Part of the Learn SQL Basics for Data Science Specialization

This course allows you to apply the SQL skills taught in “SQL for Data Science” to four increasingly complex and authentic data science inquiry case studies. We'll learn how to convert timestamps of all types to common formats and perform date/time calculations. We'll select and perform the optimal JOIN for a data science inquiry and clean data within an analysis dataset by deduping, running quality checks, backfilling, and handling nulls. We'll learn how to segment and analyze data per segment using windowing functions and use case statements to execute conditional logic to address a data science inquiry. We'll also describe how to convert a query into a scheduled job and how to insert data into a date partition. Finally, given a predictive analysis need, we'll engineer a feature from raw data using the tools and skills we've built over the course. The real-world application of these skills will give you the framework for performing the analysis of an AB test.

In this workshop, we’ll explore how GPUs can improve data pipelines and how using advanced data engineering tools and techniques can result in significant performance acceleration. Faster pipelines produce fresher dashboards and machine learning (ML) models, so users can have the most current information at their fingertips.

Participants in this workshop will learn:

• How data moves within a computer. How to build the right balance between CPU, DRAM, Disk Memory, and GPUs. How different file formats can be read and manipulated by hardware.
• How to scale an ETL pipeline with multiple GPUs using NVTabular.
• How to build an interactive Plotly dashboard where users can filter on millions of data points in less than a second.

In this workshop, we’ll explore how GPUs can improve data pipelines and how using advanced data engineering tools and techniques can result in significant performance acceleration. Faster pipelines produce fresher dashboards and machine learning (ML) models, so users can have the most current information at their fingertips.

Participants in this workshop will learn:

• How data moves within a computer.
• How to build the right balance between CPU, DRAM, Disk Memory, and GPUs.
• How different file formats can be read and manipulated by hardware.
• How to scale an ETL pipeline with multiple GPUs using NVTabular.
• How to build an interactive Plotly dashboard where users can filter on millions of data points in less than a second.

Part of the Natural Language Processing Specialization

In Course 1 of the Natural Language Processing Specialization, you will:

• Perform sentiment analysis of tweets using logistic regression and then naïve Bayes,
• Use vector space models to discover relationships between words and use PCA to reduce the dimensionality of the vector space and visualize those relationships, and
• Write a simple English to French translation algorithm using pre-computed word embeddings and locality-sensitive hashing to relate words via approximate k-nearest neighbor search.

Part of the Data Visualization with Tableau Specialization

In this third course of the specialization, we’ll drill deeper into the tools Tableau offers in the areas of charting, dates, table calculations and mapping. We’ll explore the best choices for charts, based on the type of data you are using. We’ll look at specific types of charts including scatter plots, Gantt charts, histograms, bullet charts and several others, and we’ll address charting guidelines. We’ll define discrete and continuous dates, and examine when to use each one to explain your data. You’ll learn how to create custom and quick table calculations and how to create parameters. We’ll also introduce mapping and explore how Tableau can use different types of geographic data, how to connect to multiple data sources and how to create custom maps.

Part of the Machine Learning Engineering for Production (MLOps) Specialization

In the first course of Machine Learning Engineering for Production Specialization, you will identify the various components and design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment constraints and requirements; and learn how to establish a model baseline, address concept drift, and prototype the process for developing, deploying, and continuously improving a productionized ML application.

Part of the IBM AI Enterprise Workflow Specialization

This first course in the IBM AI Enterprise Workflow Certification specialization introduces you to the scope of the specialization and prerequisites. Specifically, the courses in this specialization are meant for practicing data scientists who are knowledgeable about probability, statistics, linear algebra, and Python tooling for data science and machine learning. A hypothetical streaming media company will be introduced as your new client. You will be introduced to the concept of design thinking, IBMs framework for organizing large enterprise AI projects. You will also be introduced to the basics of scientific thinking, because the quality that distinguishes a seasoned data scientist from a beginner is creative, scientific thinking. Finally you will start your work for the hypothetical media company by understanding the data they have, and by building a data ingestion pipeline using Python and Jupyter notebooks.

Part of the Data Visualization with Tableau Specialization

In this course, you will analyze and apply essential design principles to your Tableau visualizations. This course assumes you understand the tools within Tableau and have some knowledge of the fundamental concepts of data visualization. You will define and examine the similarities and differences of exploratory and explanatory analysis as well as begin to ask the right questions about what’s needed in a visualization. You will assess how data and design work together, including how to choose the appropriate visual representation for your data, and the difference between effective and ineffective visuals. You will apply effective best practice design principles to your data visualizations and be able to illustrate examples of strategic use of contrast to highlight important elements. You will evaluate pre-attentive attributes and why they are important in visualizations. You will exam the importance of using the “right” amount of color and in the right place and be able to apply design principles to de-clutter your data visualization.

