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Our Courses
Relational Database Support for Data Warehouses
Relational Database Support for Data Warehouses is the third course in the Data Warehousing for Business Intelligence specialization. In this course, you'll use analytical elements of SQL for answering business intelligence questions. You'll learn features of relational database management systems for managing summary data commonly used in business intelligence reporting. Because of the importance and difficulty of managing implementations of data warehouses, we'll also delve into storage architectures, scalable parallel processing, data governance, and big data impacts.
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Course by
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Self Paced
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71 hours
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English
Monthly Subscription
Included in- Starter @ AED 99 + VAT
- Professional @ AED 149 + VAT
- AED 239.99 + VAT
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Econometrics: Methods and Applications
Welcome!
Do you wish to know how to analyze and solve business and economic questions with data analysis tools? Then Econometrics by Erasmus University Rotterdam is the right course for you, as you learn how to translate data into models to make forecasts and to support decision making.
* What do I learn?
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Course by
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Self Paced
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66 hours
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English
Math behind Moneyball
Learn how probability, math, and statistics can be used to help baseball, football and basketball teams improve, player and lineup selection as well as in game strategy.
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Course by
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Self Paced
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65 hours
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English
Algorithms, Part II
This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms.
All the features of this course are available for free. It does not offer a certificate upon completion.
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Course by
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Self Paced
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63 hours
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English
Geospatial Analysis Project
In this project-based course, you will design and execute a complete GIS-based analysis – from identifying a concept, question or issue you wish to develop, all the way to final data products and maps that you can add to your portfolio. Your completed project will demonstrate your mastery of the content in the GIS Specialization and is broken up into four phases:
Milestone 1: Project Proposal - Conceptualize and design your project in the abstract, and write a short proposal that includes the project description, expected data needs, timeline, and how you expect to complete it.
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Course by
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Self Paced
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62 hours
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English
Introduction to Deep Learning
Deep Learning is the go-to technique for many applications, from natural language processing to biomedical. Deep learning can handle many different types of data such as images, texts, voice/sound, graphs and so on. This course will cover the basics of DL including how to build and train multilayer perceptron, convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders (AE) and generative adversarial networks (GANs). The course includes several hands-on projects, including cancer detection with CNNs, RNNs on disaster tweets, and generating dog images with GANs.
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Course by
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Self Paced
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60 hours
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English
R Programming
In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code.
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Course by
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Self Paced
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57 hours
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English
Monthly Subscription
Included in- Starter @ AED 99 + VAT
- Professional @ AED 149 + VAT
- AED 170.99 + VAT
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The Structured Query Language (SQL)
In this course you will learn all about the Structured Query Language ("SQL".) We will review the origins of the language and its conceptual foundations. But primarily, we will focus on learning all the standard SQL commands, their syntax, and how to use these commands to conduct analysis of the data within a relational database. Our scope includes not only the SELECT statement for retrieving data and creating analytical reports, but also includes the DDL ("Data Definition Language") and DML ("Data Manipulation Language") commands necessary to create and maintain database objects.
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Course by
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Self Paced
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55 hours
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English
Monthly Subscription
Included in- Starter @ AED 99 + VAT
- Professional @ AED 149 + VAT
- AED 344.99 + VAT
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Statistical Inference
Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance.
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Course by
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Self Paced
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54 hours
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English
Monthly Subscription
Included in- Starter @ AED 99 + VAT
- Professional @ AED 149 + VAT
- AED 170.99 + VAT
Buy Now
Algorithms, Part I
This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms.
All the features of this course are available for free. It does not offer a certificate upon completion.
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Course by
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Self Paced
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54 hours
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English
Regression Models
Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated.
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Course by
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Self Paced
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54 hours
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English
Monthly Subscription
Included in- Starter @ AED 99 + VAT
- Professional @ AED 149 + VAT
- AED 170.99 + VAT
Buy Now
Probabilistic Deep Learning with TensorFlow 2
Welcome to this course on Probabilistic Deep Learning with TensorFlow!\n\nThis course builds on the foundational concepts and skills for TensorFlow taught in the first two courses in this specialisation, and focuses on the probabilistic approach to deep learning. This is an increasingly important area of deep learning that aims to quantify the noise and uncertainty that is often present in real world datasets.
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Course by
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Self Paced
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53 hours
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English
Monthly Subscription
Included in- Starter @ AED 99 + VAT
- Professional @ AED 149 + VAT
- AED 170.99 + VAT
Buy Now
AI Materials
Learn about the materials that have advanced the performance of artificial intelligence, and the machine learning models that could help accelerate the design and development of novel materials.
This course defines artificial intelligence (AI) as a machine to which some or all of the functions of the human brain have been delegated. It highlights the need, and explains in an easy-to-understand way how machine learning from artificial intelligence can dramatically accelerate the development of new materials.
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Course by
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Self Paced
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50 hours
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English
Foundations of Sports Analytics: Data, Representation, and Models in Sports
This course provides an introduction to using Python to analyze team performance in sports. Learners will discover a variety of techniques that can be used to represent sports data and how to extract narratives based on these analytical techniques. The main focus of the introduction will be on the use of regression analysis to analyze team and player performance data, using examples drawn from the National Football League (NFL), the National Basketball Association (NBA), the National Hockey League (NHL), the English Premier LEague (EPL, soccer) and the Indian Premier League (IPL, cricket).
