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html\u003e\n\u003chtml\u003e\n\u003chead\u003e\n\u003c/head\u003e\n\u003cbody\u003e\n    \u003ccenter\u003e\n        \u003cimg src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0105EN-SkillsNetwork/labs/Module2/images/SN_web_lightmode.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"\u003e\n    \u003c/center\u003e\n    \u003ch1\u003eMy Jupyter Notebook on IBM Watson Studio\u003c/h1\u003e\n    \u003ch2\u003eNafisa Lawal Idris\u003c/h2\u003e\n    \u003ch3\u003eData Scientist\u003c/h3\u003e\n    \u003cp\u003eData science fascinates me as it provides the necessary tools to uncover hidden patterns and insights from the vast amounts of information generated daily, and I find it incredibly satisfying to use this knowledge to automate tasks and solve real-world problems, while pushing the boundaries of the field.\u003c/p\u003e\n    \u003ch3\u003ePopular Data Science Languages\u003c/h3\u003e\n    \u003col\u003e\n        \u003cli\u003ePython\u003c/li\u003e\n        \u003cli\u003eR\u003c/li\u003e\n        \u003cli\u003eSQL\u003c/li\u003e\n        \u003cli\u003eJava\u003c/li\u003e\n        \u003cli\u003eJulia\u003c/li\u003e\n        \u003cli\u003eMATLAB\u003c/li\u003e\n        \u003cli\u003eScala\u003c/li\u003e\n        \u003cli\u003eSAS\u003c/li\u003e\n    \u003c/ol\u003e\n    \u003ch3\u003ePopular Data Science Libraries\u003c/h3\u003e\n    \u003col\u003e\n        \u003cli\u003eNumPy\u003c/li\u003e\n        \u003cli\u003ePandas\u003c/li\u003e\n        \u003cli\u003eMatplotlib\u003c/li\u003e\n        \u003cli\u003eScikit-learn\u003c/li\u003e\n        \u003cli\u003eTensorFlow\u003c/li\u003e\n        \u003cli\u003ePyTorch\u003c/li\u003e\n        \u003cli\u003eSciPy\u003c/li\u003e\n        \u003cli\u003eStatsmodels\u003c/li\u003e\n        \u003cli\u003eOpenCV\u003c/li\u003e\n        \u003cli\u003eXGBoost\u003c/li\u003e\n    \u003c/ol\u003e\n    \u003ch3\u003eTable of Data Science Tools\u003c/h3\u003e\n    \u003ctable\u003e\n        \u003ctr\u003e\n            \u003cth\u003eTools\u003c/th\u003e\n            \u003cth\u003eDescription\u003c/th\u003e\n        \u003c/tr\u003e\n        \u003ctr\u003e\n            \u003ctd\u003ePython\u003c/td\u003e\n            \u003ctd\u003eGeneral-purpose programming language with extensive support for scientific computing and data analysis\u003c/td\u003e\n        \u003c/tr\u003e\n        \u003ctr\u003e\n            \u003ctd\u003eR\u003c/td\u003e\n            \u003ctd\u003eStatistical programming language for data analysis and visualization\u003c/td\u003e\n        \u003c/tr\u003e\n        \u003ctr\u003e\n            \u003ctd\u003eSQL\u003c/td\u003e\n            \u003ctd\u003eQuery language used for managing and manipulating relational databases\u003c/td\u003e\n        \u003c/tr\u003e\n        \u003ctr\u003e\n            \u003ctd\u003eTableau\u003c/td\u003e\n            \u003ctd\u003eData visualization tool for creating interactive dashboards and reports\u003c/td\u003e\n        \u003c/tr\u003e\n        \u003ctr\u003e\n            \u003ctd\u003ePower BI\u003c/td\u003e\n            \u003ctd\u003eBusiness intelligence platform for creating visualizations and analyzing data\u003c/td\u003e\n        \u003c/tr\u003e\n        \u003ctr\u003e\n            \u003ctd\u003eExcel\u003c/td\u003e\n            \u003ctd\u003eSpreadsheet software with built-in data analysis tools\u003c/td\u003e\n        \u003c/tr\u003e\n        \u003ctr\u003e\n            \u003ctd\u003eJupyter Notebook\u003c/td\u003e\n            \u003ctd\u003eWeb application for creating and sharing documents containing live code, equations, visualizations, and narrative text\u003c/td\u003e\n        \u003c/tr\u003e\n        \u003ctr\u003e\n            \u003ctd\u003eApache Hadoop\u003c/td\u003e\n            \u003ctd\u003eDistributed storage and processing system for big data\u003c/td\u003e\n        \u003c/tr\u003e\n        \u003ctr\u003e\n            \u003ctd\u003eApache Spark\u003c/td\u003e\n            \u003ctd\u003eDistributed computing system for processing large-scale data sets\u003c/td\u003e\n        \u003c/tr\u003e\n        \u003ctr\u003e\n            \u003ctd\u003eDatabricks\u003c/td\u003e\n            \u003ctd\u003eCloud-based platform for processing and analyzing big data using Apache Spark\u003c/td\u003e\n        \u003c/tr\u003e\n    \u003c/table\u003e\n    \u003ch3\u003eArithmetic Expressions\u003c/h3\u003e\n    \u003cp\u003eArithmetic expressions are a fundamental part of mathematics and programming. In mathematics, arithmetic expressions are used to represent numeric calculations, such as addition, subtraction, multiplication, and division. In programming, arithmetic expressions are used to perform calculations on numerical data types, such as integers and floating-point numbers.\u003c/p\u003e\n    \u003cp\u003eExamples of arithmetic expressions:\u003c/p\u003e\n    \u003col\u003e\n        \u003cli\u003e2 + 3: addition of 2 and 3, which evaluates to 5.\u003c/li\u003e\n        \u003cli\u003e5 - 2: subtraction of 2 from 5, which evaluates to 3.\u003c/li\u003e\n        \u003cli\u003e4 * 6: multiplication of 4 and 6, which evaluates to 24.\u003c/li\u003e\n        \u003cli\u003e10 / 2: division of 10 by 2, which evaluates to 5.\u003c/li\u003e\n        \u003cli\u003e3 ** 4: exponentiation of 3 to the power of 4, which evaluates to 81.\u003c/li\u003e\n    \u003c/ol\u003e\n    \u003cp\u003eThese examples demonstrate the basic arithmetic operators in Python, which are + (addition), - (subtraction), * (multiplication), / (division), and ** (exponentiation). By combining these operators with numeric operands, we can create complex arithmetic expressions that perform various calculations.\u003c/p\u003e\n    \u003ch3\u003eMultiplies and Adds numbers\u003c/h3\u003e\n    \u003cpre\u003e\u003ccode\u003e\n        # multiply two numbers\n        a = 3\n        b = 4\n        c = a * b\n        print(c)\n            # add two numbers\n    x = 10\n    y = 7\n    z = x + y\n    print(z)\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003eConverts minutes to hours\u003c/h3\u003e\n\u003cpre\u003e\u003ccode\u003e\n    # convert minutes to hours\n    minutes = 145\n    hours = minutes / 60\n    print(hours)\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003eObjectives\u003c/h3\u003e\n\u003col\u003e\n    \u003cli\u003eTo introduce learners to the fundamentals of data science and full-stack web development.\u003c/li\u003e\n    \u003cli\u003eTo provide hands-on experience with popular data science tools and libraries, such as Python, NumPy, Pandas, and Matplotlib.\u003c/li\u003e\n    \u003cli\u003eTo teach learners how to build and deploy web applications using HTML, CSS, JavaScript, and popular frameworks such as React and Node.js.\u003c/li\u003e\n    \u003cli\u003eTo equip learners with the skills and knowledge to analyze data, build predictive models, and make data-driven decisions.\u003c/li\u003e\n    \u003cli\u003eTo help learners develop their critical thinking and problem-solving skills, as well as their ability to communicate complex technical concepts to non-technical stakeholders.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eBy the end of this program, learners would have a strong foundation in data science and full-stack web development and be able to apply these skills to real-world problems and projects.\u003c/p\u003e\n\u003ch4\u003eAuthor: Nafisa Lawal Idris\u003c/h4\u003e\n\u003ca href=\"https://github.com/elfeenah\"\u003eLink to My GitHub\u003c/a\u003e\n\u003c/body\u003e\n\u003c/html\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnafisalawalidris%2Ftools-for-data-science","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnafisalawalidris%2Ftools-for-data-science","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnafisalawalidris%2Ftools-for-data-science/lists"}