This repository contains my work for the "Tools for Data Science" course offered by IBM. In this course, I completed various exercises and practices to enhance my understanding of data science tools and techniques. The course provides practical exercises and hands-on experiences to enhance our skills in working with data, data analysis, and more.
I completed the practice notebook in both online and local environments:
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Online Platform: I worked on the provided platform at IBM's JupyterLite Platform. This online environment allowed me to interact with the notebook directly in the browser.
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Local Development: Additionally, I worked on the same notebook using Visual Studio Code (VSCode). Working locally allowed me to customize my environment and leverage additional tools for coding.
In this notebook, you can find Data Science Tools and Ecosystems.
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Python: Python is one of the most widely used languages in data science due to its rich ecosystem of libraries such as NumPy, pandas, matplotlib, and scikit-learn. It's known for its readability and versatility, making it a great choice for various data manipulation, analysis, and machine learning tasks.
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R: R is another popular language in the data science community, especially for statistical analysis and data visualization. It has a comprehensive collection of packages like ggplot2 and dplyr that are designed specifically for data analysis and visualization.
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SQL: Structured Query Language (SQL) is essential for working with databases. Data scientists often use SQL to query and manipulate data stored in relational databases. It's a fundamental skill for retrieving and transforming data before analysis.
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Julia: Julia is a newer language gaining traction in the data science field due to its high-performance capabilities. It's designed for numerical and scientific computing, making it suitable for tasks that require heavy computational resources.
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Scala: Scala, often used with Apache Spark, is known for its concurrency and distributed computing capabilities. It's a good choice for large-scale data processing and analysis.
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SAS: SAS (Statistical Analysis System) is widely used in industries like healthcare and finance for data analysis and reporting. It provides various statistical and data manipulation tools.
These languages offer a variety of tools and libraries that cater to different aspects of data science, from data cleaning and preprocessing to advanced machine learning modeling.
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NumPy: is a fundamental library for numerical computations in Python. It provides support for multi-dimensional arrays and matrices, along with a wide range of mathematical functions. Data scientists use NumPy for efficient data manipulation and numerical operations.
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pandas: is a powerful library for data manipulation and analysis. It offers data structures like DataFrames and Series, making it easy to handle and analyze structured data. It's commonly used for data cleaning, transformation, and exploration.
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matplotlib: is a popular library for creating static, interactive, and animated visualizations in Python. It provides a wide range of plotting options, making it an essential tool for data visualization.
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scikit-learn: is a machine learning library that provides simple and efficient tools for data mining and data analysis. It includes a wide variety of machine learning algorithms, preprocessing techniques, and model evaluation tools.
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TensorFlow and PyTorch: are deep learning frameworks that enable the creation and training of complex neural network models. They're widely used for tasks like image recognition, natural language processing, and other deep learning applications.
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Seaborn: is a statistical data visualization library built on top of matplotlib. It provides a high-level interface for creating attractive and informative statistical graphics.
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StatsModels: is a library used for estimating and interpreting statistical models. It's particularly useful for performing various statistical analyses and hypothesis testing.
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NLTK (Natural Language Toolkit): is a library for working with human language data. It's commonly used for tasks like text preprocessing, sentiment analysis, and language modeling.
These libraries form the core of the data science ecosystem and offer a wide range of tools for various aspects of data manipulation, analysis, visualization, and machine learning.
| Data Science Development Environment |
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| Jupyter Notebook |
| RStudio |
| Visual Studio Code |
- List popular languages for Data Science.
- Introduce commonly used data science libraries.
- Understand data science development environment tools.
- Evaluate arithmetic expressions in Python.
- Convert minutes to hours using basic operations.
Shima Naderi