Explore All - Rolai

Crash Course

Fundamentals of Data Visualization – Part I

Get started with this applied course focusing on the building blocks of analytics.

Difficulty: Beginner
Approx. 15 hours to complete

Automotive

Fleet Vehicle Fuel Consumption Analysis using Feature Engineering

This case study utilizes the fundamental concepts of statistical tests and visualizations to predict student academic performance, and uses those insi

Difficulty: Advanced
Approx. 3 hours to complete

Healthcare

Categorizing Medications using Support Vector Machine (SVM) Classifiers

The case study focuses on the most popular technique of Supervised Machine Learning algorithm, SVM its basics of Support Vector Machines (SVM) and how

Difficulty: Advanced
Approx. 3 hours to complete

Healthcare

Predicting Heart Disease using Logistic Regression

This case shows how binary classification models can efficiently predict if a patient is likely to suffer from a heart disease or not based on the inp

Difficulty: Advanced
Approx. 3 hours to complete

Automotive

Predicting Car Prices using Explainable AI (XAI)

This case study utilizes the fundamental concepts of statistical tests and visualizations to predict student academic performance, and uses those insi

Difficulty: Advanced
Approx. 3 hours to complete

Healthcare

Predicting Water Drinkability using Naive Bayes Classifiers

This case familiarizes the learner with how to use the concept of a Naïve Bayes classifier to create a prediction model, based on water quality param

Difficulty: Advanced
Approx. 3 hours to complete

Automotive

Data Pre-processing on Used Car Data

This case study utilizes the fundamental concepts of statistical tests and visualizations to predict student academic performance, and uses those insi

Difficulty: Advanced
Approx. 3 hours to complete

Automotive

Predict Used Car Prices using Linear Regression

This case study utilizes the fundamental concepts of statistical tests and visualizations to predict student academic performance, and uses those insi

Difficulty: Advanced
Approx. 3 hours to complete

Crash Course

Fundamentals of Data Visualization – Part I

Get started with this applied course focusing on the building blocks of analytics.

Difficulty: Beginner
Approx. 15 hours to complete

Crash Course

Time Series Forecasting

This course introduces the concepts of Time Series Forecasting and how to prepare data for the concepts to be applied on real-world business scenarios

Difficulty: Advanced
Approx. 4 hours to complete

Crash Course

Machine Learning – Regularization in Linear Models

This course explains different regularization techniques in linear machine learning algorithms and how those could be used to treat the problem of ove

Difficulty: Intermediate
Approx. 3 hours to complete

Crash Course

Introduction to Explainable AI (XAI) using SHAP

This course introduces the concept of SHAP methodology, an XAI technique, which can be applied to understand the results an ML model and how they can

Difficulty: Advanced
Approx. 5 hours to complete

Crash Course

Introduction to Explainable AI (XAI) for Text Data using LIME & SHAP

This course explains the need of Explainable AI (XAI) for text data and introduces the concepts of LIME & SHAP, which are the techniques of XAI.

Difficulty: Advanced
Approx. 5 hours to complete

Crash Course

Introduction to Explainable AI (XAI) using LIME

This course introduces the concept of LIME methodology, an XAI technique, which can be applied to understand the results an ML model and how they can

Difficulty: Advanced
Approx. 5 hours to complete

Crash Course

Introduction to Natural Language Processing (NLP)

This course introduces the concepts of Natural Language Processing, which creates systems such as speech recognition, machine translation, spam detect

Difficulty: Advanced
Approx. 5 hours to complete

Crash Course

Introduction to AdaBoost

This course introduces the concept and implementation of Adaptive Boosting (AdaBoost) for both Classification and Regression tasks.

Difficulty: Advanced
Approx. 5 hours to complete

Crash Course

Introduction to CatBoost

This course introduces the concept of Catboost algorithm, that can easily integrate with different deep learning frameworks and can work on diverse da

Difficulty: Advanced
Approx. 5 hours to complete

Crash Course

Introduction to XGBoost

This course introduces the concept of Extreme Gradient Boosting Classifier that clearly lays out improvements of the algorithm over other Boosting met

Difficulty: Advanced
Approx. 5 hours to complete

Crash Course

Introduction to Gradient Boosting

This course introduces the concepts of Gradient Boosting Classifier and how it can be applied to achieve better accuracy & predictions for the problem

Difficulty: Advanced
Approx. 5 hours to complete

Crash Course

Model Evaluation Techniques – Classification Models

This course introduces the different performance measures to evaluate the performance of Classification models.

