HOW I BECAME A DATA ANALYST WITHOUT ANY COMPUTER-RELATED DEGREE Part II: Skills and Tools
In my previous post in the series: How I Became Data Analyst without any Computer-related Degree part I. I shared the process of researching the data industry which led to my decision to become a full-time data analyst. This time, I will share the skills and tools that got me the first job in the data world.
Part 1: Researching- The Journey Begins
Part 2: Skills and Tools- Get the Right Weapons
Part 3: Show your work- Time to Shine
Part 4: Job Hunt Process- You Are Almost There!
A data analyst typically requires a combination of technical skills, an analytical mindset, and familiarity with relevant tools and technologies. Here’s a breakdown of the key skills and tools required for an entry-level data analyst role:
I. Technical Skills:
From my experience, technical skills are the easiest to learn among other skills required for a data analyst. As I was transitioning to a data career from a different background, I aimed for an entry-level position only. Everything I needed at this point was one foot to the door and learned faster by interacting with the real business challenges. I spent 3–4 months learning technical skills while making connections with data professionals and showcasing my work.
Note: I will update each part weekly from now on, if you are interested in my story, feel free to subscribe and sign up for the email notification for the next post so you won’t miss out on any good tips and information :)
- Excel: Everybody uses Excel nowadays. However, just knowing Excel is not enough to work effectively as a data analyst. Mastering certain formulas can greatly enhance your ability to manipulate and analyze data proficiently.
Here are the 15 basic Excel formulas with examples you can start with.
After understanding the uses of these formulas, you should apply them to practical projects. By actually working on a business issue, you can stimulate yourself as a data analyst and try to find insights (the goal 🏆). This practice not only helps to strengthen your technical skills but also improves for critical thinking skills. To simplify the process, I always start with the topic that I am interested in, it can be analyzing sports games, neighborhood activities this summer, or your hobbies. Do not need to force yourself to work on a complicated business challenge right at the beginning, everything takes time :)
- If you have no idea where to start, you can try my Food Delivery Analysis project.
- Or follow step-by-step with professionals such as Chandoo and MyOnlineTrainingHub and so many free YouTube tutorials. You know it!
2. SQL (Structured Query Language):
SQL is the next skill you need to know as it is a powerful tool that allows data analysts to communicate and manipulate the database. Whether you’re working with small datasets or large enterprise databases, SQL provides a standardized way to retrieve, update, and manage data.
For me, SQL is a harder skill compared to Excel to learn, and takes a lot of effort to master it. It involves more coding skills to write the queries so it is a new concept for you if you have never done coding before. But once you know how to write the queries and extract the data from the database quickly, you will love it so much!
Again, always start with the simple definitions, and basic functions then advanced queries for complicated datasets. You can try my project in finance here or healthcare here. Also, YouTube is your best friend, you can easily find some good tutorials such as Alex The Analyst series and so on. I also really like this post on LinkedIn, you should check it out.
More information: Top SQL Uses
3. Programming languages:
Proficiency in languages such as Python or R for data analysis, manipulation, and visualization. I learned both Python and R but so far Python is still my favorite which is also one of the skills that my current employer asked me during my interview (and got me a job I guess).
I was unsure before starting to learn Python. I had a phobia of coding as I had no idea about it. I thought it would be heavily programmed with extensive brain work (just like in the movies). But well, I survived and found joy in working with this new language. I do not know a lot of other computer languages but for me, Python is quite a straightforward language with powerful applications for data analysis, development, and so on.
Everything you need is patience and trust in the process!
I learned the basics from data camp, again YouTube, and the Udemy course of Dr. Angela Yu (100 Days of Code: The Complete Python Pro Bootcamp). The next step is to put what you learn into the projects, I completed this mining project with Python here and followed a few guided projects on YouTube.
4. Data Visualization and Story Telling Skills:
Someone said “ A good visualization can replace a thousand words” and I think it is absolutely right. Proficiency in tools like Tableau, Power BI, matplotlib/seaborn in Python, or even Excel for creating insightful visualizations to communicate findings effectively. By using effective color indicators, filters, and different chart types along with great storytelling skills, the insights will be delivered easily to the stakeholders for the answers they are looking for.
