Data Science Lecturer

An ONLINE Institution is seeking a Data Science Lecturer who would lecture based on established curriculum and provide mentorship to students interested in our Data Analysis training. The role involves delivering lectures, creating course materials, and guiding learners. This position requires strong background and comprehensive knowledge of collecting, processing, and performing statistical analyses of data uncovering trends, patterns, and insights from large datasets. The Lecturer will work in collaboration with various departments to identify business needs, translating them into data-driven insights, and presenting actionable.

What you bring with you
You…
 Are a digital native
 Are analytical and have a very high level of comprehension
 Can quickly familiarize yourself with complex content and feel comfortable in a dynamic environment
 You have already worked in an industrial environment
 You have already worked with tools and have gained initial experience

Responsibilities
 Course Design & Delivery: Develop and deliver engaging lectures on topics such as data collection, data cleaning, statistical analysis, data
visualization, and reporting.
 Software Training: Provide hands-on training with data analysis tools and software such as Excel, SQL, Python, R, and Tableau.
 Curriculum Development: Design and update the curriculum to reflect the latest industry trends and analytical methodologies.
 Student Mentorship: Guide and mentor students through projects, practical assignments, and case studies, ensuring they can apply
theoretical concepts in real-world scenarios.
 Assessment & Feedback: Evaluate students’ performance through exams, quizzes, and project work, o􀆯ering constructive feedback to help
them improve their skills.
 Industry Trends: Stay updated with current data trends, tools, and best practices to keep the teaching material relevant and insightful.

Eligibility
 Master’s degree in a related field (e.g., Data Science, Statistics, Computer
Science, Mathematics, or Economics).
 Certifications or specialized courses in data analysis (preferred).
 At least 6 years of experience in data analysis or a related role.
 Proficiency in data analysis tools (e.g., Excel, SQL, Python, R).
 Experience with data visualization software (e.g., Tableau, Power BI).
 Knowledge of database management and querying (SQL, NoSQL).
 Experience with analyzing data from di􀆯erent sectors like Banking, Real
Estate, Fintech etc.
 Proficiency in data analysis tools and statistical software.
 Strong foundation in statistical concepts, data modeling, and data-driven
decision-making.
 Effective communication and presentation skills to explain complex data
topics.
 Prior industry experience in data analysis is often preferred.
 Ability to simplify complex ideas and foster a positive learning
environment.

Areas of Focus
Introduction
 Python Installation and environment setup (using VSCode) Introduction to Python programming (syntax, data types, control flow, data
structures)
 NumPy and Pandas for data
 Descriptive and inferential statistics (show some concepts in Python)
 Probability and distributions
 Hypothesis testing: Z test, T-test, etc.
 Matplotlib and Seaborn for data visualization
 Exploratory data analysis (EDA)
 Setup & Introduction to SQL
 Data querying and manipulation using SQL
 Types of Jains
 Query optimization.
 Introduction to version control.
 Introduction to Git & Github

Intermediate
 Supervised vs unsupervised learning
 Model evaluation metrics.
 Model selection and hyperparameter tuning.
 Linear and logistic regression
 Decision trees, random forests
 Support vector machines & Naive Bayes Ensemble methods (bagging,
boosting)
 Clustering (K-means, hierarchical)
 Dimensionality reduction (PCA, LDA)
 Neural networks: architecture, activation functions, backpropagation
 TensorFlow/Keras, basics

Advanced
 Introduction to Convolutional Neural Networks (CNNs)
 Image processing techniques
 Object detection, image segmentation (A small demo with Keras)
 Image classification with CNNs
 Text preprocessing, tokenization, stemming, lemmatization
 Sentiment analysis, text classification Natural language processing with RNNs/LSTMS
 Word embeddings (Word2Vec, Gloe)
 Language models (BERT, GPT)
 Introduction to generative models Text generation, image generation
 Applications of generative Al
 Generative Adversarial Networks (GANS) Time series data characteristics
 Time series forecasting methods (ARIMA, AutoARIMA HoltWinters, Prophet)
 Time series with deep learning (LSTM, GRU)

What you can look forward to
We…
 Believe that our first employees form the cornerstone of our long-termsuccess.
 Offer lucrative compensation.
 Offer insights into the daily work in a always meet us as equals
 Offer attractive compensation models (freedom in work, flexible working hours)
 Are a super motivated team that lives an open feedback culture

How to Apply
Interested Applicants should submit their resume, cover letter, and any relevant
portfolio or case studies to Human Resource Department Email:
hr@etesot.co.ke