Data Analytics & Data Science Instructor (Part-Time) at Ai Multimedia Academy

Data Analytics & Data Science Instructor


Job Description

Are you passionate about data analytics and data science? Do you have the expertise to inspire and educate others on the power of data-driven decision-making?
If so, we want YOU to join our dynamic team as a Part-Time Data Analytics & Data Science Instructor!


Role and Responsibilities

As a Data Analytics & Data Science Instructor, you will play a crucial role in shaping the next generation of data professionals. Your responsibilities will include:

1. Curriculum Development:

  • Designing and developing a comprehensive curriculum for data analytics and data science courses.
  • Staying updated with industry trends and integrating real-world applications into the curriculum.

2. Instruction and Facilitation:

  • Delivering engaging and interactive lectures, workshops, and hands-on exercises.
  • Providing mentorship and guidance to students, fostering a collaborative learning environment.

3. Project Supervision:

  • Overseeing and guiding students in practical projects, ensuring they apply theoretical knowledge to real-world scenarios.
  • Offering constructive feedback to help students refine their analytical and problem-solving skills.

4. Stay Current:

  • Keeping abreast of the latest tools, technologies, and methodologies in data analytics and data science.
  • Actively participating in professional development to enhance your own expertise.

5. Inspire and Motivate:

  • Inspiring students to explore the vast possibilities of data analytics and data science.
  • Motivating learners to think critically, solve problems, and cultivate a passion for continuous learning.

6. Collaboration:

  • Collaborating with the curriculum development team, fellow instructors, and industry professionals to ensure our programs align with industry standards.

7. Online and/or On-Site:

  • Delivering courses through online platforms and/or in-person sessions based on the needs of our diverse student base.

8. Student Support:

  • Providing additional support through office hours, one-on-one sessions, and online forums to ensure student success.

9. Diversity and Inclusion:

  • Creating an inclusive and supportive learning environment that celebrates diversity and fosters equal opportunities for all.



1. Programming Languages:

Python: Widely used for data analysis, machine learning, and statistical modeling.
R: Commonly used for statistical analysis and visualization.

2. Data Manipulation and Analysis:

Pandas: Python library for data manipulation and analysis.
NumPy: Python library for numerical operations on arrays.

3. Data Visualization:

Matplotlib: Python library for creating static, animated, and interactive visualizations.
Seaborn: Built on top of Matplotlib, it provides a high-level interface for drawing attractive statistical graphics.
Plotly: Interactive and web-based visualization library.

4. Statistical Analysis:

RStudio: An integrated development environment (IDE) for R, widely used for statistical analysis.
Jupyter Notebooks: Interactive web-based notebooks supporting various programming languages, commonly used for Python.

5. Machine Learning:

Scikit-learn: A machine learning library for classical algorithms in Python.
TensorFlow and PyTorch: Popular open-source deep learning frameworks.
Keras: High-level neural networks API, often used with TensorFlow.

6. Database and SQL:

SQL: Structured Query Language for managing and querying relational databases.
MySQL, PostgreSQL, SQLite: Popular relational database management systems.
MongoDB: A NoSQL database commonly used for handling unstructured or semi-structured data.

7. Big Data Technologies:

Hadoop: Distributed storage and processing of large data sets.
Spark: A fast, in-memory data processing engine for big data processing.

8. Data Cleaning and Transformation:

OpenRefine: An open-source tool for cleaning and transforming messy data.

9.Version Control:

Git: For tracking changes in source code and collaborative development.

10. Business Intelligence Tools:

Tableau, Power BI, Looker: Tools for creating interactive and shareable dashboards.

11. Cloud Platforms:
AWS, Azure, and Google Cloud: Cloud platforms that offer a range of services for data storage, processing, and analysis.

12. Text and NLP Processing:

NLTK (Natural Language Toolkit), SpaCy: Libraries for natural language processing in Python.

13. Collaboration and Documentation:

JIRA, Confluence: Tools for project management and documentation.
Strong communication skills, both written and verbal.
Previous teaching or mentoring experience is a plus.
Self-motivated and able to work independently.



  • Flexibility work schedule.
  • Competitive compensation is based on the number of trainees per session.
  • Opportunity to make a real impact on the lives of aspiring coders.


Application Closing Date
31st July, 2024.


How to Apply

Interested and qualified candidates should send their resume, a brief portfolio, and a cover letter outlining their experience and teaching approach to: using the Job Title as the subject of the mail.


Note: Do not attach the proof of your work, only send the link, similarly do not send the link to your CV / Resume, your resume or CV must be sent as an attachment, and your cover letter must be typed in the email body.






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