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Amani Abbas
في هذه الدورة التدريبية القائمة على المشروع والتي تستغرق ساعة ونصف تقريباً ستتعلم كيفية تحليل البيانات باستخدام لغة البرمجة بايثون. ستتعرف على مكتبات تحليل البيانات والتعامل مع الأرقام في بايثون، كما ستتعلم كيفية تنظيف البيانات وترتيبها لتهيئتها للتحليل وكيفية الحصول على إجابات للأسئلة المتعلقة بالبيانات التي لديك.
سنقوم باستخدام جوبيتر نوتبوك لكتابة الأكواد وتنفيذها والحصول على رسومات بيانية تمثل البيانات.
لا يوجد متطلبات سابقة للبدء بالمشروع حيث أن هذا المشروع هو للمبتدئين تماما في تحليل البيانات باستخدام بايثون وسيكون هناك شرح مفصل لكل خطوة من الخطوات التي ستقوم بها لإتمامه.
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What's inside
Syllabus
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what should give you pause and
possible dealbreakers
Teaches core concepts of data analysis in a project-based format
Helps learners become familiar with data analysis libraries in Python
Covers data cleaning and transformation for analysis
Uses Jupyter Notebook for coding and visualization
Provides instructors for guidance and support
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Reviews summary
مقدمة عملية لأساسيات تحليل البيانات
يقول المتعلمون إن هذه الدورة توفر مقدمة ممتازة ومبسطة لتحليل البيانات باستخدام بايثون وباندا، وهي مناسبة تمامًا للمبتدئين. يثني العديد على وضوح الشرح وسهولة المتابعة، مؤكدين أنها تساعد على بناء أساس جيد في هذا المجال. يُعتبر المشروع العملي نقطة قوة رئيسية، حيث يوفر تجربة تطبيقية مفيدة. ومع ذلك، يرى بعض الطلاب أنها قصيرة بعض الشيء وتفتقر إلى العمق في المواضيع المتقدمة، مما يعني أنها تتطلب دراسة إضافية لمن يسعون إلى الاحتراف.
تركز على التطبيق العملي من خلال مشروع يرسخ الفهم.
"استفدت كثيراً من الجزء العملي وتطبيق الباندا."
"المشروع عملي ومفيد. تعلمت أساسيات بايثون وباندا بسرعة."
"المحتوى جيد جداً للمبتدئين، جوبيتر نوتبوك كانت تجربة ممتازة."
"أساسيات ممتازة، المشروع كان جيداً للممارسة."
تقدم الدورة شرحًا سهل المتابعة ومفصلاً للمفاهيم الأساسية.
"الشرح كان واضح جداً والخطوات سهلة المتابعة."
"مقدمة رائعة لتحليل البيانات. شرح مبسط ومفهوم."
"دورة مكثفة ومفيدة للغاية. المحاضر شرح كل شيء بوضوح."
"رائعة للمبتدئين. كل خطوة مشروحة بالتفصيل وهذا ما أحتاجه."
توفر نقطة انطلاق قوية للمبتدئين بلا متطلبات مسبقة.
"دورة ممتازة للمبتدئين! أنصح بها كل من يريد أساسيات تحليل البيانات."
"لم أكن أعرف شيئاً عن تحليل البيانات والآن أشعر أن لدي أساس جيد."
"أفضل دورة بدأت بها لتعلم البايثون وتحليل البيانات."
"مقدمة جيدة، تحتاج لمزيد من التمارين العملية."
مقدمة سريعة تحتاج إلى دورات إضافية للوصول للاحتراف.
"أساسيات ممتازة، ولكنها قصيرة جداً."
"لكي تصبح محترفاً تحتاج لدورات أخرى بعدها."
"استفدت من الدورة كبداية، لكنها لا تغطي كل شيء."
"لابد من البحث عن مصادر إضافية بعد الانتهاء منها."
قد تكون سطحية أو قصيرة لمن يبحث عن تعمق أكبر.
"جيدة للمبتدئين ولكنها سطحية جداً."
"تمنيت لو كان هناك المزيد من التحديات أو البيانات الأكبر للتعامل معها."
"تحتاج لمزيد من العمق لمن لديهم خلفية بسيطة."
"أتمنى أن يتم تحديث المحتوى بإضافة مواضيع متقدمة قليلاً."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in أساسيات تحليل البيانات باستخدام بايثون وباندا with these
activities:
Review Python programming basics
Show steps
Start by refreshing your understanding of Python basics to enhance your ability to follow the course material.
