Ai Multimedia Academy was established to provide technical training in the area of multimedia, cinematography & digital skills.
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:
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.
Instruction and Facilitation:
- Delivering engaging and interactive lectures, workshops, and hands-on exercises.
- Providing mentorship and guidance to students, fostering a collaborative learning environment.
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.
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.
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.
Collaboration:
- Collaborating with the curriculum development team, fellow instructors, and industry professionals to ensure our programs align with industry standards.
Online and/or On-Site:
- Delivering courses through online platforms and/or in-person sessions based on the needs of our diverse student base.
Student Support:
- Providing additional support through office hours, one-on-one sessions, and online forums to ensure student success.
Diversity and Inclusion:
- Creating an inclusive and supportive learning environment that celebrates diversity and fosters equal opportunities for all.
Requirements
Programming Languages:
- Python: Widely used for data analysis, machine learning, and statistical modeling.
- R: Commonly used for statistical analysis and visualization.
Data Manipulation and Analysis:
- Pandas: Python library for data manipulation and analysis.
- NumPy: Python library for numerical operations on arrays.
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.
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.
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.
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.
Big Data Technologies:
- Hadoop: Distributed storage and processing of large data sets.
- Spark: A fast, in-memory data processing engine for big data processing.
Data Cleaning and Transformation:
- OpenRefine: An open-source tool for cleaning and transforming messy data.
Version Control:
- Git: For tracking changes in source code and collaborative development.
Business Intelligence Tools:
- Tableau, Power BI, Looker: Tools for creating interactive and shareable dashboards.
- Cloud Platforms:
- AWS, Azure, and Google Cloud: Cloud platforms that offer a range of services for data storage, processing, and analysis.
Text and NLP Processing:
- NLTK (Natural Language Toolkit), SpaCy: Libraries for natural language processing in Python.
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.
Benefits
- 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.
Method of Application
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