Finding Your Path in Data Science, Data Analysis and Business Analysis

The Spaces convened a mentorship webinar on finding a path in data science, data analysis, and business analysis, anchored by Abdullah with support from Shane. Speakers Gifts Ukuwei (data scientist/AI researcher), Taiwo (business and data analyst), Lydie/Laidi (data analyst with a statistics focus), and Matthew Solomon (data analyst) shared practical journeys, tools, and day-to-day impact. Gifts described transitioning from Python and a short stint in web development to data science via structured learning and scholarships, stressing consistency and that earnings are not immediate. She differentiates data analysis (descriptive/diagnostic, reporting) from data science (predictive modeling, often in Python) and works with geospatial data and AI in engineering. Taiwo bridged data and business analysis, centering stakeholder needs, process improvement, and monetization through LinkedIn optimization. Lydie emphasized passion for numbers, SPSS-driven beginnings, and understanding concepts over tools. Matthew highlighted busting misconceptions (certificates vs practice), exploring multiple methods, and using dashboards to drive staffing and performance decisions. Q&A covered math requirements, role taxonomy, progressive learning (Python/R, SQL, BI tools), statistics as a strong base for DA/DS, and AI augmenting rather than replacing roles. Program updates included course outlines, capstones, CV/LinkedIn support, change-of-course logistics, and next sessions on cybersecurity and cloud.

Webinar: Finding Your Path in Data Science, Data Analysis, and Business Analysis

Context and Hosts

• Organizer: Shane opened the session and handed facilitation to Abisola (host and moderator). Early housekeeping mentioned a pending start while more attendees joined.
• Program context: This is the penultimate session of the Tech Rush mentorship webinars. Applications are closed; selection has begun. The final session (next day) will cover cybersecurity, cloud computing, and ethical hacking.

Speakers and Roles

• Gifts Ukuwei — Data Scientist and AI Researcher
• Taiwo (also heard as Taiwu/Tai) — Data Analyst and Business Analyst
• Laidi — Data Analyst
• Matthew Solomon — Data Analyst (tools: Excel, Power BI)

Topic Overview and Purpose

• Clarify the distinct paths and overlaps of data science, data analysis, and business analysis.
• Share career journeys, challenges, the reality of earning in tech, day-to-day work, tools used, and practical advice for beginners.
• Provide program updates: scholarship selection timeline, course outlines, capstone projects, and career enablement (LinkedIn/CV optimization).

Speaker Journeys and Key Insights

Gifts Ukuwei (Data Scientist & AI Researcher)

• Entry into tech: Began in 2022 learning Python out of curiosity. Initially explored web development but found it misaligned; pivoted to data science where Python skills and affinity for numbers fit.
• Structured learning: Secured a 6-month scholarship program in data science; strongly advises beginners to seek structured learning (bootcamps, scholarships).
• Early constraints: Lacked a reliable laptop (Windows 7 desktop-like laptop that only worked with power). Could not install heavier tools (e.g., Anaconda), so used cloud notebooks (Google Colab, Kaggle) and even learned on phone. Balanced learning with academics.
• Success factors: Consistency and clear goals. Emphasizes that tech is not a get-rich-quick scheme; earnings rarely come in 3–6 months. Persistence pays off later.
• Role distinction (DS vs DA):
  • Data Analyst: Performs descriptive and diagnostic analysis; builds reports and dashboards to explain what is happening and why.
  • Data Scientist: Goes further with predictive modeling (typically in Python) to forecast what will happen and deliver advanced solutions.
• Current work: Heavy use of geospatial data (field/site surveys), spatial analysis, and research to bridge AI with engineering across manufacturing/energy/construction—deploying AI to optimize engineering processes.

Taiwo (Data Analyst & Business Analyst)

• Background: Studied economics; started working with data during undergraduate projects using SPSS and helped peers.
• Learning path: Self-learned Excel and Power BI (YouTube), struggled with consistency, paused and resumed; monetized gradually by analyzing peers’ academic data.
• Dual role perspective:
  • Business Analyst (BA): Prioritizes stakeholder needs, bridges business and technical teams, and improves processes/policies/systems to enhance customer/end-user experience.
  • Data Analyst (DA): Works with data to inform decision-making (what happened, why, and how to improve), often producing dashboards and reports.
• Day-to-day (BA): Requirement gathering, defining solutions for business problems (process improvement, policy changes, new systems), coordinating across functions; not fixed to one industry.
• Challenges: Family skepticism (needed to prove the path), financial constraints (self-funded learning). Overcame by delivering value (including to family business) and visible results.
• Monetization strategy: LinkedIn optimization, personal branding, putting herself out there; consistent applications despite rejections; growth mindset. Notes many beginners mistakenly expect immediate earnings.
• Advice: Be open-minded, patient, consistent. Tech success requires sustained effort beyond acquiring tools (Excel, Power BI, SQL).