Browse courses on
Python Basics
Show steps
-
Revisit introductory Python tutorials
-
Practice writing simple Python scripts
Follow external NumPy tutorials
Show steps
Supplement course material by exploring NumPy tutorials to deepen your understanding of numerical computing in Python.
Browse courses on
NumPy
Show steps
-
Identify reputable NumPy tutorials online
-
Follow the tutorials and complete the exercises
Assist peers in the course forums
Show steps
Enhance your understanding by helping others in the course forums, solidifying your knowledge and fostering a supportive learning environment.
Show steps
-
Actively participate in the course forums
-
Provide assistance and guidance to peers
One other activity
Expand to see all activities and additional details
Show all four activities
Develop a Python project
Show steps
Apply your Python knowledge to build a project, solidifying your understanding and showcasing your skills.
Show steps
-
Identify a problem or idea for your project
-
Design and plan your project architecture
-
Implement the project using Python
-
Test and debug your project
Review Python programming basics
Show steps
Start by refreshing your understanding of Python basics to enhance your ability to follow the course material.
Browse courses on
Python Basics
Show steps
Show steps
Show steps
- Revisit introductory Python tutorials
- Practice writing simple Python scripts
Follow external NumPy tutorials
Show steps
Supplement course material by exploring NumPy tutorials to deepen your understanding of numerical computing in Python.
Browse courses on
NumPy
Show steps
Show steps
Show steps
- Identify reputable NumPy tutorials online
- Follow the tutorials and complete the exercises
Assist peers in the course forums
Show steps
Enhance your understanding by helping others in the course forums, solidifying your knowledge and fostering a supportive learning environment.
Show steps
Show steps
Show steps
- Actively participate in the course forums
- Provide assistance and guidance to peers
One other activity
Expand to see all activities and additional details
Show all four activities
Develop a Python project
Show steps
Apply your Python knowledge to build a project, solidifying your understanding and showcasing your skills.
Show steps
Show steps
Show steps
- Identify a problem or idea for your project
- Design and plan your project architecture
- Implement the project using Python
- Test and debug your project
Career center
Learners who complete أساسيات تحليل البيانات باستخدام بايثون وباندا will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data scientists commonly use Python for data analysis and machine learning. This course may be beneficial for aspiring data scientists, as it covers data cleaning, manipulation, and visualization techniques, which are essential for data science projects. The course may also provide a foundation for understanding numerical operations in Python, which is important for data analysis and machine learning.
Quantitative Analyst
Quantitative Analyst
Quantitative analysts heavily rely on data analysis skills to develop and implement mathematical and statistical models. This course may provide a foundation for understanding data analysis concepts and techniques, which can be essential for quantitative analysts. The course may also help build a foundation for numerical operations in Python, which is important for developing and implementing mathematical and statistical models.
Market Researcher
Market Researcher
Market researchers often use data analysis techniques to gather and interpret data about consumer behavior and market trends. This course may provide a foundation for understanding data analysis concepts and techniques, such as data cleaning, manipulation, and visualization. These skills can be valuable for market researchers in conducting market research studies and providing insights to clients.
Financial Analyst
Financial Analyst
Financial analysts often use data analysis skills to analyze financial data and make investment recommendations. This course may provide a foundation for understanding data analysis concepts and techniques, such as data cleaning, manipulation, and visualization. These skills can be valuable for financial analysts in analyzing financial data and making informed investment decisions.
Operations Research Analyst
Operations Research Analyst
Operations research analysts utilize data analysis techniques to improve operational efficiency and decision-making. This course may provide a foundation for understanding data analysis concepts and techniques, such as data cleaning, manipulation, and visualization. These skills can be valuable for operations research analysts in analyzing data and developing solutions to improve operational efficiency.
Data Engineer
Data Engineer
Data engineers design, build, and maintain data systems and infrastructure. This course may provide a foundation for understanding data analysis concepts and techniques, such as data cleaning, manipulation, and visualization. These skills can be valuable for data engineers in designing and building data systems and infrastructure that can handle large volumes of data.
Data Analyst
Data Analyst
Data analysts may utilize their Python and data analysis skills to collect, clean, and analyze large datasets. This course may provide a foundation for understanding the fundamentals of data analysis, such as data cleaning and manipulation, numerical operations, and data visualization. These skills can be essential for success in this role.
Business Analyst
Business Analyst
Business analysts often require data analysis skills to identify trends and patterns in business data. This course may provide a foundation for understanding data analysis concepts and techniques, such as data cleaning, manipulation, and visualization. These skills can be valuable for business analysts in making data-driven decisions and providing insights to stakeholders.
