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Отзывы учащихся о курсе Applied Data Science for Data Analysts от партнера Databricks

Оценки: 29
Рецензии: 7

О курсе

In this course, you will develop your data science skills while solving real-world problems. You'll work through the data science process to and use unsupervised learning to explore data, engineer and select meaningful features, and solve complex supervised learning problems using tree-based models. You will also learn to apply hyperparameter tuning and cross-validation strategies to improve model performance. NOTE: This is the third and final course in the Data Science with Databricks for Data Analysts Coursera specialization. To be successful in this course we highly recommend taking the first two courses in that specialization prior to taking this course. These courses are: Apache Spark for Data Analysts and Data Science Fundamentals for Data Analysts....
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1–6 из 6 отзывов о курсе Applied Data Science for Data Analysts

автор: Xiaoyue Z

19 дек. 2020 г.

Very bad notebooks/Exam. Bug in exam, instruction missing in notebook which cause it impossible to calculate the correct answer. Don't know why coursera put such un-tested, un-updated, un-reviewed course online.

автор: Saurab R R

7 июля 2021 г.

Was expecting spark based Mlib not scikit learn

автор: Ivan O C

23 июня 2021 г.

Nice course, its approach is the right mix between theory and hands-on exercise on the databricks platform...

автор: Leandro C

17 апр. 2021 г.

Perfect and objective content. Simple and practical exercises. I strongly recommend!

автор: Dzmitry S

8 янв. 2022 г.

G​ood course with good practical tasks (felt relatively simple for me, but may be more challenging for others). It's happy to see quizes as performance evaluation in comparison with peer reviews from previous specialization course. However I lack Databricks features usage and explanations (MlFlow expecially). Also Databricks runtime used during course preparation is no longer available for selection as cluster configuration - it results in different answers for specific questions (it was very painful with K-Means clustering - I just had other ordering cluster labels)

автор: stephane d

29 дек. 2020 г.

Great course for an overview but with a high level of abstraction (usage of existing libraries but very little coding of algorithms that show the details of the principles)