Part of the Information Visualization Specialization

This course aims at introducing fundamental knowledge for information visualization. The main goal is to provide the students with the necessary “vocabulary” to describe visualizations in a way that helps them reason about what designs are appropriate for a given problem. This module also gives a broad overview of the field of visualization, introducing its goals, methods and applications.

Part of the IBM Applied AI Professional Certificate

This course will teach you how to create useful chatbots without the need to write any code. Leveraging IBM Watson's Natural Language Processing capabilities, you'll learn how to plan, implement, test, and deploy chatbots that delight your users, rather than frustrate them.

Part of the Data Visualization with Tableau Specialization

In this first course of this specialization, you will discover what data visualization is, and how we can use it to better see and understand data. Using Tableau, we’ll examine the fundamental concepts of data visualization and explore the Tableau interface, identifying and applying the various tools Tableau has to offer. By the end of the course you will be able to prepare and import data into Tableau and explain the relationship between data analytics and data visualization. This course is designed for the learner who has never used Tableau before, or who may need a refresher or want to explore Tableau in more depth. No prior technical or analytical background is required. The course will guide you through the steps necessary to create your first visualization from the beginning based on data context, setting the stage for you to advance to the next course in the Specialization.

Recommender Systems are everywhere, and as businesses go digital into the online marketplace, these product recommendation engines can offer highly personalized suggestions to a wide diversity of users. In this course, students will learn:

• A survey of different Recommender System algorithms and their strengths
• How to overcome the practical challenges of working with real data
• How to deploy a production system

Upon completion, students will know how to design and deploy a Recommender System tailored to their unique business needs.

• Learn the fundamental techniques and tools required to train a deep learning model
• Gain experience with common deep learning data types and model architectures
• Enhance datasets through data augmentation to improve model accuracy
• Leverage transfer learning between models to achieve efficient results with less data and computation
• Build confidence to take on your own project with a modern deep learning framework

• Implement common deep learning workflows such as Image Classification and Object Detection.
• Experiment with data, training parameters, network structure, and other strategies to increase performance and capability.
• Deploy your networks to start solving real-world problems.

• Prepare data and build, train, and evaluate models using XGBoost, autoencoders, and GANs
• Detect anomalies in datasets with both labeled and unlabeled data
• Classify anomalies into multiple categories regardless of whether the original data was labeled

Part of the Statistics with Python Specialization

In this course, learners will be introduced to the field of statistics, including where data come from, study design, data management, and exploring and visualizing data. Learners will identify different types of data, and learn how to visualize, analyze, and interpret summaries for both univariate and multivariate data. Learners will also be introduced to the differences between probability and non-probability sampling from larger populations, the idea of how sample estimates vary, and how inferences can be made about larger populations based on probability sampling.

Part of the Statistics with Python Specialization

In this course, learners will be introduced to the field of statistics, including where data come from, study design, data management, and exploring and visualizing data. Learners will identify different types of data, and learn how to visualize, analyze, and interpret summaries for both univariate and multivariate data. Learners will also be introduced to the differences between probability and non-probability sampling from larger populations, the idea of how sample estimates vary, and how inferences can be made about larger populations based on probability sampling.

• Sequence Models
• Convolutional Neural Networks
• Structuring Machine Learning Projects
• Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
• Neural Networks and Deep Learning

• Programming for Everybody (Getting Started with Python)
• Python Data Structures
• Using Python to Access Web Data
• Using Databases with Python
• Capstone: Retrieving, Processing, and Visualizing Data with Python

Role: Lecturer & Responsible of Practial Sessions

Mandatory course of an Artificial Intelligence Specialization

Role: Responsible of Practial Sessions

Elective course for Bachelor Software Engineering in and PhD in Computer Sciences

Role: Responsible of Practial Sessions

Elective course for Bachelor Software Engineering in and PhD in Computer Sciences

Role: Teaching Assistant

Mandatory fourth year course of Bachelor in Software Engineering

Role: Lecturer & Responsible of Practial Sessions

Elective course for Bachelor Software Engineering in and PhD in Computer Sciences

Role: Teaching Assistant

Mandatory first year course of Programmer Analyst

Role: Teaching Assistant

Mandatory first year course of Bachelor in Software Engineering

Role: Teaching Assistant

Mandatory third year course of Bachelor in Software Engineering

Role: Teaching Assistant

Mandatory second year course of Bachelor in Software Engineering

Role: Teaching Assistant

Mandatory third year course of Bachelor in Software Engineering

Role: Teaching Assistant

Mandatory first year course of Bachelor in Software Engineering