Data science tool Training

Live Online & Classroom Enterprise Training

Data science tools are software and frameworks used for data analysis, visualization, and machine learning. They include tools like Python, R, Jupyter, TensorFlow, and Tableau to support data-driven decision-making.

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What is Data science tool Training about?

This course introduces the most widely used tools and environments in data science. Learners will explore platforms and software for data cleaning, visualization, analysis, modeling, and collaboration, helping them build a solid foundation for real-world data science workflows.

What are the objectives of Data science tool Training ?

Explore key tools used in data science like Jupyter, RStudio, VS Code, and Git
Learn to use platforms like IBM Watson Studio, Google Colab, and Anaconda
Practice using SQL, Python, and data visualization libraries
Understand the role of version control, collaboration tools, and cloud notebooks

Who is Data science tool Training for?

Aspiring data scientists and analysts
Students in data science, ML, or statistics programs
Software engineers exploring data workflows
Anyone starting with data science toolchains

What are the prerequisites for Data science tool Training?

Prerequisites:

Basic understanding of statistics and data analysis
Familiarity with Python or R programming
Awareness of data structures (CSV, JSON, SQL)
No prior tool-specific experience required

Learning Path:

Introduction to data science workflows
Hands-on with tools: Jupyter, RStudio, Git, SQL
Explore cloud platforms: Colab, Watson Studio
Set up environments using Anaconda or Docker

Related Courses:

IBM Data Science Tools
Python for Data Science Toolbox
Git & GitHub for Data Science
Intro to Jupyter Notebooks

Available Training Modes

Live Online Training

2 Days

Course Outline Expand All

Expand All

Module 1 – Overview of Data Science Tools

Categories of Data Science Tools

Comercial tools for data science

Cloud based tools for data science

Module 2 - Languages of Data Science

Introduction to languages of data science

Module 3 – Packages, APIs, Datasets and Models

Introduction to packages, APIs, Data Sets and Models

Hands on lab

Module 4 – Jupyter Notebook and JupyterLab

Introduction to Jupyter and JupyterLab

Hands on lab

Module 5 - RStudio and GitHub

RStudio IDE

Github

Hands on lab

Module 6 - Watson Studio and IBM Tools

Watson Studio

Other IBM tools

Hands on lab

Who is the instructor for this training?

The trainer for this Data science tool Training has extensive experience in this domain, including years of experience training & mentoring professionals.

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Sony

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