Learning visualization tools such as Tableau and Power BI is easier than learning Python (which requires coding skills). Tableau and Power BI are user-friendly platforms with powerful analytical capabilities. They both support a wide range of data sources with drag-and-drop functionalities to make the visualization process simpler for users. You can start with basic knowledge of their free versions while following the tutorial on YouTube or checking my post here.
My favorite book for visualization you might want to read:
- Effective Data Storytelling by Brent Dykes
- Storytelling with Data: A Data Visualization Guide for Business Professionals by Cole Nussbaumer Knaflic
5. Advanced Skills
Being a data analyst is a long and continuous journey, you will be required to always update new technology, new skills, and new challenges. There are some studies I am focusing on to upgrade myself in the future.
- Statistical analysis: Understanding of statistical concepts and methods for analyzing data distributions, correlations, hypothesis testing, etc.
- Machine learning: Familiarity with basic machine learning concepts and algorithms for predictive modeling and pattern recognition.
- Data manipulation: Proficiency in libraries like Pandas in Python for data manipulation and transformation tasks.
- Data wrangling techniques: Ability to clean, reshape, and merge datasets to prepare them for analysis.
- Database management systems (DBMS): Understanding of database concepts and experience working with systems like MySQL, PostgreSQL, or SQLite.
- Data warehousing: Knowledge of concepts related to storing and retrieving large volumes of data efficiently.
II. Analytical Skills:
It seems like a lot of technical skills to learn at first but in my opinion, they are easier to get than analytical skills. If you spend 4–5 hours a day learning technical skills, you could use these tools effectively in 3–4 months. However, analytical skills do not follow that timeline. You need to work on the actual datasets, practice problem-solving with case studies, and try to solve the challenges and find insights that bring value to the business. That is the key to success.
- Critical thinking: Ability to logically analyze and interpret complex data.
- Problem-solving: Capacity to identify issues, formulate hypotheses, and devise solutions.
- Attention to detail: Precision in examining data for accuracy and consistency.
- Data interpretation: Capability to extract insights and draw meaningful conclusions from data.
Free datasets that you can try out:
- Kaggles
- Government Open Data
- GitHub
- Google Dataset Search
- Data. world
Or experience the virtual internship with certificates: https://www.theforage.com/
6. Communication Skills:
Communication is required in every aspect of business activities. As a data analyst, communication is essential to ensure the message is transferred carefully and correctly. Asking the right questions is a skill that I underrated before beginning my career but I learned that I can save so much time and effort if the key questions are asked and the requirements are clear upfront.
- Written communication: Ability to document findings, methodologies, and insights clearly and concisely.
- Verbal communication: Capability to present complex technical information clearly and understandably to non-technical stakeholders.
7. Domain Knowledge:
- Understanding the specific industry or domain in which you are working can greatly enhance your ability to derive meaningful insights from data.
8. Continuous Learning:
- Data analysts need to stay updated with emerging trends, techniques, and technologies in data analysis and related fields.
Mastering these skills and tools will equip you to excel in a data analyst role and contribute effectively to data-driven decision-making processes within organizations. Additionally, gaining practical experience through internships, projects, or online competitions can further enhance your proficiency in these areas.
Remember to follow the steps:
Learn the knowledge → Apply it to real-world projects → Showcase your skills
Hi friends,
My name is Tris, and I have recently switched my career from a chemical technician to a data analyst. I wrote about my experience, my projects, and the lessons I learned in this Medium. I hope my content somehow strengthens your journey to get better tomorrow.
Starting this Fall, I came back to school to continue my studies in Bachelor of Science while working as an analyst. I know it will be a challenge again but I prepared for it :) I created a YouTube channel to motivate myself and maybe YOU to reach our goals. Enjoy the music and please subscribe for my future content.
👩💻Let’s connect: My LinkedIn
Have a Good Day!