- Teaching and delivery
- Teach the core Python skills students need: syntax, data structures, functions, and working with files.
- Cover the main analytics libraries: NumPy, Pandas, Matplotlib, Seaborn, and often Plotly, SciPy, and scikit-learn.
- Explain data analysis concepts such as data cleaning, exploratory data analysis (EDA), statistics, hypothesis testing, and visualization.
- Run live coding sessions, demos, and hands-on labs rather than just lecturing.
- Introduce SQL, Jupyter Notebooks, and Git, since analysts use them daily.
- Curriculum and content
- Design or update the syllabus, learning outcomes, and weekly lesson plans.
- Prepare slides, notebooks, datasets, exercises, and reference materials.
- Choose real-world datasets (sales, health, finance, agriculture, and so on) so students practice on realistic problems.
- Keep content current as tools and industry practices change.
- Assessment and feedback
- Create quizzes, assignments, and exams that test practical skills.
- Review students’ code and analyses, and give clear, constructive feedback.
- Supervise capstone or portfolio projects, from raw data to final insights and presentation.
- Track progress and spot students who need extra help.
- Student support
- Answer questions during and outside class, via office hours, forums, or chat.
- Help students debug code and build good habits like clean, readable, reproducible work.
- Teach students to communicate findings to non-technical audiences, which is often what separates a good analyst from a great one.
- Offer career guidance, such as portfolio building, CV tips, and interview prep.
- Professional and administrative duties
- Keep attendance, grades, and progress records.
- Set up and maintain the learning environment (Anaconda, Google Colab, or a shared Jupyter server).
- Collaborate with other instructors, program coordinators, and sometimes industry partners.
- Collect student feedback and use it to improve the course.
- Keep learning, since the data field moves fast.
← All positions
Careers · Data and Machine Learning
What you would do
What we are looking for
Data Analytics Lecturer
Data Analytics with Python Instructor Needed.
- Full-time
- Molyko, Buea
- 3 posts
- Closes in 5 days
- Education
- A bachelor’s degree in Computer Science, Statistics, Mathematics, Data Science, Engineering, or a related field is the usual minimum.
- A master’s degree is often preferred for universities and sometimes required for higher-level teaching. Bootcamps and training centers care less about degrees and more about proven skills.
- Technical skills
- Python, strong and practical: data structures, functions, OOP basics, writing clean code.
- Data libraries: Pandas, NumPy, Matplotlib, Seaborn, and ideally Plotly, SciPy, and scikit-learn.
- SQL for querying and joining data.
- Statistics: descriptive stats, probability, hypothesis testing, regression.
- Data visualization and BI tools such as Power BI or Tableau (often a plus).
- Tools: Jupyter or Colab, Git/GitHub, and Excel.
- Basic machine learning knowledge is increasingly expected.
- Experience
- Industry experience: usually 2 to 5 years working with real data, such as analyst, data scientist, or developer roles. Recruiters like instructors who can say “here’s how this works on a real project.”
- Teaching experience: classroom teaching, tutoring, mentoring, or running workshops all count. Employers want proof you can explain things clearly.
- A portfolio: GitHub projects, dashboards, or case studies showing you can do the work you’ll be teaching.
- Teaching skills
- Clear communication, especially explaining abstract ideas simply.
- Ability to design lesson plans, assignments, and assessments.
- Comfort with live coding, labs, and mixed-ability groups.
- Experience with online teaching tools (Zoom, Moodle, Google Classroom) if the role is remote or hybrid.
- Helpful extras
- Certifications such as Google Data Analytics, Microsoft Power BI, IBM Data Science, or a teaching or training certificate.
- Published tutorials, a blog, a YouTube channel, or conference talks.
- Language fluency required for the program (for example English proficiency, sometimes shown through IELTS or TOEFL).
- Mentoring or supervising capstone projects.
Apply
Send your application
About five minutes. You will need your CV as a PDF or Word document, up to 10 MB. Applications close on 7 November 2026.