Tools for Data Science

Go to Course: https://www.coursera.org/learn/open-source-tools-for-data-science

Introduction

**Course Review: Tools for Data Science on Coursera** In today's data-driven world, the demand for data science skills is rapidly growing across various industries. The "Tools for Data Science" course on Coursera provides a robust introduction to the essential tools and technologies that data scientists utilize in their work. This comprehensive course is perfect for beginners and those looking to enhance their existing skills in data science. ### Overview The "Tools for Data Science" course aims to familiarize you with the foundational toolkit of a data scientist. From programming languages to cloud-based platforms, this course covers a wide range of tools necessary for successful data analysis and model building. If you've ever felt overwhelmed by the myriad of options available in data science, this course will guide you through the possibilities, providing clarity and structured learning. ### Detailed Breakdown of Course Syllabus **1. Overview of Data Science Tools** The first module introduces you to the various categories and examples of popular data science tools, including open-source, cloud-based, and commercial options. This gives you a solid foundation on which to build your understanding of the data science landscape. **2. Languages of Data Science** In this module, you will explore common programming languages used in data science, including Python, R, and SQL. The lesson structure helps you determine which language might be best for your needs. This targeted approach is beneficial for anyone feeling uncertain about where to start. **3. Packages, APIs, Datasets, and Models** The focus here shifts to data science libraries and APIs. You'll learn how to navigate through open datasets and understand machine learning models, cultivated through hands-on activities and practical examples. This module is essential as it bridges the gap between theoretical knowledge and practical application. **4. Jupyter Notebooks and JupyterLab** As data scientists often work with Jupyter Notebooks, this module introduces you to its features and capabilities. The hands-on practice with different kernels and environments will empower you to efficiently document your data experiments and findings. **5. RStudio & GitHub** This segment of the course delves into R, introducing its visualization packages and chart creation techniques. By also addressing Git and GitHub, you’ll gain crucial skills in version control—an indispensable tool in modern data science practice. You'll develop an understanding of branching, pull requests, and how to collaborate effectively on coding projects. **6. Create and Share your Jupyter Notebook** Towards the end of the course, you'll undertake a final project that consolidates your learning. This project allows you to showcase the skills you've developed in previous modules, making your newfound knowledge applicable and relevant. **Optional: IBM Watson Studio** As an added bonus, if you wish to delve deeper, the optional IBM Watson Studio module introduces a powerful platform for collaborative data science work. Through practical tasks, you will learn to connect your work to GitHub, further enhancing your coding and data management skills. ### Review and Recommendations The "Tools for Data Science" course is thoughtfully designed, enabling learners to gradually build their knowledge without feeling overwhelmed. Each module contains a well-structured series of lessons, enabling self-paced learning, making it ideal for busy professionals or students with varied backgrounds. One of the standout features of this course is its practical focus. The hands-on projects and tasks are specifically designed to ensure that theoretical knowledge is effectively translated into real-world skills. The inclusion of optional modules like IBM Watson Studio provides additional value for learners looking to broaden their experience. For anyone interested in pursuing a career in data science or wanting to enhance their data analytical capabilities, I highly recommend this course. Whether you’re a complete beginner or someone looking to update your skills, "Tools for Data Science" equips you with the necessary tools to thrive in the ever-evolving landscape of data science. Overall, this course is a must-have for aspiring data scientists aiming for a competitive edge in the job market. Enroll today and start your journey into the fascinating world of data science!

Syllabus

Overview of Data Science Tools

In this module, you will learn about the different types and categories of tools that data scientists use and popular examples of each. You will also become familiar with Open Source, Cloud-based, and Commercial options for data science tools.

For users who are just starting on their data science journey, the range of programming languages can be overwhelming. So, which language should you learn first? This module will bring awareness about the criteria that would determine which language you should learn. You will learn the benefits of Python, R, SQL, and other common languages such as Java, Scala, C++, JavaScript, and Julia. You will explore how you can use these languages in Data Science. You will also look at some sites to locate more information about the languages.

In this module, you will learn about the various libraries in data science. In addition, you will understand an API in relation to REST request and response. Further, in the module, you will explore open data sets on the Data Asset eXchange. Finally, you will learn how to use a machine learning model to solve a problem and navigate the Model Asset eXchange.

With the advancement of digital data, Jupyter Notebook allows a Data Scientist to record their data experiments and results that others can reuse. This module introduces the Jupyter Notebook and Jupyter Lab. You will learn how to work with different kernels in a Notebook session and about the basic Jupyter architecture. In addition, you will identify the tools in an Anaconda Jupyter environment. Finally, the module gives an overview of cloud based Jupyter environments and their data science features.

R is a statistical programming language and is a powerful tool for data processing and manipulation. This module will start with an introduction to R and RStudio. You will learn about the different R visualization packages and how to create visual charts using the plot function. In addition, Distributed Version Control Systems (DVCS) have become critical tools in software development and key enablers for social and collaborative coding. While there are many distributed versioning systems, Git is amongst the most popular ones. Further in the module, you will develop the essential conceptual and hands-on skills to work with Git and GitHub. You will start with an overview of Git and GitHub, followed by creation of a GitHub account and a project repository, adding files to it, and committing your changes using the web interface. Next, you will become familiar with Git workflows involving branches and pull requests (PRs) and merges. You will also complete a project at the end to apply and demonstrate your newly acquired skills.

In this module, you will work on a final project to demonstrate some of the skills learned in the course. You will also be tested on your knowledge of various components and tools in a Data Scientist's toolkit learned in the previous modules.

Watson Studio is a collaborative platform for the data science community and is used by Data Analysts, Data Scientists, Data Engineers, Developers, and Data Stewards to analyze data and construct models. In this module, you will learn about Watson Studio and IBM Cloud Pak for data as a service. Then you will create an IBM Watson Studio service and a project in Watson Studio. After creating the project, you will create a Jupyter notebook and load a data file. You will also explore the different templates and kernels in a Jupyter notebook. Finally, you will connect your Watson Studio account to GitHub and publish the notebook in GitHub. Note: This part of the course is optional and is not a mandatory requirement to complete the lab provided in this week of the course.