Специализация Practical Data Science

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• Действительно ли это полностью дистанционный курс? Нужно ли мне посещать какие-либо занятия лично?

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• What is the Practical Data Science Specialization about?

  Development environments might not have the exact requirements as production environments. Moving data science and machine learning projects from idea to production requires state-of-the-art skills. You need to architect and implement your projects for scale and operational efficiency. Data science is an interdisciplinary field that combines domain knowledge with mathematics, statistics, data visualization, and programming skills. The Practical Data Science Specialization brings together these disciplines using purpose-built ML tools in the AWS cloud. It helps you develop the practical skills to effectively deploy your data science projects and overcome challenges at each step of the ML workflow using Amazon SageMaker. This Specialization is designed for data-focused developers, scientists, and analysts familiar with the Python and SQL programming languages and want to learn how to build, train, and deploy scalable, end-to-end ML pipelines - both automated and human-in-the-loop - in the AWS cloud. Each of the 10 weeks features a comprehensive lab developed specifically for this Specialization that provides hands-on experience with state-of-the-art algorithms for natural language processing (NLP) and natural language understanding (NLU), including BERT and FastText using Amazon SageMaker. In this Specialization, you will master how to remove obstacles to advance your projects from idea to deployment quickly and to scale to thousands of models or more. Learn how to prepare massive datasets originating from social media channels, mobile/web applications, or other public/private data sources that don’t fit in your local hardware and use them for training models. Understand how to tune models for the highest accuracy and then deploy and manage them over time.

• What are the benefits of running data science projects in the cloud?

  One of the biggest benefits of developing data science projects in the cloud is the agility to scale up and out at a minimum cost. The cloud brings together data, low-cost storage, security, and ML services along with high-performance CPUs and GPUs for model training and deployment. As a result, you can store as much data as you need and use high-performance compute elastically, so it’s much faster to realize the value of machine learning. This Specialization provides tools to analyze and clean the data, extract relevant features, and teach you how to use tools purpose-built for ML such as visual development environment, debuggers, profilers, and pipelines to scale models building, training, and deployment.

• What will I learn in the Practical Data Science Specialization?

  1. Prepare data, detect statistical data biases, and perform feature engineering at scale to train models
  2. Automatically train, evaluate, and tune models with automated machine learning (AutoML)
  3. Store and manage machine learning features using a feature store
  4. Debug, profile, tune and evaluate models while tracking data lineage and model artifacts
  5. Build, deploy, monitor, and operationalize end-to-end machine learning pipelines.
  6. Build data labeling and human-in-the-loop pipelines to improve model performance with human intelligence

• What will I be able to do upon completing the Practical Data Science Specialization?

  By the end of this Specialization, you will be ready to:

  • Ingest, register, and explore datasets
  • Detect statistical bias in a dataset
  • Automatically train and select models with AutoML
  • Create machine learning features from raw data
  • Save and manage features in a feature store
  • Train and evaluate models using built-in algorithms and custom BERT models
  • Debug, profile, and compare models to improve performance
  • Build and run a complete ML pipeline end-to-end
  • Optimize model performance using hyperparameter tuning
  • Deploy and monitor models
  • Perform data labeling at scale
  • Build a human-in-the-loop pipeline to improve model performance
  • Reduce cost and improve performance of data products

• What background knowledge is necessary for the Practical Data Science Specialization?

  Learners should have a working knowledge of ML algorithms and principles, be proficient in Python programming at an intermediate level, and be familiar with Jupyter notebooks and statistics. We recommend you complete the Deep Learning Specialization or an equivalent program. 

  Learners should also be familiar with the fundamentals of AWS and cloud computing. Completion of Coursera AWS Cloud Technical Essentials or similar is considered the prerequisite knowledge base.

• Who is the Practical Data Science Specialization for?

  This Specialization is designed for data-focused developers, scientists, and analysts familiar with the Python and SQL programming languages who want to learn how to build, train, and deploy scalable, end-to-end ML pipelines - both automated and human-in-the-loop - in the AWS cloud.

