Data careers, skills & tools. Reddit AMA recap
Real question from real people, answers that can boost your data analytics
👋🏽 Hey, it’s Ismail. Welcome to data nomads lab newsletter on learning data analytics, career growth, networking, building portfolios, and interview skills to break into tech role as a high-performer.
Hello Readers,
I recently hosted an AMA in a Reddit community I am a member of, inviting questions on related to data analytics, from its fundamentals and how to get started to the essential skills and emerging career opportunities with an overwhelming response that ranged from entry-level basics to the impact of recent AI advancements on the industry.
I have compiled the most valuable insights from our discussion into this comprehensive guide to help others looking to break into this dynamic field.
Lets get started!
Data analytics has emerged as one of the most promising career paths in today’s digital economy. As organizations continue to recognize the value of data-driven decision making, the demand for skilled analysts continues to grow.
In this newsletter, I will share insights from my experience in the field that I shared during the AMA to help you navigate your journey into data analytics.
Building your professional network
Success in data analytics isn’t just about technical skills, it’s also about building meaningful professional connections. The key is being proactive and genuine in your networking approach:
Join LinkedIn groups focused on data analytics and actively participate in discussions
Engage with online communities like Reddit’s data analysis forums and relevant Discord servers
Share your learning journey and projects on LinkedIn
Connect with professionals for coffee chats, being specific about what you hope to learn
Contribute meaningful insights to industry discussions
Creating an impressive portfolio (show not only tell)
Your portfolio is your chance to demonstrate real-world problem-solving abilities. Here’s what to focus on:
Build projects that solve actual problems using public datasets
Include diverse projects showcasing different tools (Excel, Python, SQL, Tableau, MS Power BI)
Ensure all code is well-documented on GitHub
Focus on demonstrating your ability to clean messy data, derive insights, and communicate findings effectively
Essential skills for entry-level success
The skills needed for data analytics can be grouped into three main categories:
Technical Skills:
SQL
Excel
Descriptive statistics
Business metrics
Data visualization tools
Analytical Skills:
Problem-solving
Statistical thinking
Attention to detail
Soft Skills:
Communication
Time management
Quick learning ability
Standing out as a new hire
Once you land that first job, here's how to make your mark:
Develop specific competencies that become your signature strengths
Look for process improvements, even in small tasks
Ask thoughtful questions about the broader business context
Take initiative while knowing when to seek guidance
Build relationships across different teams
Consistently meet deadlines
Build credibility by doing what you promise you will do
Learning resources
While the field evolves quickly, these resources can help you build a strong foundation:
Online Platforms:
Coursera.org
YouTube educational channels
Data tools
Excel & Google Sheet
Promote your experience with data analysis tools
Proficiency in data analysis software is in high demand, and showcasing these skills can boost your professional image and unlock new opportunities.
A few ways you can promote your knowledge and expertise in data analysis tools are:
Add your skills to your resume: Tailor your resume to emphasize how you have used analytics to drive business decisions.
Feature specific achievements like “Developed executive dashboards that improved sales forecasting accuracy by 30%” or “Led cross-functional analysis that identified $2M in cost-saving opportunities.”
Highlight proficiency in essential business analytics tools like Tableau, Power BI, Advanced Excel, and SQL.
Include experience with business metrics (KPIs, ROI analysis, market sizing) and emphasize your ability to translate complex data into clear business recommendations.
Create a professional portfolio: Create a portfolio that showcases your ability to solve real business problems through analytics.
Include case studies that walk through your problem-solving process: from defining business questions and selecting appropriate analytical methods to delivering actionable recommendations.
Feature projects that demonstrate commercial acumen, such as customer segmentation analysis, revenue optimization studies, or market trend analyses.
Clearly articulate the business context, methodology, and most importantly, the tangible business outcomes of your work.
Utilize LinkedIn and other social profiles: Develop your professional presence to reflect your business analytics expertise.
On LinkedIn, detail experiences where you have partnered with stakeholders to drive data-informed decisions.
Share insights about business analytics trends and tools, focusing on their practical business applications.
Connect with business analytics professionals and engage with content related to your industry's analytical challenges.
Consider writing about how specific analytics techniques can solve common business problems, demonstrating your ability to bridge the gap between technical analysis and business strategy.
Industries with strong demand
Data analysts are needed across virtually every sector, but these industries show particularly strong demand:
Healthcare/Biotech (patient data analysis)
Financial Services (risk analysis, fraud detection)
E-commerce/Retail (customer behavior analysis)
Technology (product analytics)
Consulting (varied projects)
Manufacturing (supply chain optimization)
Insurance (risk assessment, claims analysis)
The impact of AI on data analytics
Rather than replacing data analysts, AI and generative AI will empower those who learn to use them—leaving behind those who don’t.
Learn how to work with AI tools (ChatGPT, Claude.ai) and integrate them into your workflow.
Familiarize yourself with AI-powered analytics platforms and automation.
Stay up to date with the latest AI trends and how they impact data analysis.
Practice prompt engineering to get better, more accurate insights from AI.
Share your learning and experiment, adapting is the key to staying relevant.
Compensation insights
Entry-level data analysts in the US typically earn between $50,000–$70,000 annually, with experienced analysts earning $80,000 or more.
Data scientists command higher salaries, starting at $70,000-$90,000 and reaching $100,000-$150,000+ with experience.
Freelance opportunities can also be lucrative, with earnings varying based on project scope and expertise.
Global opportunities
The beauty of data analytics is its global reach. Whether you're based in a major tech hub or a developing market, opportunities exist through:
Remote work for global companies
Freelance platforms like Upwork
Local roles with international organizations
Positions with NGOs and financial institutions
Monitoring and evaluation specialist roles
Looking ahead
Despite current market fluctuations, the future of data analytics remains bright.
The field continues to evolve with new tools and technologies,
but the fundamental need for data-driven insights persists.
Success in this field requires
continuous learning, adaptability, and a commitment to solving real-world problems through data.
Remember
the path to becoming a successful data analyst isn’t just about mastering tools and techniques
it’s about developing a mindset of curiosity, problem-solving, and continuous improvement.
Start building your skills today, and
you will be well-positioned for a rewarding career in this dynamic demanding field.
Thanks,
- Ismail Osman