Learn Data Science Fundamentals with Python

Python has emerged as the go-to programming language for data scientists due to its versatility and powerful libraries tailored for data analysis, visualization, and machine learning. With its intuitive syntax and extensive ecosystem, Python enables data scientists to explore and manipulate large datasets efficiently. Libraries such as NumPy, Pandas, and Matplotlib provide essential tools for data manipulation, analysis, and visualization, while frameworks like scikit-learn and TensorFlow facilitate machine learning and predictive modeling. Python's readability and simplicity empower data scientists to focus on extracting insights from data rather than grappling with complex syntax, making it an indispensable tool for modern data-driven organizations.

Curriculum Content

Data Science Fundamentals with Python

This course is designed to provide aspiring data scientists with a solid foundation in Python programming tailored for data analysis, manipulation, feature engineering, predictive modeling and machine learning. Students will build on fundamental concepts in Python with a focus on their application in data science contexts. They will then delve into more advanced topics including working with libraries like NumPy, Pandas, Folium, Matplotlib, and scikit-learn empowering students to efficiently clean, explore, analyze, and visualize datasets. By the end of this course, students will have the skills and knowledge needed to leverage Python effectively in data science projects, from data cleaning and preprocessing to statistical exploratory data analysis and model building. Quizzes and hands-on projects will reinforce learning and provide practical experience in applying Python to real-world data science tasks..

