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Senior Data Scientist
This track provides an understanding and hands-on application of Advanced Machine Learning techniques – Ensemble Learning, Boosting, Bagging, Deep Learning, Time Series Forecasting, Text Analytics, Explainable AI, Network Flow Optimization and Operations Research with the help of real life case studies and projects.
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18 Courses
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20 Projects & Case Studies
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Difficulty: Advanced
Prior knowledge or professional experience is highly recommended
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Duration: Approximately 1 Year
Suggested learning pace is 5hr/week
Course Overview
- Learn the concepts underlying various ensemble techniques like Bagging and Boosting in supervised learning, advanced clustering algorithms in unsupervised learning, text analytics for sentiment analysis and forecasting using advanced time series models.
- Learn the fundamental concepts of Deep Learning starting from the simplest Neural Network model to the Multi-Layered Perceptron model, different architectures and techniques for regularization and optimization.
- Develop a working understanding of the concepts and techniques of Explainable AI methods like LIME, SHAP and their application to evaluate the performance of an ML model for comparison and to understand the predictions of different models.
- Learn the essentials of mathematical optimization techniques like Linear Programming, Mixed-Integer Programming, along with a detailed study of specialized optimization problems like Knapsack, Travelling Salesman and Assignment problem.
What’s included
Lifetime Access
Access this learning track for life once completed
Flexible Scheduling
Start learning online immediately, at your own pace
Desktop Only
We recommend completing this learning track on a desktop
Skills you will learn
- Bagging and Random Forest in Machine Learning
- Introduction to Gradient Boosting Classification
- Introduction to Extreme Gradient Boosting Classifier
- Introduction to AdaBoost Classifier
- Catboost Classifiers – An Introduction
- Concepts and Application of Objective and Subjective Segmentation
- Introduction to Neural Networks
- Fundamentals of Time Series Analysis
- Introduction to Optimization Techniques
- Optimization Techniques – Specialized Optimization Problems
- Optimization Techniques – Network Flow Problems
- Introduction to Natural Language Processing (NLP)Mining Text Data Cleansing, Treatment, Structural Representation & Visualization
- Text Analytics – Classification and Clustering
- Sentiment Analysis – Using Unstructured Text Data
- Introduction to Explainable AI (XAI) using LIME
- Introduction to Explainable AI (XAI) using SHAP
- Introduction to Explainable AI (XAI) for Text using LIME & SHAP
- Predicting Acquisition of Start Ups
- Predicting Carbon Dioxide Emission by Cars Using Boosting Technique
- Predicting Traffic, Driving Style & Road Surface Conditions by applying Advanced Classification Techniques
- Anomaly Detection in Manufacturing Spare Parts Using Machine Learning Techniques
- Grouping the Driving Styles based on Telematics Data
- Clustering on Locations Services from Vehicle Telematics Data for Service Center Location
- Application of various clustering techniques to group the steel type based on its mechanical properties
- Predicting Credit Card Spend – Artificial Neural Network
- Predicting Customer Churn – Artificial Neural Network
- Forecasting Vehicle Registration for Sales Trends on Monthly basis
- Forecast daily electricity prices for hedging – Application of ARIMA
- Forecasting the Demand & Price of Manufacturing Auto Parts Using Time Series Analysis
- Exploratory Data Analysis On Spam Text Classification
- Sentiment Analysis on Car Reviews
- Application of Text Classification on Women’s E-Commerce Clothing Reviews
- Customer feedback analysis for a Cab Aggregator Platform using NLP Techniques
- Sentiment Analysis and Root Cause Analysis for Production Reviews of Headphones through Text Logs
- Predicting Car Price using XAI
- Credit Risk Modelling – Probability of Default: Model Comparison & XAI
- Predicting Income Using Adult Census Data – Supervised ML Model Comparison & Explainable AI
How it Works
Learn new skills that will boost your career by enrolling in courses across data analytics, data science, ML and AI. These courses will utilize readings, videos, quizzes, data cases, and even coding exercises to teach you skills and concepts in a way that will solidify your new knowledge for hands-on application.
With our hands-on projects, you will take your newly learned skills along with our 750+ low-code/no-code functions and embedded coding console to complete milestone-based projects. Once completed, you will have effectively applied new skills and concepts to real-world data cases that can be translated directly into your career.
Complete assessments and track your progress in real-time to benchmark your proficiency in relation to key functional areas. As you progress through your courses, our patented platform will utilize ML and AI to record and analyze your inputs and output to provide active feedback and recommendations that will help you learn more effectively than the standard Letter Grade system used today.
Learner Outcomes
Complete learning tracks to earn shareable certificates and badges. These awarded items will look great in your portfolio, resume, and LinkedIn as you showcase your skills and project experience to employers and colleagues.
- Understand different ensemble methods like Bagging and Boosting and apply advanced models like GradBoost, CatBoost, AdaBoost and perform detailed sentiment analysis using NLP techniques.
- Understand the fundamental concepts of Deep Learning, build Neural Network models and apply different regularization and optimization techniques to improve modelling results.
- Gain a comprehensive understanding of Explainable AI (XAI) techniques, importance of XAI and the application of LIME and SHAP methodologies to compare the performance of ML models and understand the predictions of different models.
- Understand and apply advanced Time Series Modelling techniques and apply a variety of optimization techniques like Linear Programming, and Mixed-Integer Programming to solve specialized problems and generate business insights.
- Interpret and visualize the outcome of the applied techniques.
“Rolai provides contextual upskilling opportunities … on one single platform.”
“An excellent tool for anyone who wants to quickly learn the ropes.”
“Rolai is the best program available for someone looking to enhance their skills”
“Great way to learn data analytics and data science”
“The courses were excellent and covered topics that I didn’t expect”
Rolai’s patented process provides a personalized learning process for each user. Rolai goes deeper than simply learning concepts and testing your skills. At Rolai, learners can apply their skills to actual industry use cases and projects.
Our courses include readings, videos, quizzes, and hands-on data cases that are completed using our virtual lab; give learners an applied learning experience.
No additional tools are needed to begin learning with Rolai. Our virtual lab contains the necessary data workspace and an embedded coding console.
- We have internal SMEs across industries and domains that we work with to develop relevant content and assure quality datasets and problem statements.
- We also work with enterprises and universities to develop new content directed towards their industry and expertise.
