In order to be successful in Data Science, you need to be skilled with using tools that Data Science professionals employ as part of their jobs. This course teaches you about the popular tools in Data Science and how to use them.

Über diesen Kurs
Anyone can complete this course in a self-paced manner without any prior programming or Data Science experience.
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Probieren Sie Coursera for Business ausWas Sie lernen werden
Describe the Data Scientist’s tool kit which includes: Libraries & Packages, Data sets, Machine learning models, and Big Data tools
Utilize languages commonly used by data scientists like Python, R, and SQL
Demonstrate working knowledge of tools such as Jupyter notebooks and RStudio and utilize their various features
Create and manage source code for data science using Git repositories and GitHub.
Kompetenzen, die Sie erwerben
- Data Science
- Github
- Python Programming
- Jupyter notebooks
- Rstudio
Anyone can complete this course in a self-paced manner without any prior programming or Data Science experience.
Könnte Ihr Unternehmen von Mitarbeiterweiterbildungen für gefragte Kompetenzen profitieren?
Probieren Sie Coursera for Business ausBeginnen Sie damit, auf Ihren Bachelor-Abschluss hinzuarbeiten
Lehrplan - Was Sie in diesem Kurs lernen werden
Data Scientist's Toolkit
Open Source Tools
IBM Tools for Data Science
Final Assignment: Create and Share Your Jupyter Notebook
Bewertungen
- 5 stars66,82 %
- 4 stars22,19 %
- 3 stars6,76 %
- 2 stars2,21 %
- 1 star1,99 %
Top-Bewertungen von TOOLS FOR DATA SCIENCE
Tools are fantastic and will make a significant contribution to my education. Videos need to be updated for changes to Watson Studios. Support from IBM on their cloud services should also be improved.
Excellent introduction to a variety of open source tools available and commonly used by Data Science Professionals. Some of the video material is slightly outdated due to updates to the platform.
The course contents are verry good and well structured except that Watson Studio tools and services options need to be explaind more, specially those used in the lab steps or explained in the videos.
Good content, excellent delivery. Week1 had too much of information at once. I was left with little motivation after week-1. All the other weeks were good. Exercises were engaging and very useful.
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