Data Analytics & Data Science Instructor at Ai Multimedia Academy

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.