Getting Started in Analytics - A Roadmap

Analytics Roadmap - Foundational Knowledge, Data Wrangling & Cleaning, Data Exploration, AI/ML, Business Analytics and Storytelling

When you’re starting with analytics, it can be overwhelming to identify the most crucial skills and tools you’ll need. There are many possible analytics routes, and you don’t need to be an expert in every tool or area. Whether you should learn Python or Power BI will depend on the type of work you want to do the most. Once you’ve learned one language or visualization tool, learning another becomes much easier.

The question is: Why do you want to get into analytics? Understanding your motivations will guide you toward the right tools and career path.

Data Visualization

• If your passion lies in telling stories with data, sharing insights, and making information accessible to others, data visualization might be your calling.

• You enjoy working closely with business teams, presenting clear and compelling visuals that drive decision-making and organizational change.

• Your strength is communicating complex ideas simply and aligning data with business goals to create a meaningful impact.

• You may find satisfaction in empowering stakeholders by giving them tools and dashboards to explore data independently.

• Typical tools include Power BI, Tableau, or Excel.

Data Science/AI

• If you are fascinated by solving complex problems, creating innovative solutions, and exploring patterns hidden in data, then data science or AI might be the right path.

• You enjoy automation, predictive analytics, and building algorithms to tackle unique challenges.

• Working with raw, unstructured data, developing models, and enhancing efficiency through intelligent systems excites you.

• Your strength lies in creativity and critical thinking, where you tackle big questions like, "How can we predict customer churn?" or "What factors drive business performance?"

• Typical tools include Python, R, SQL, and machine learning frameworks like TensorFlow and Scikit-learn.

Considerations for Both Paths

• Interpersonal Dynamics: Does collaboration with non-technical stakeholders energize you (lean toward data visualization) or prefer working on independent, highly technical projects (lean toward data science/AI)?

• Impact Timeline: Do you want to see the immediate impact of your work (data visualization), or are you more motivated by long-term, transformative projects (data science/AI)?

• Learning Style: Do you prefer learning tools with a more visual, intuitive interface (data visualization), or are you comfortable diving into programming and advanced mathematics (data science/AI)?

Regardless of your direction, you’ll benefit from foundational knowledge in basic stats, spreadsheet tools (Excel and Google Sheets), and SQL. The rest of the skills and tools depend on your preferences; many analytics professionals bridge both worlds.

Journey Based on the Path You Choose

Your journey along this roadmap will differ depending on whether you choose Data Visualization or Data Science/AI.

For Data Visualization (Path to Business Analytics & Storytelling):

The emphasis is on understanding how to communicate insights effectively. You'll begin with Foundational Knowledge of data concepts and tools like Power BI or Tableau, then move to Data Wrangling & Cleaning, where you'll learn how to prepare datasets for visualization. In Data Exploration, you'll focus on analyzing patterns, trends, and relationships, often using built-in tools within visualization platforms. While AI/ML may be lightly touched upon (e.g., integrating predictive analytics visualizations), your goal is to master Business Analytics & Storytelling, creating compelling narratives that drive decisions.

For Data Science/AI (Path to AI/ML and Beyond):

You'll start similarly with Foundational Knowledge but delve deeper into programming languages like Python, or R. Data Wrangling & Cleaning will be more technical, involving libraries like Pandas or NumPy to handle raw and unstructured data. During Data Exploration, you'll write code to analyze data and uncover meaningful patterns, setting the stage for AI/ML, where you'll learn to build and deploy machine learning models. While storytelling remains valuable, your journey will focus on optimizing algorithms, automating workflows, and developing predictive capabilities.

Each path has overlapping skills but prioritizes different end goals—collaboration and communication for visualization versus problem-solving and technical depth for data science. You can always pivot or combine the two as your career evolves.