Career Paths & Certification

All Levels 4 hrs 2 Concepts

Your Learning Map

📌 You already know

You can deliver a complete data project.

🎯 You'll learn here

The roles R opens (analyst, data scientist, researcher) and how to build a portfolio and certification path.

🌍 Where it's used

Translating skills into jobs, internships and research positions.

🔗 Unlocks next

Loops back to practice — revisit any chapter to deepen a skill an opening needs.

Career Paths

Concept 1

Roles for R Analysts

R analysts work in finance, pharma, academia, NGOs, and tech. Key roles: Data Analyst, Statistician, Data Scientist, Bioinformatician.

# Key skills by role:
# Data Analyst:    R, SQL, ggplot2, Excel
# Data Scientist:  R + Python, ML, Shiny
# Statistician:    R, SAS, clinical trials
# Bioinformatician:Bioconductor, RNA-seq

R — R career roles LIVE READY

roles <- c("Data Analyst", "Data Scientist", "Research Analyst", "BI Developer")
roles

Output below is verified. Click to run real R in your browser (first run loads ~20 MB once).

Output (verified)

[1] "Data Analyst"     "Data Scientist"   "Research Analyst" "BI Developer"

Solved Examples

Example 1 Apply the concept of Roles for R Analysts to a sample dataset. Show at least two approaches.

# See the code example above and adapt it to your data. # Always check your output with str() and head().

Self-Assessment (2 questions)

Q1. A Data Analyst most typically focuses on:

Analysts turn data into clear summaries, charts and answers for decision-makers.

Q2. Which skill set most distinguishes a Data Scientist from a Data Analyst?

Data scientists add statistical modelling and machine learning on top of analysis skills.

Portfolio Building

Concept 1

Building a Strong Portfolio

Employers want to see real work: GitHub repositories, Kaggle participation, a personal blog or Quarto website.

# Portfolio checklist:
# 1. 5+ public GitHub repos with README
# 2. Kaggle profile with at least 1 bronze medal
# 3. quarto::create_website() for your blog
# 4. LinkedIn: add R, dplyr, ggplot2, Shiny skills
# 5. Write 1 blog post per month explaining a new technique
quarto::create_website(dir='my-portfolio', type='blog')

Solved Examples

Example 1 Apply the concept of Building a Strong Portfolio to a sample dataset. Show at least two approaches.

# See the code example above and adapt it to your data. # Always check your output with str() and head().

Self-Assessment (2 questions)

Q1. The strongest item in a data portfolio is usually:

Employers value demonstrable projects (e.g. on GitHub) over lists of courses.

Q2. A good place to host and showcase your R project code is:

A public GitHub repo lets others see your code, commits and documentation.

Capstone Project