Software Engineer
Software Engineer
Software engineers may utilize data analysis techniques to develop and implement data-driven features in software applications. This course may provide a foundation for understanding data analysis concepts and techniques, such as data cleaning, manipulation, and visualization. These skills can be valuable for software engineers in developing and implementing software applications that can effectively leverage data.
Product Manager
Product Manager
Product managers often require data analysis skills to understand user behavior and make data-driven product decisions. This course may provide a foundation for understanding data analysis concepts and techniques, such as data cleaning, manipulation, and visualization. These skills can be valuable for product managers in analyzing data and making informed product decisions.
Project Manager
Project Manager
Project managers may utilize data analysis techniques to track project progress and identify potential risks. This course may provide a foundation for understanding data analysis concepts and techniques, such as data cleaning, manipulation, and visualization. These skills can be valuable for project managers in analyzing data and making informed project decisions.
Marketing Manager
Marketing Manager
Marketing managers often require data analysis skills to understand customer behavior and make data-driven marketing decisions. This course may provide a foundation for understanding data analysis concepts and techniques, such as data cleaning, manipulation, and visualization. These skills can be valuable for marketing managers in analyzing data and making informed marketing decisions.
Sales Manager
Sales Manager
Sales managers may utilize data analysis techniques to identify sales trends and opportunities. This course may provide a foundation for understanding data analysis concepts and techniques, such as data cleaning, manipulation, and visualization. These skills can be valuable for sales managers in analyzing data and making informed sales decisions.
Account Manager
Account Manager
Account managers may utilize data analysis techniques to track customer activity and identify opportunities for growth. This course may provide a foundation for understanding data analysis concepts and techniques, such as data cleaning, manipulation, and visualization. These skills can be valuable for account managers in analyzing data and making informed decisions about customer relationships.
Customer Success Manager
Customer Success Manager
Customer success managers may utilize data analysis techniques to track customer satisfaction and identify opportunities for improvement. This course may provide a foundation for understanding data analysis concepts and techniques, such as data cleaning, manipulation, and visualization. These skills can be valuable for customer success managers in analyzing data and making informed decisions about customer relationships.
For more career information including salaries, visit:
OpenCourser.com/course/d9lev9/'ssyt
Reading list
We've selected 12 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
أساسيات تحليل البيانات باستخدام بايثون وباندا.
Classic in the field of machine learning. It provides a comprehensive overview of statistical learning methods, from linear regression to support vector machines. While it more advanced book, it valuable resource for those interested in gaining a deeper understanding of machine learning.
Provides a comprehensive overview of Python data analysis libraries, including NumPy, Pandas, Matplotlib, and Seaborn. It covers data cleaning, manipulation, visualization, and modeling, making it a great resource for those interested in learning more about Python for data analysis.
Offers a comprehensive introduction to machine learning and pattern recognition. It covers a wide range of topics, from supervised learning to unsupervised learning, making it a great resource for those interested in gaining a deeper understanding of machine learning.
Provides a comprehensive introduction to data science using Python and its popular libraries. It covers data cleaning, manipulation, visualization, and modeling, making it a great resource for those interested in learning more about data science.
Practical guide to data science using Python. It covers a wide range of topics, from data cleaning to model evaluation, making it a great resource for those interested in learning more about data science using Python.
Offers a practical introduction to machine learning using Python. It covers a wide range of topics, from supervised learning to unsupervised learning, making it a great resource for those interested in learning more about machine learning.
Offers a practical introduction to data science, covering data cleaning, manipulation, modeling, and visualization. It uses Python and popular Python libraries, making it accessible to those with limited programming experience.
Provides a comprehensive guide to machine learning using Python and its popular libraries, including Scikit-Learn, Keras, and TensorFlow. It covers a wide range of topics, from data preprocessing to model evaluation.
Provides a comprehensive introduction to computer vision using Python. It covers a wide range of topics, from image processing to object recognition, making it a great resource for those interested in learning more about computer vision.
Provides a comprehensive introduction to deep learning using Python. It covers a wide range of topics, from neural networks to convolutional neural networks, making it a great resource for those interested in learning more about deep learning.
Offers a practical introduction to natural language processing using Python. It covers a wide range of topics, from text classification to machine translation, making it a great resource for those interested in learning more about natural language processing.
Offers a practical introduction to Python programming, covering basic syntax, data structures, and functions. It great resource for those with no prior programming experience who want to learn the fundamentals of Python.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/d9lev9/'ssyt
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