Difficulty: Beginner
Approx. 15 hours to complete

Automotive

Fleet Vehicle Fuel Consumption Analysis using Feature Engineering

This case study utilizes the fundamental concepts of statistical tests and visualizations to predict student academic performance, and uses those insi

Difficulty: Advanced
Approx. 3 hours to complete

Healthcare

Categorizing Medications using Support Vector Machine (SVM) Classifiers

The case study focuses on the most popular technique of Supervised Machine Learning algorithm, SVM its basics of Support Vector Machines (SVM) and how

Difficulty: Advanced
Approx. 3 hours to complete

Healthcare

Predicting Water Drinkability using Naive Bayes Classifiers

This case familiarizes the learner with how to use the concept of a Naïve Bayes classifier to create a prediction model, based on water quality param

Difficulty: Advanced
Approx. 3 hours to complete

Healthcare

Predicting Heart Disease using Logistic Regression

This case shows how binary classification models can efficiently predict if a patient is likely to suffer from a heart disease or not based on the inp

Difficulty: Advanced
Approx. 3 hours to complete

Automotive

Predict Used Car Prices using Linear Regression

This case study utilizes the fundamental concepts of statistical tests and visualizations to predict student academic performance, and uses those insi

Difficulty: Advanced
Approx. 3 hours to complete

Automotive

Data Pre-processing on Used Car Data

This case study utilizes the fundamental concepts of statistical tests and visualizations to predict student academic performance, and uses those insi

Difficulty: Advanced
Approx. 3 hours to complete

Automotive

Predicting Car Prices using Explainable AI (XAI)

This case study utilizes the fundamental concepts of statistical tests and visualizations to predict student academic performance, and uses those insi

Difficulty: Advanced
Approx. 3 hours to complete

Automotive

Sentiment Analysis on Automobile Reviews

This case study utilizes the fundamental concepts of statistical tests and visualizations to predict student academic performance, and uses those insi

Difficulty: Advanced
Approx. 3 hours to complete

Retail & Consumer Goods

Application of Text Classification on Clothing Reviews

This case study utilizes the fundamental concepts of statistical tests and visualizations to predict student academic performance, and uses those insi

Difficulty: Advanced
Approx. 3 hours to complete

Media & Entertainment

Classifying ‘Spam’ Text using Exploratory Data Analysis

This case study utilizes the fundamental concepts of statistical tests and visualizations to predict student academic performance, and uses those insi

Difficulty: Advanced
Approx. 3 hours to complete

Banking & Financial Services

Predicting Customer Churn – Artificial Neural Networks

This case study utilizes the fundamental concepts of statistical tests and visualizations to predict student academic performance, and uses those insi

Difficulty: Advanced
Approx. 3 hours to complete

Banking & Financial Services

Predicting Credit Card Spend – Artificial Neural Networks

This case study utilizes the fundamental concepts of statistical tests and visualizations to predict student academic performance, and uses those insi

Difficulty: Advanced
Approx. 3 hours to complete

Industry Track

Insurance Analytics

This learning track introduces the concepts, techniques, and applications of Data Treatment, Data Visualizations, Feature Engineering, Descriptive and

Difficulty: Intermediate
Duration: Approximately 3 months

Industry Track

Healthcare Analytics

This learning track introduces the concepts, techniques, and applications of Data Treatment, Data Visualizations, Feature Engineering, Descriptive and

Difficulty: Intermediate
Duration: Approximately 3 months

Industry Track

Telecommunications Analytics

This learning track introduces the concepts, techniques, and applications of Data Treatment, Data Visualizations, Feature Engineering, Descriptive and

Difficulty: Intermediate
Duration: Approximately 3 months

Industry Track

Manufacturing Analytics

This learning track introduces the concepts, techniques, and applications of Data Treatment, Data Visualizations, Feature Engineering, Descriptive and

Difficulty: Intermediate
Duration: Approximately 3 months

Industry Track

Retail and Consumer Goods Analytics

This learning track introduces the concepts, techniques, and applications of Data Treatment, Data Visualizations, Feature Engineering, Descriptive and

Difficulty: Intermediate
Duration: Approximately 3 months

Industry Track

Banking and Financial Services Analytics

This learning track introduces the concepts, techniques, and applications of Data Treatment, Data Visualizations, Feature Engineering, Descriptive and

Difficulty: Intermediate
Duration: Approximately 3 months

Industry Track

Automotive Industry Analytics

This learning track introduces the concepts, techniques, and applications of Data Treatment, Data Visualizations, Feature Engineering, Descriptive and

Difficulty: Intermediate
Duration: Approximately 3 months

Skill Track

Python Programming for Data Science

This is a placeholder description for this Learning Track that will be displayed on the front facing Track Card.

Difficulty: Intermediate
Duration: Approximately 3-4 Weeks

Skill Track

Statistics for Data Science

This is a placeholder description for this Learning Track that will be displayed on the front facing Track Card.

Difficulty: Intermediate
Duration: Approximately 3 months

Skill Track

Data Understanding

This is a placeholder description for this Learning Track that will be displayed on the front facing Track Card.

Difficulty: Beginner
Duration: Approximately 3-4 Weeks

Skill Track

Data Treatment and Feature Engineering

This is a placeholder description for this Learning Track that will be displayed on the front facing Track Card.

Difficulty: Beginner
Duration: Approximately 3-4 Weeks

Skill Track

Python Programming

This is a placeholder description for this Learning Track that will be displayed on the front facing Track Card.

Difficulty: Beginner
Duration: Approximately 12 Hours

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