Generative AI for Business Leaders
Advertising with Meta
How Google does Machine Learning
دوراتنا
Data Warehouse Concepts, Design, and Data Integration
This is the second course in the Data Warehousing for Business Intelligence specialization. Ideally, the courses should be taken in sequence. In this course, you will learn exciting concepts and skills for designing data warehouses and creating data integration workflows. These are fundamental skills for data warehouse developers and administrators. You will have hands-on experience for data warehouse design and use open source products for manipulating pivot tables and creating data integration workflows.
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Course by
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Self Paced
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62 ساعات
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الإنجليزية
Data Science in Real Life
Have you ever had the perfect data science experience? The data pull went perfectly. There were no merging errors or missing data. Hypotheses were clearly defined prior to analyses. Randomization was performed for the treatment of interest. The analytic plan was outlined prior to analysis and followed exactly. The conclusions were clear and actionable decisions were obvious. Has that every happened to you? Of course not. Data analysis in real life is messy. How does one manage a team facing real data analyses? In this one-week course, we contrast the ideal with what happens in real life.
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Course by
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Self Paced
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7 ساعات
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الإنجليزية
Launching into Machine Learning
The course begins with a discussion about data: how to improve data quality and perform exploratory data analysis. We describe Vertex AI AutoML and how to build, train, and deploy an ML model without writing a single line of code. You will understand the benefits of Big Query ML. We then discuss how to optimize a machine learning (ML) model and how generalization and sampling can help assess the quality of ML models for custom training.
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Course by
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Self Paced
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14 ساعات
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الإنجليزية
Using clinical health data for better healthcare
Digital health is rapidly being realised as the future of healthcare.
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Course by
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Self Paced
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16 ساعات
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الإنجليزية
PyTorch and Deep Learning for Decision Makers
Learn how PyTorch, a deep learning framework, can be used to automate and optimize processes through the development and deployment of state-of-the-art AI applications. The course will also help you understand the importance of data quality, how to choose the right model, and the challenges in deploying and maintaining both deep learning and machine learning applications.
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Course by
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Self Paced
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15
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الإنجليزية
Design Strategies for Maximizing Total Data Quality
By the end of this third course in the Total Data Quality Specialization, learners will be able to: 1. Learn about design tools and techniques for maximizing TDQ across all stages of the TDQ framework during a data collection or a data gathering process. 2. Identify aspects of the data generating or data gathering process that impact TDQ and be able to assess whether and how such aspects can be measured. 3. Understand TDQ maximization strategies that can be applied when gathering designed and found/organic data. 4.
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Course by
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Self Paced
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9 ساعات
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الإنجليزية
The Total Data Quality Framework
By the end of this first course in the Total Data Quality specialization, learners will be able to: 1. Identify the essential differences between designed and gathered data and summarize the key dimensions of the Total Data Quality (TDQ) Framework; 2. Define the three measurement dimensions of the Total Data Quality framework, and describe potential threats to data quality along each of these dimensions for both gathered and designed data; 3.
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Course by
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Self Paced
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12 ساعات
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الإنجليزية
ETL and Data Pipelines with Shell, Airflow and Kafka
Delve into the two different approaches to converting raw data into analytics-ready data. One approach is the Extract, Transform, Load (ETL) process. The other contrasting approach is the Extract, Load, and Transform (ELT) process. ETL processes apply to data warehouses and data marts. ELT processes apply to data lakes, where the data is transformed on demand by the requesting/calling application. In this course, you will learn about the different tools and techniques that are used with ETL and Data pipelines.
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Course by
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Self Paced
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17 ساعات
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الإنجليزية
Machine Learning Data Lifecycle in Production
**Starting May 8, enrollment for the Machine Learning Engineering for Production Specialization will be closed.
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Course by
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Self Paced
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22 ساعات
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الإنجليزية
Clinical Data Models and Data Quality Assessments
This course aims to teach the concepts of clinical data models and common data models. Upon completion of this course, learners will be able to interpret and evaluate data model designs using Entity-Relationship Diagrams (ERDs), differentiate between data models and articulate how each are used to support clinical care and data science, and create SQL statements in Google BigQuery to query the MIMIC3 clinical data model and the OMOP common data model.
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Course by
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Self Paced
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18 ساعات
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الإنجليزية
Data Collection: Online, Telephone and Face-to-face
This course presents research conducted to increase our understanding of how data collection decisions affect survey errors.
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Course by
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Self Paced
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21 ساعات
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الإنجليزية
Data Cleaning in Snowflake: Techniques to Clean Messy Data
in 2006, the British mathematician Clive Humby coined the phrase "Data is the new Oil". This analogy has been proven correct as data powers entire industries nowadays but if left unrefined, is effectively worthless. This 2.5 hours-long guided project is designed for business analysts & data engineers eager to learn how to Clean Messy Data in Snowflake Data Platform. By the end of the project, you will -Be able to identify common data quality issues then use SQL String functions to remove unwanted characters and split rows into multiple columns.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Measuring Total Data Quality
By the end of this second course in the Total Data Quality Specialization, learners will be able to: 1. Learn various metrics for evaluating Total Data Quality (TDQ) at each stage of the TDQ framework. 2. Create a quality concept map that tracks relevant aspects of TDQ from a particular application or data source. 3. Think through relative trade-offs between quality aspects, relative costs and practical constraints imposed by a particular project or study. 4. Identify relevant software and related tools for computing the various metrics. 5.
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Course by
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Self Paced
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9 ساعات
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الإنجليزية
Using Descriptive Statistics to Analyze Data in R
By the end of this project, you will create a data quality report file (exported to Excel in CSV format) from a dataset loaded in R, a free, open-source program that you can download. You will learn how to use the following descriptive statistical metrics in order to describe a dataset and how to calculate them in basic R with no additional libraries.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Healthcare Data Models
Career prospects are bright for those qualified to work in healthcare data analytics. Perhaps you work in data analytics, but are considering a move into healthcare where your work can improve people’s quality of life.
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Course by
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Self Paced
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12 ساعات
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الإنجليزية
Data Science Fundamentals
This specialization demystifies data science and familiarizes learners with key data science skills, techniques, and concepts. The course begins with foundational concepts such as analytics taxonomy, the Cross-Industry Standard Process for Data Mining, and data diagnostics, and then moves on to compare data science with classical statistical techniques.
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Course by
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Self Paced
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الإنجليزية
Healthcare Data Quality and Governance
Career prospects are bright for those qualified to work with healthcare data or as Health Information Management (HIM) professionals. Perhaps you work in data analytics but are considering a move into healthcare, or you work in healthcare but are considering a transition into a new role. In either case, Healthcare Data Quality and Governance will provide insight into how valuable data assets are protected to maintain data quality. This serves care providers, patients, doctors, clinicians, and those who carry out the business of improving health outcomes.
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Course by
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Self Paced
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12 ساعات
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الإنجليزية
Total Data Quality
This specialization aims to explore the Total Data Quality framework in depth and provide learners with more information about the detailed evaluation of total data quality that needs to happen prior to data analysis. The goal is for learners to incorporate evaluations of data quality into their process as a critical component for all projects.
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Course by
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Self Paced
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الإنجليزية