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Found 8 courses in category: Data Science & Analytics

Marketing Analytics
📊 Data Science & Analytics

Marketing Analytics

Marketing Analytics is a comprehensive, skills-first course that teaches how to collect, process, analyze, and act on marketing data to drive measurable business outcomes. Students will learn fundamentals of data instrumentation, data engineering, exploratory analysis, digital analytics, customer segmentation, campaign measurement, attribution methods, predictive modeling, experimentation, marketing mix modeling, personalization and optimization, dashboarding, governance, privacy, and how to operationalize analytics at scale. The course balances theory with practical workflows, tools, and templates used by data-driven marketing teams and prepares learners to design analytics frameworks, run experiments, build predictive models, and communicate insights to stakeholders.

48Students
Power BI
📊 Data Science & Analytics

Power BI

This comprehensive Power BI course teaches end-to-end skills for data professionals, analysts, and business users to extract, transform, model, visualize, and share data-driven insights using the Microsoft Power BI platform. Starting with foundational concepts and the Power BI ecosystem, the course progresses through data connectivity, Power Query transformations, robust data modeling, and beginner-to-advanced DAX for calculations and time intelligence. You will learn visualization best practices, interactive report design, and how to publish, secure, and govern content in the Power BI Service. Advanced modules cover performance optimization, automation with APIs and Power Automate, integrating machine learning and Python/R, embedding Power BI in applications, and practical deployment strategies. The course culminates with real-world projects and a capstone that synthesize technical and business skills to deliver production-ready reports and dashboards. By course end, learners will be equipped to build scalable, performant, and secure Power BI solutions that support enterprise analytics and decision-making.

57Students
Data Science: R Basics
📊 Data Science & Analytics

Data Science: R Basics

A practical, beginner-friendly path to using R for data science. You will learn core R syntax, data structures, importing and cleaning data, the tidyverse (dplyr, tidyr), visualization with ggplot2, control flow and functions, reproducible workflows, and a gentle introduction to statistics and modeling. Each chapter builds on the last with concise examples and best practices for writing clear, efficient, and reproducible R code.

194Students
Python for Data Science, AI & Development
📊 Data Science & Analytics

Python for Data Science, AI & Development

Build real-world expertise in Python across data science, AI, and modern software development. This hands-on course moves from core Python and idiomatic data manipulation through numerical computing, pandas analytics, cleaning and feature engineering, and effective visualization. You will strengthen statistical intuition, practice end-to-end machine learning with scikit-learn, and step into deep learning with PyTorch, including CNNs, RNNs, and transformers. Beyond modeling, you will learn to work with files, web APIs, and databases; manage environments and dependencies; write tested, production-ready code; and package, version, and deploy services with FastAPI and containers. Each module emphasizes practical workflows, performance-aware techniques, and reproducibility—using notebooks and scripts, real datasets, and best practices such as pipelines, cross-validation, and model monitoring. By the end, you can acquire and prepare data, build and explain models, automate experiments, and ship reliable AI-powered applications.

120Students
Data Science for Business Decision Making
📊 Data Science & Analytics

Data Science for Business Decision Making

This course equips business leaders, analysts, and product owners to apply data science to real decisions—from framing problems and building reliable pipelines to experimentation, modeling, forecasting, and operationalizing insights. Learn to translate strategy into metrics, design trustworthy analyses, and deploy models that drive measurable impact. Emphasis is on practical tools, governance, ethics, and storytelling to influence stakeholders. Hands-on cases trace the end-to-end lifecycle, connecting methods to ROI and change management. By the end, you will be ready to lead data-informed initiatives with confidence.

857Students
🤖
🤖 AI & Machine Learning

Full Stack AI and Data Science Professional

Become a full‑stack AI and data professional by mastering the complete lifecycle: problem framing, data engineering, analysis, machine learning, deep learning, large language models, and production MLOps. You’ll build practical skills in Python, SQL, math for ML, EDA, supervised and unsupervised learning, neural networks, NLP/LLMs, and scalable cloud pipelines. The course emphasizes reproducibility, governance, security, and cost‑aware deployment, culminating in end‑to‑end systems that deliver real business value.

1230Students
Data Science : Begineer to Advance
📊 Data Science & Analytics

Data Science : Begineer to Advance

This end-to-end program takes you from absolute beginner to confident practitioner across the full data science lifecycle. You’ll start by learning core concepts, workflows, and tooling, then master Python, SQL, and data ingestion. You’ll progress through data wrangling, cleaning, and exploratory analysis, and build a solid foundation in probability and statistics to make sound, testable inferences. From there, you’ll learn essential machine learning theory and implement supervised and unsupervised models with professional-grade validation, tuning, and interpretability. You’ll deepen your expertise with feature engineering, pipelines, time series forecasting, and natural language processing. The course culminates in modern deep learning fundamentals and a practical MLOps toolkit for deploying, monitoring, and maintaining models in production. Throughout, you’ll apply best practices in reproducibility, ethics, and communication, preparing you to deliver reliable, value-driven data products and to build a compelling portfolio.

979Students
📊
📊 Data Science & Analytics

Microsoft Certified: Fabric Analytics Engineer Associate

This comprehensive course prepares learners for the Microsoft Certified: Fabric Analytics Engineer Associate certification. The course covers essential concepts, tools, and skills required to design and implement analytics solutions using Microsoft Fabric. Participants will gain hands-on experience with data integration, data warehousing, data analysis, and visualization, ensuring they are well-equipped to leverage Microsoft Fabric in real-world scenarios.

2
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1016
Students