Laidi (Data Analyst)

• Motivation: Longstanding love of mathematics and statistics from secondary school; enjoyed working with numbers and teaching peers.
• Transition: Needed to move from theory to software-based analysis. Sought SPSS training from a senior colleague (first-class graduate), negotiated installment payments, and hustled—early-morning sessions, carrying a heavy laptop, earning by analyzing classmates’ projects to fund training.
• Lessons: Passion, grit, and resourcefulness matter. Tools are facilitators; understanding concepts and what you want to achieve comes first.
• Advice for beginners: Expect challenges. Understand fundamentals (e.g., what you want to analyze and why). Tools (Excel, SQL, Power BI, SPSS, etc.) make work easier but do not replace core analytical thinking.
• Personal data use: Uses data analysis to track personal spending and make informed decisions—a reminder that practical application starts at small scales.

Matthew Solomon (Data Analyst)

• Background: Studied agricultural economics; previously did consulting/strategy. Friends encouraged a tech skill; identified data analytics as a fit after research and observing a friend in a structured course.
• Learning approach: Registered and started immediately; completed a purported 6-month course in 8 weeks due to high engagement. Reinforced learning by teaching others (explaining new concepts to peers to deepen mastery).
• Misconceptions: Certificates alone do not guarantee competence. Many avoid hands-on practice, portfolio projects, and real problem solving. Advocates exploring multiple methods and “T-shaped” learning: broad understanding plus deep proficiency in one or two tools.
• Day-to-day work:
  • Workforce planning: Analyzes transactions/customer flow to determine staffing levels by location and timing.
  • Performance monitoring: Builds dashboards and reports on product performance across locations; identifies underperformance and collaborates on root cause analysis (staffing, tools, network issues) and remediation.
• Value of data roles: Sees tangible impact when work is implemented and stakeholders provide feedback; data-driven decisions influence operations directly.

Role Distinctions and Pathways

• Data Analyst (DA):
  • Focus: Descriptive/diagnostic analytics, reporting, dashboards, stakeholder communication.
  • Tools: Excel, Power BI, Tableau/Looker Studio, SQL; some scripting may be helpful but not mandatory for entry.
• Data Scientist (DS):
  • Focus: Predictive modeling, advanced statistics, machine learning, possibly deep learning; handles larger, complex datasets.
  • Tools: Python (primary), sometimes R; knowledge of statistical modeling and ML frameworks.
• Business Analyst (BA):
  • Focus: Stakeholder needs, process improvement, system changes, requirements gathering, business documentation (e.g., BRDs), coordinating with data and engineering teams.
  • Tools: Vary; not necessarily heavy analytics tools. Emphasis on communication, process mapping, and change facilitation.
• Supporting roles mentioned:
  • Data Engineer: Structures, pipelines, and provisions data; enables access and reliable delivery.
  • DBA (Database Administrator): Manages databases and access provisioning.
  • Data Governance: Policies, security, privacy, and breach prevention.

Tools and Skills Discussed

• Core analytics: Excel, Power BI, SQL, Tableau/Looker Studio.
• Data science: Python (Anaconda, notebooks), cloud notebooks (Google Colab, Kaggle), statistics, ML.
• BA artifacts: Business requirements, process documentation; vendor coordination for automation.
• Complementary professional skills: LinkedIn profile optimization, CV tailoring, social presence, portfolio projects.

Day-to-Day Work Snapshots

• Data Scientist (Gifts): Geospatial analysis of survey/site data; AI research to optimize engineering processes in manufacturing/energy/construction; bridging AI with traditional engineering workflows.
• Data Analyst (Matthew/Laidi): Operational analytics (staffing, product performance), dashboarding, reporting, stakeholder insights, practical personal finance tracking.
• Business Analyst (Taiwo): Translating stakeholder needs into solutions (process, policy, system changes), coordinating delivery across teams, ensuring improved customer/end-user experience.