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Course by
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Self Paced
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49 hours
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English
Machine Learning Modeling Pipelines in Production
In the third course of Machine Learning Engineering for Production Specialization, you will build models for different serving environments; implement tools and techniques to effectively manage your modeling resources and best serve offline and online inference requests; and use analytics tools and performance metrics to address model fairness, explainability issues, and mitigate bottlenecks.
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Course by
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Self Paced
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48 hours
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English
Decision Making and Reinforcement Learning
This course is an introduction to sequential decision making and reinforcement learning. We start with a discussion of utility theory to learn how preferences can be represented and modeled for decision making. We first model simple decision problems as multi-armed bandit problems in and discuss several approaches to evaluate feedback. We will then model decision problems as finite Markov decision processes (MDPs), and discuss their solutions via dynamic programming algorithms.
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Course by
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Self Paced
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47 hours
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English
Design and Build a Data Warehouse for Business Intelligence Implementation
The capstone course, Design and Build a Data Warehouse for Business Intelligence Implementation, features a real-world case study that integrates your learning across all courses in the specialization. In response to business requirements presented in a case study, you’ll design and build a small data warehouse, create data integration workflows to refresh the warehouse, write SQL statements to support analytical and summary query requirements, and use the MicroStrategy business intelligence platform to create dashboards and visualizations.
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Course by
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Self Paced
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45 hours
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English
Statistical Thinking for Industrial Problem Solving, presented by JMP
Statistical Thinking for Industrial Problem Solving is an applied statistics course for scientists and engineers offered by JMP, a division of SAS. By completing this course, students will understand the importance of statistical thinking, and will be able to use data and basic statistical methods to solve many real-world problems. Students completing this course will be able to:
•\tExplain the importance of statistical thinking in solving problems
•\tDescribe the importance of data, and the steps needed to compile and prepare data for analysis
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Course by
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Self Paced
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44 hours
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English
Regression Analysis
The "Regression Analysis" course equips students with the fundamental concepts of one of the most important supervised learning methods, regression. Participants will explore various regression techniques and learn how to evaluate them effectively. Additionally, students will gain expertise in advanced topics, including polynomial regression, regularization techniques (Ridge, Lasso, and Elastic Net), cross-validation, and ensemble methods (bagging, boosting, and stacking).
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Course by
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Self Paced
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40 hours
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English
Introduction to Machine Learning: Supervised Learning
In this course, you’ll be learning various supervised ML algorithms and prediction tasks applied to different data. You’ll learn when to use which model and why, and how to improve the model performances. We will cover models such as linear and logistic regression, KNN, Decision trees and ensembling methods such as Random Forest and Boosting, kernel methods such as SVM.
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Course by
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Self Paced
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40 hours
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English
Robotic Path Planning and Task Execution
This course, which is the last and final course in the Introduction to Robotics with Webots specialization, will teach you basic approaches for planning robot trajectories and sequence their task execution. In "Robotic Path Planning and Task Execution", you will develop standard algorithms such as Breadth-First Search, Dijkstra's, A* and Rapidly Exploring Random Trees through guided exercises. You will implement Behavior Trees for task sequencing and experiment with a mobile manipulation robot "Tiago Steel".
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Course by
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Self Paced
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39 hours
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English
Classification Analysis
The "Classification Analysis" course provides you with a comprehensive understanding of one of the fundamental supervised learning methods, classification. You will explore various classifiers, including KNN, decision tree, support vector machine, naive bayes, and logistic regression, and learn how to evaluate their performance. Through tutorials and engaging case studies, you will gain hands-on experience and practice in applying classification techniques to real-world data analysis tasks.
By the end of this course, you will be able to:
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Course by
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Self Paced
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38 hours
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English
Machine Learning: Concepts and Applications
This course gives you a comprehensive introduction to both the theory and practice of machine learning. You will learn to use Python along with industry-standard libraries and tools, including Pandas, Scikit-learn, and Tensorflow, to ingest, explore, and prepare data for modeling and then train and evaluate models using a wide variety of techniques. Those techniques include linear regression with ordinary least squares, logistic regression, support vector machines, decision trees and ensembles, clustering, principal component analysis, hidden Markov models, and deep learning.
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Course by
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Self Paced
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38 hours
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English
Dynamic Programming, Greedy Algorithms
This course covers basic algorithm design techniques such as divide and conquer, dynamic programming, and greedy algorithms. It concludes with a brief introduction to intractability (NP-completeness) and using linear/integer programming solvers for solving optimization problems. We will also cover some advanced topics in data structures.
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Course by
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Self Paced
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38 hours
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English
Unsupervised Algorithms in Machine Learning
One of the most useful areas in machine learning is discovering hidden patterns from unlabeled data. Add the fundamentals of this in-demand skill to your Data Science toolkit. In this course, we will learn selected unsupervised learning methods for dimensionality reduction, clustering, and learning latent features. We will also focus on real-world applications such as recommender systems with hands-on examples of product recommendation algorithms.
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Course by
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Self Paced
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38 hours
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English