• How long does it take to complete the Practical Data Science Specialization?

  This Specialization consists of three courses. At the rate of 5 hours a week, it typically takes 4 weeks to complete course 1, 3 weeks to complete course 2, and 3 weeks to complete course 3.

• Who created the Practical Data Science Specialization?

  This Specialization was created by Antje Barth, Sireesha Muppala, Shelbee Eigenbrode, and Chris Fregly. 

  Antje Barth is a Sr. Developer Advocate for AI and Machine Learning at Amazon Web Services (AWS). She is co-author of the O'Reilly book - Data Science on AWS. Antje frequently speaks at AI / ML conferences, events, and meetups around the world. Previously, Antje worked in technical evangelism and solutions engineering at Cisco and MapR, focused on data center technologies, big data, and AI applications. Antje is also a co-founder of the Düsseldorf chapter of Women in Big Data.

  Sireesha Muppala is an Enterprise Principal SA, AI/ML at Amazon Web Services (AWS) who guides customers on architecting and implementing machine learning solutions at scale. She received her Ph.D. in Computer Science from the University of Colorado, Colorado Springs, and has authored several research papers, white papers, blog articles. Sireesha frequently speaks at industry conferences, events, and meetups. She co-founded the Denver chapter of Women in Big Data.

  Shelbee Eigenbrode is a Principal AI and Machine Learning Specialist Solutions Architect at Amazon Web Services (AWS). She holds 6 AWS certifications and has been in technology for 23 years spanning multiple industries, technologies, and roles. She is currently focusing on combining her DevOps and ML background to deliver and manage ML workloads at scale. With over 35 patents granted across various technology domains, she has a passion for continuous innovation and using data to drive business outcomes. Shelbee co-founded the Denver chapter of Women in Big Data.

  Chris Fregly is a Principal Developer Advocate for AI and Machine Learning at Amazon Web Services (AWS) based in San Francisco, California. He is co-author of the O'Reilly Book, "Data Science on AWS." Chris is also the founder of the global "Data Science on AWS" Meetup series.  He regularly speaks at AI and Machine Learning conferences across the world including O’Reilly AI, Open Data Science Conference (ODSC), and Nvidia GPU Technology Conference (GTC).

• Is this a standalone course or a Specialization?

  The Practical Data Science Specialization is made up of 3 courses.

• How do I take the Specialization?

  You can enroll in the Practical Data Science Specialization on Coursera. You will watch videos and complete assignments on Coursera as well.

• Do I need to take the courses in a specific order?

  We recommend taking the courses in the prescribed order for a logical and thorough learning experience.

• How much does the Specialization cost?

  A Coursera subscription costs $49 / month.

• Can I apply for financial aid?

  Yes, Coursera provides financial aid to learners who cannot afford the fee.

• Can I audit the Practical Data Science Specialization?

  You can audit the courses in the Practical Data Science Specialization for free. 

  Note that you will not receive a certificate at the end of the course if you choose to audit it for free instead of purchasing it.

• How do I get a receipt to get this reimbursed by my employer?

  1. Go to your Coursera account. 
  2. Click on My Purchases and find the relevant course or Specialization.
  3. Click Email Receipt and wait up to 24 hours to receive the receipt. 

  You can read more about it here.

• I want to purchase this Specialization for my employees. How can I do that?

  Visit coursera.org/business for more information, to pick up a plan, and to contact Coursera. For each plan, you decide the number of courses every member can enroll in and the collection of courses they can choose from.

• Will I receive a certificate at the end of the Specialization?

  You will receive a certificate at the end of each course if you pay for the courses and complete the programming assignments. There is a limit of 180 days of certificate eligibility, after which you must re-purchase the course to obtain a certificate. If you audit the course for free, you will not receive a certificate.

  If you complete all 4 courses and are subscribed to the Specialization, you will also receive an additional certificate showing that you completed the entire Specialization.

• Получу ли я зачеты в университете за прохождение специализации?

  No

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