7.5 hour

1 project

Expert tutors

Samuel Taiwo

Samuel Taiwo

MLOps Engineer, UNICCON Group

Organizations where some of our students come from

/static/media/cu.9b013cf236264c32c994.png
/static/media/lbschool.097d77e16cc33d3b5f1e.png
/static/media/egbin-power.af5e008af772f9565023.png
/static/media/m-kopa.7b2b8a0d882f84b6d223.png
/static/media/shell.23463a783a2912e9e382.png
data:image/png;base64,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
/static/media/trade-depot.b963d0ac0cbf3f682df8.png
/static/media/uofibadan.e72d2718d02929786cde.png
/static/media/dangote.2b0773bb79fcfa46c9a7.jpg
data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAADICAIAAAAiOjnJAAALm0lEQVR42u3dfVAT6QHH8WxIgAgYMF5LwVOrJ75QtaIVpweEBAXDy8SgVA/Uob1SEdHztIxj5cr5EgJVRAYCN1pfqj3Pzmgt4niHJBwVfBtH/1D/YnDUyjgqjgiiEIaRbrKAUbHNyz6bhP19Z4fhEHN74XPPPvuw2QgooUhAiQRCL4oyb0LLZv5cOLgxXxEOfp168/V3/+jtv8L80VvfL8Tjj/DHH9zEAooSChBiNYoeqmhfeCIQYCHAQoCFEGAhwEKAhRBgIcBCgIUQYCHAQoCFEGAhwEKAhRBgIcBCgIUQYCHAQoCFEGAhwEKAhRBgIcBCgIUQYCHAQoCFEGAhwEKAhRBgIcBCgIUQYCHAQoCFEGAhwBIIampqvnNdhw4dKi0tzc/Pz8rKSkxMDA8P9/b2Bo6RAKvfLWtra6utrd2+fXtaWtqCBQuCgoIgBrCc7fXr18N+/fDhw1OmTJFKpUIh3oMIsNiup6fn+fPn+/fvH3weKWACLJZrbm4uLy8fM2YMPAEWkc6dOxcTEwNVgEVkWvbq1Sv6vNLLywuwAIv9Ojo6Zs6cCViARaSmpiaZTAZYgEWk7Oxsvp05AhZH3b9/n1ezLsDidGofFxcHWIBFpKqqKsACLCIdPXp0xE+5AMs1x8SrV68CFmARqaWlBbAAi0iXLl0CLMAickzU6/WABVhEKigoACzAIlJwcDBgARb7B0STyTTC1uUBy106derUSFrcAiw3SqVSYcQCLCIBlmfA+tArbdy2kpISwPKMEWvTpk0ajSZ1uNLS0latWrVx48bi4uLjx4+fP3++paXFtbP4np4ewPIMWBEREfbuTGxs7LVr17q6ulzC6+TJk4A1MmExjRo1Kioqqru7m2NY9L/R398fsEYsrKGWL1/Osa3169cD1siHxVRbW8vx6aFHr2kBlh3p9XrOYCUnJ2PE4gssuqysLG5gnT17FrB4BIsuPz+fG1se/dtDwHLgKaNu3brFweprXl4eYPEIFhMHa/oGgwGweAeroKCANKyuri7PPTcELMd78uQJaVsTJ07EiMU7WBqNhvRM68CBA4DFO1gikYj0iNXW1gZYvINFV1ZWRhTWixcvJBIJYPEOlkwmI3oo7Ovr89BbmwKWU/n4+HR0dBAdtCZPngxYvINFd/v2bSyTAhb7ZWRkEL2mtK6uDrD4CEtA/pUggMVTWO3t7YAFWOx348YNorCkUilg8RFWdXU1UViTJk0CLD7COnLkCFFYc+bMASw+wiJ96Z9cLgcsPsJKT08nCisxMRGw+AhLoVCQW8eiP6ampgIWH2GFh4cTHbFWrFgBWHyENW7cOMACLPYLDAwELMBiP39/f6KXkgIWYAEWYOFQCFhuDis4OJgorGXLlgEWH2GFhYURheWJNwgBLBaKjIwkCkupVAIWH2Gp1WqisObPnw9YfIS1bt06orCmTZsGWHyEVVpaShSWJ77TDmCx0IkTJ4jCEovFgMVHWAaDAde8Axb7NTc3k9v/Bw8eABZPYfX29pLb/2PHjgEWH2EFBAQQPQ6uXLkSsPgIa/PmzTglBCz2a21tJbfzJpMpMDAQsHgHy9fXt7Ozk9zO0w9O/ysAi3ewpFIp0ftjPXv2TOCZAZZTVVRUEJ1gVVVVARa/YDF3ye4nXFhYGGDxDlZKSgpRVR79bquA5aYL7nSNjY32/jQHfqbmj0LK1jcfMH8j6+9TAFgORvoaLLrS0lJn9lAi9Q6PDE3Mmrm+XKE7l6S/unz/9c8qLqcVVifl7FUqV08Ni/iZ2E805HE4o4DFISzm/23SwxVdaGionTsmpCgvgVDgO1a0riSuonGpzhBXaIjVGmPpjwObUc58ojPEFhkWlTcuS//Tr30DReYxix7kzP9pQoxYLhuxmHfEJLrQcOfOHQeOgrSKgm+XFNbFFxoVunrGkPyNKstWXKcsGvxcVx+jrVdqaxM2lqveHqkwYnEFa2gSQvre7kw5OTk2ehraMUX6jD31KcxoNMhoWFuKN98wOJjpjMqiH5Lmxv988GEpJ4+JgGWfLYlE0s9Jtr89E30EpD/+dkdMyQXVO4OTvdvexuTE38/GodAFh8KHDx++tkRU1b1792zfJfqn9+dvNTqDsrBeUWiMNm9O2CoyxmV+HW09TlGARRrW48ePuRmuJkyYYPvMapM+3qzKoHByuLKytTDzq5jBswEhRiyCsKKjo00mUz/5t+vtt7z/pUgk+v8DFbNjy6YW18cPTZXeOgF0ZFOaH8FIT7niJ8/7CHMsgrCkUunevXv7OWzNmjU2ngQGfexXeeU3DAi2hquh7ZvLn/kGiB1eNwWsD+bv75+bm8vNKDXUo0ePbF9b+PKbOC0Lo9QHt6V/nIvJ+webO9fuZ2f27NnXr1/v7u7u57wtW7bYgooeSCbM+unufyfqjApCqrRGRVmTeszHUstRVwhY72bL27LNmDEjMzPz4MGDTU1NL1++7HdRra2ttpwFmn9sXoKv/5HCLEcVkYIlLzQqMgs+tTABrOEOLv95L/qL7e3tXB7jbCkqKsqWOQ0loHz9xYXfm+fsWvMiZ4x5Y92WUaEzyndWq3Ao9Oxu3rwpsPV6BOFMZajOGEfoIGi9lVxICpke6MAUHrBcHz1w9vT0jB492hZYzDd8UalgceHqf61p1cet+ioSI5anplar7Xq6qi5nkCalo+FaLoXQnV7qwGIWYLm+mpoau54rSaBPWZOGNCztwPmmvLA6xcsbk3dPy4Hrj2WhgSUNKRwcBwfOCaqTxBIRYHlYoaGhlCXbn6ufTJDt/jGJvKpo5sLAHdWJ3qPEgOVJRUY6Mi8eOy5oz4/JXIxYlmX9XWeSvDFieVC5ubkO/q5pjKT0gpobWPQca1d1spePF2B5Rtu2bXPs97vM36okf1bIXIlqvjT+X6kOXAcPWC7I+TsTbfnbIqtLkJXk5lg6ozJLF4V1LA8oLS3NeuxxrEj1J9Yr71qjksTsij4OllxQTflViAAr7+68vN7b25uUlOT8c0WL9PX3LjyfoDPKSU7b5dr62KIfkh3dScDipL6+PolE4jyqgasbxIKd/1xivsh9wEEciRGLtvWH4mjAct/OnDnD9kvYBbOUE/c0LB4YtNi6usHqhRg6o2LfxZTxv/gIsNw05vDHOiy6rccStOweDa2A0iPW58VR1heBAZa71NDQ4OfnR4IUc1ScOj+krEnN5ohl9QLXskaNLCSA2XkKv4R2k54+fTp9+nQOnrfktbN07C83KIoMiyJU4507wwAsttNoNNy8SQkznOw8mcritVk6y9rVZv1i82sK7fwlJmARyWQyrV69WigUcv3ciQW7Tmt0zGXKBuaGH4php022XINFP86X+kVsoAcsp2tsbHTFbf6pwR+hmXLeXxP+0rCQnh5p7b1G1Orz3Q0Ja3crBG/dFASwOF+X6ujo0Ov1AnfIwmDltk+L6hYPXPxpsOm10cVD98oyxurOqxZ/Ppu9wzRg2X9pXk5OjlQqde27vb13nyHznRaCQvz2GdOLjQuLbJ516erlRYaFO05qfALElAA3XuO2ixcv5uXlxcfHy2Qygds3Izp0y6GE8ktLdEaF1iC3vJBLwdwNS2uemw+sLBTXL6y4lLpuX+wn84Lf4wpYrNbZ2Xn37t0rV66cPn26srIyPT09JCRE4JnJxgek5syj3Wz9bpGuNmHfRXXZZXVpY4ru+4Stf09Yu0ee9Ltf+sl8iJ2xuges/Pz8DRs2fMFh2dnZGRkZKpUqIiJi7NixNp7be0TMKoF5Uk+5cB/cAxYaYQHWyPyxvvcVoQ3fA1gIIxYCLIQACwEWAiyEAAsBFgIshAALARYCLIQACwEWAiyEAAsBFgIshAALARYCLIQACwEWAiyEAAsBFgIshAALARYCLIQACwEWAiyEAAsBFgIshAALuQssCs8GYkGUFSzKy6bv/cA/sv5HePwR8PhmWAKhiLaFDRsrm9CyUULxfwGoT/lLscYfjgAAAABJRU5ErkJggg==

