version control but also facilitates collaboration on software development projects, enabling multiple users to work on code simultaneously. Its features include change tracking, repository management, and teamwork facilitation. I wish I had discovered this platform earlier; I now use GitHub to host my own website and organize my code. Your repositories' links will later connect to your portfolio webpage, a practice highly regarded in the developer community. The sooner you become proficient with GitHub, the better it is for your career.

LinkedIn: Since I lacked prior work experience, I didn't initially have a LinkedIn account. Make it a habit to upload your certificates each time you achieve a milestone, bolstering your online professional presence.

MySQL: MySQL, an open-source relational database management system, excels in storing and retrieving data for a wide array of applications. Utilizing structured query language (SQL), it efficiently manages and manipulates data. While I initially explored Microsoft SQL server management studio, I found MySQL to be easier to install and work with for database management.

Jupyter Notebook: A revelation for me, Jupyter Notebook quickly became an essential platform for programming work. It closely aligns with my learning journey, enabling code execution cell by cell, making it perfect for data analysis, visualization, and iterative development with immediate feedback.

Kaggle: This website is an excellent starting point for your own projects. It hosts data science competitions and provides datasets for machine learning and data analysis. You can practice with projects and explore datasets, setting the stage for your independent endeavors.

Step 4. Create your own portfolio website

Creating your own website portfolio is an exciting step, and it can be done at no cost by following YouTube tutorials. There are two primary ways to craft a website: using Wix.com or GitHub.

Wix.com is a widely-used, cloud-based website builder that empowers users to design and personalize websites without demanding extensive technical expertise. It offers a variety of templates, design tools, and hosting services, making it accessible for individuals to establish an online presence. I personally created my blog using Wix, and it provides a user-friendly way to manage and modify content, ideal for those who prefer not to delve into coding.

Alternatively, you can opt for GitHub to create your website. To build a website through GitHub, you can host a repository containing HTML, CSS, and JavaScript files. Afterward, activate GitHub Pages in your repository settings to make your site accessible online. Following a YouTube tutorial, I learned how to leverage this method to craft a unique website. Watch the tutorial here.

To get started, you'll need to download template codes from html5up.net and work on them using Visual Studio Code.

You can see an example of a website that I created using this method at Soyoung, the Data Analyst. I employed AI (midjourney) to generate a personalized image for the background photo. Feel free to explore it for inspiration and ideas as you embark on your own website-building journey.

Step 5. Make your own project

I believe it's common to tackle this step before creating your portfolio, but I took a different path by building my portfolio website first. It served as a catalyst for me to populate the project section. Moreover, structuring your journey and skills through the portfolio site provides a holistic view of your work and accomplishments, motivating you to forge ahead. Crafting a website injected fresh enthusiasm into my project work, and I'm eagerly looking forward to filling up the project section to complete my portfolio.

When it comes to sourcing data for your data science projects, numerous reputable options are available for your portfolio, including Kaggle, UCI Machine Learning Repository, Data.gov, World Bank Data, Google Dataset Search, and data from your local government.

I recommend aligning your project choices with your passion or prior work experience. In my case, my college major was in genetics, so I'm on the lookout for data related to genes, DNA, or RNA. Additionally, working as a substitute teacher in public schools allows me to access education-related data. As many experts advise, finding your unique niche can make you stand out in the hiring process.

Step 6. Make your resume and apply

I find myself in a blend of excitement and apprehension at this juncture. Drawing from insights shared by numerous YouTubers who have ventured into the field of data science, I've distilled a set of invaluable tips:

1. Create a Concise Resume: In the current landscape, a one-page resume is the preferred format. Consider incorporating a link to your portfolio website, where you can proudly showcase the projects and skills you've amassed.

2. Cast a Wide Net: Don't hesitate to apply for numerous opportunities. Many successful professionals in the field applied to over a hundred positions before securing their ideal role.

3. Prepare for Interviews: Anticipate that interviewers will likely inquire about your projects. Be well-prepared to explain and discuss your work in detail.

4. Exercise Patience: Expect a series of rejections along the way. It's a common experience, especially for individuals without prior experience or formal degrees in the field. Despite setbacks, maintain your resilience and motivation.
   

This is a shared journey for many job seekers, and while awaiting your first position, channeling your energy into new projects can be a rewarding pursuit. We're all part of this collective journey, and with determination, we will ultimately achieve our goals.