Challenges and How They Were Addressed

• Hardware and environment constraints (Gifts): Used cloud environments and mobile devices; persisted despite limited local tooling.
• Balancing academics and learning (Gifts): Consistency and goal orientation.
• Financial and familial skepticism (Taiwo): Self-funded learning, proving ROI via results and helping family business.
• Access to training (Laidi): Negotiated payments, leveraged early project earnings to fund training; persistence in scheduling and effort.

Monetization and Career Growth

• Expectation management: Earnings typically follow sustained learning; tech is not instant wealth.
• Personal branding: LinkedIn optimization, visibility, networking, consistent applications.
• Portfolio and experience: Capstone projects, internships, volunteering; demonstrate impact with project artifacts and dashboards.
• Resilience: Rejections are common; persist with a growth mindset.

Advice for Beginners

• Seek structured learning (scholarships, bootcamps) and a clear roadmap.
• Be consistent, patient, and realistic about timelines to earning.
• Understand foundational concepts; tools enable execution, they don’t replace thinking.
• Practice relentlessly: build dashboards, analyze real data, deliver capstone projects, and do portfolio work.
• Explore broadly, then go deep (T-shaped learning); try multiple methods and tools.
• Develop professional presence (LinkedIn, CV, social proof) and show up—jobs won’t come to you if you stay invisible.

Q&A Highlights

• Math requirements (Louis): The data field is diverse. BA roles can be less stats-heavy, focusing more on stakeholder engagement and process. DA uses descriptive/diagnostic analytics; DS uses more programming and predictive modeling. Many paths exist without deep math, though some quantitative comfort helps.
• Curriculum depth in 3–4 months (Thomas): Yes—dashboards, SQL, and Python are covered. Programs start from basics; by month one some students build dashboards. Typical structure: Weeks 1–8 learning; Week 9 capstone. Commitment is essential.
• Languages beyond Python/R for DS: Start with Python; learning is progressive. Expand skills continuously over time.
• Statisticians transitioning (Mirek): Strong fit for DA and DS. With stats background, choose DS (add programming) or DA (analytics/dashboards) depending on preference.
• Does data science encompass data analysis, and can applicants change courses (Young Cave): Overlaps exist; course outline clarifies specifics. Change-of-course form will be opened briefly; watch community channels for timing.
• Will AI replace data roles (Movagi): AI augments rather than replaces competent professionals. Humans are needed for data collection, governance, prompting, and fixing issues. Use AI (e.g., Microsoft Copilot, ChatGPT) to enhance productivity; being good at your job is the safeguard.

Program Updates and Logistics (Tech Rush)

• Applications closed; selection process has begun.
• Award emails scheduled to roll out October 3–7.
• Detailed course outlines for all tracks to be shared the next day.
• Change-of-course form: Will open briefly; monitor official community announcements (Telegram/WhatsApp) and the host page for updates.
• Program structure: From basics through capstone projects, plus LinkedIn optimization, CV optimization, and guidance on social presence for job search and freelancing.
• Career enablement: Emphasis on portfolio-building, internships/volunteering, and practical projects to demonstrate skills.

Key Takeaways

• Choose your path based on interests: DA (insight and reporting), DS (modeling and programming), BA (stakeholder/process solutions). All are viable and interdependent.
• Structured learning, consistency, and project work matter more than certificates alone.
• Be realistic about earnings timelines; invest in skill-building and visibility.
• Leverage cloud tools if hardware is limited; remove excuses with resourcefulness.
• Use LinkedIn and social presence strategically; jobs come to those who show up with demonstrable value.
• AI is a force multiplier—embrace it to build better solutions, not a reason to avoid the field.

Suggested Actions for Attendees

• Review the course outlines when shared; confirm your track fit.
• If needed, use the change-of-course form promptly when announced.
• Prepare for Weeks 1–8 learning and Week 9 capstone by clearing time and committing to consistency.
• Start/continue LinkedIn optimization: publish project posts, connect with peers, and seek feedback.
• Identify one or two portfolio projects per track to complete within the program timeline.
• Attend the final webinar (cybersecurity, cloud computing, ethical hacking) to broaden perspective and confirm your direction.