In this course you’ll receive one of the most comprehensive overviews on open source and commercial tooling available for data science, and the skills on how to use them.
What are some of the most popular data science tools, how do you use them, and what are their features?
In this course, you'll learn about the day-to-day experiences of Data Scientists. You’ll be introduced to some of the programming languages commonly used, including Python, R, Scala, and SQL. You’ll work with the tools that professional Data Scientists work with, like Jupyter Notebooks, RStudio IDE, and others. You will learn about what each tool is used for, what languages they can execute, and their features and limitations. With the tools hosted in the cloud on Cognitive Class Labs, you will be able to use each tool and follow instructions to run simple code in Python, R, or Scala.
We have created this course so that anyone with basic computer skills would be able to learn about the tools for data science. The only prerequisite for this course is your desire to learn.
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08 Oct 2020 (Aije Egwaikhide): Re-ordered course and dividd modules into 7 parts
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01 Sept 2020: Updated version of the course published on edX.org.
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01 Sept 2020 (Sonia Gupta): Replaced links to labs with links from SN Asset Library.
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23 Mar 2020: Initial version of the course published on edX.org.
- Module 0 - Welcome and Course Introduction
- Module 1 - Languages of Data Science
- Module 2 - Data Science Tools
- Module 3 - Packages, APIs, Data Sets and Models
- Module 4 - GitHub
- Module 5 - Jupyter Notebooks and JupyterLab
- Module 6 - RStudio IDE
- Module 7 - Watson Studio
- Flink is a stream processing
- Flink is image processing, with its mains focus on processus
- it focus large-scale deep learning model training
- it combine jupyter notebook with graphical tools to mixamize data scientist performance
- integrated and cover fully in the data science live cycle and all the tache we discuss previously
This clusters are composed of multiple server machines transparently for the user in the background.
Watson Studio, together with Watson OpenScale, covers the complete development lefe cycle all data science machine learning ad IA tasks
cloudhosted offering supporting the complete
development life cycle of all data science, machine learning and IA tasks.
it is Amazon Web services DynamoDB, a noSQL database that akkows storage and retrieval of data in key value or document store format.
it is a database as a service offering
it is apache couch DB, it has a adventage although complex operational tasks like updating backup restore and scaling are done by the cloud privider under the hood. This offering is compatible with couch DB, therefore the application can be migrated to another couch DB server without changing the application.
When it comes to commercial data integration tools, we talk not only about "Extract, Transform, and Load", or "ETL" tools, but also about "Extract, Load, and Transform", or "ELT" tools.
Data Refinery enables transformation of large amounts of raw data into consumable quality information in a spread sheet-like user interface.
In watson Studio, an abundance of different visualizations can be used to better understand data
- The primary instrument of Pandas is a two-dimensional table consisting of columns and rows. This table is caled a "DataFrame" and designed to provide easy indexing so you can work with your data.
- Numpy libraries are based on arrays, enabling you to apply mathematical functions to these, pandas is actually build on top of Numpy.
Data Visualization methods are a great way to communicate with others and show the meaningfull results of analysis. These libraries enable you to create graphs, charts and maps.
- Matplotlib(plots & most popular) the Matplotlib package is the most well-know library data visualization, and it's excellent for making graphs and plots.
- seaborn is based on matplotlib. Seaborn makes it easy to generate plots like heat maps, time series, and violon plots.
- For machine learning, the Scikit-learn library contains tools for statistical modeling, including regression, classification, clustering and others. It is built on Numpy, Scipy and Matplotlib and it's relatively simple to get started.
- For Deep Learning, Keras enable you to build the standard deep learning model. Like Scikit-learning, the high-level interface enables you to build models quickly and simply. It can function using graphics processing units(GPU), but for many deep learning cases a lowel-level environment is required.
- TensorFlow is a low-level framework used in large production of deep learning models. It designed for production but can be unwieldy for experimentation.
- PyTorch is used for experimentation, making it simple for researchers to test their ideas.
Apache Spark is a general-purpose cluster-computing framework that enables you to process data using compute clusters.
This means that you process data in parallel,
using multiple computers simultaneously.
- The Spark library has similar functionnality as Pandas, Numpy, and Scikit-learng
In Spark Data processing, you need Python, R, Scala, Or SQL.
- Vegas is a Scala library for statistical data visualizations. With Vegas, you can work with data files as well as Spark DataFrames.
- For Deep Learning, you can use Big DL.
R has built-in function for machine learning and data visualization. There are also several coçmplementary libraries:
- ggplot2 is a popular library for data visualization
- interface with keras and tensorFlow.
- R has been the de-facto standard for open source data science but it is now being superseded by Python.
A version control system allows you to keep track of changes to your documents. This makes it easy for you to recover older versions of your documents if you make a mistake, and it makes collaboration with others much easier.
Git is free and open source software distributed under the GNU General Public Licence.
Github is one of the most popular web-hosted services for Git repositories.