Want to skill up?

Join our learners to gain in-demand skills

FAQs

Frequently asked questions

  • Are the courses online?

    Yes the courses are hosted and administered online which gives you the ability to learn at your own time, and pace. It includes both both self-paced components and live tutoring.

  • What can I do with Python Data Science skills?

    With Python Data Science skills, you can pursue various rewarding career paths. As a Data Analyst, you'll analyze data and derive insights for informed decision-making. Data Scientists use Python to build predictive models and extract valuable insights from data. Machine Learning Engineers develop algorithms and solutions for tasks like image recognition and natural language processing.

    AI Researchers leverage Python to advance the field of machine learning and deep learning. Data Engineers manage and process large datasets, while Business Intelligence Analysts create visualizations for strategic decision-making. Freelance opportunities also abound for consultants offering Python Data Science expertise.

    In essence, Python Data Science skills open doors to impactful roles across industries, making you a sought-after professional in today's data-driven landscape. Average salary for Python developers in the US is $96,000.

  • What do I need to get started?

    There is little or no prerequisite knowledge required to get started. However, you require a laptop and access to stable internet to get the best out of the learning contents. You should also be comfortable with some maths.

resa logo

Empowering individuals and businesses with the tools to harness data, drive innovation, and achieve excellence in a digital world.

Copyright 2025Resagratia. All Rights Reserved.