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Rav Ahuja
and
Alex Aklson
فن الكشف عن الرؤى والاتجاهات في البيانات موجود منذ القِدم. استخدم قدماء المصريين بيانات الإحصاء لزيادة الكفاءة في عملية تحصيل الضرائب وكانوا يتنبؤون بفيضان نهر النيل كل عام بدقة. ومنذ ذلك الحين، اختط العاملون في علم البيانات مجالاً فريدًا ومتميزًا للعمل الذي...
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فن الكشف عن الرؤى والاتجاهات في البيانات موجود منذ القِدم. استخدم قدماء المصريين بيانات الإحصاء لزيادة الكفاءة في عملية تحصيل الضرائب وكانوا يتنبؤون بفيضان نهر النيل كل عام بدقة. ومنذ ذلك الحين، اختط العاملون في علم البيانات مجالاً فريدًا ومتميزًا للعمل الذي...
Read more
فن الكشف عن الرؤى والاتجاهات في البيانات موجود منذ القِدم. استخدم قدماء المصريين بيانات الإحصاء لزيادة الكفاءة في عملية تحصيل الضرائب وكانوا يتنبؤون بفيضان نهر النيل كل عام بدقة. ومنذ ذلك الحين، اختط العاملون في علم البيانات مجالاً فريدًا ومتميزًا للعمل الذي يؤدونه. هذا المجال هو علم البيانات. في هذه الدورة، سنلتقي بعض الممارسين في مجال علم البيانات وسنحصل على لمحة عامة حول ماهية علم البيانات اليوم.
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Taught by seasoned professionals Alex Aklson and Rav Ahuja, who have extensive experience in the field of data science
Covers a wide range of topics in data science, including data mining, machine learning, and statistical modeling
Emphasizes hands-on learning through interactive exercises and case studies
Suitable for individuals with both technical and non-technical backgrounds
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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 ما علم البيانات؟ with these
activities:
Review Linear Algebra Concepts
Show steps
Refresh your understanding of linear algebra concepts, which are fundamental to many machine learning algorithms, to ensure a strong foundation for the course material.
Browse courses on
Linear Algebra
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-
Review notes or textbooks from previous courses or online resources.
-
Practice solving linear algebra problems and exercises.
-
Seek additional support if needed through online tutorials or forums.
Organize and Review Course Materials
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Enhance your learning by organizing and reviewing course materials, including notes, assignments, and quizzes, to reinforce your understanding and retention.
Browse courses on
Study Habits
Show steps
-
Organize notes and materials into a structured format.
-
Review and summarize key concepts and information.
-
Create flashcards or study guides for efficient memorization.
Connect with Experts in the Field
Show steps
Seek guidance and mentorship from experienced professionals in the field of data science to enhance your learning and gain insights beyond the classroom.
Show steps
-
Identify experts in the field through online platforms, conferences, or industry events.
-
Reach out to potential mentors and express your interest in their guidance.
-
Meet with mentors to discuss your goals, ask questions, and receive feedback.
Five other activities
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Show all eight activities
Read 'Data Mining: Practical Machine Learning Tools and Techniques'
Show steps
Reinforce your understanding of machine learning concepts and techniques by reading this comprehensive book, providing a deeper dive into the material covered in the course.
View
Data Mining: Practical Machine Learning Tools...
on Amazon
Show steps
-
Read selected chapters or sections of the book.
-
Summarize key concepts and techniques.
-
Apply the knowledge gained to your own projects or assignments.
Practice with Data Visualization Libraries
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Practice working with data visualization libraries to solidify your understanding of data visualization techniques and build proficiency in using these tools.
Browse courses on
Data Visualization
Show steps
-
Choose a data visualization library (e.g., Python's matplotlib or seaborn).
-
Find a dataset to work with.
-
Visualize the data using the chosen library.
-
Repeat the process with different datasets and visualization types.
Join a Study Group
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Collaborate with peers to discuss course material, solve problems, and reinforce your understanding through peer-to-peer learning.
Show steps
-
Find or create a study group with other students taking the course.
-
Meet regularly to discuss assignments, concepts, and projects.
-
Share resources, notes, and insights with each other.
Explore Advanced Machine Learning Algorithms
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Seek out and follow guided tutorials to learn about advanced machine learning algorithms and techniques beyond the scope of the course.
Browse courses on
Machine Learning Algorithms
Show steps
-
Identify advanced machine learning algorithms (e.g., deep learning, ensemble methods).
-
Find online tutorials or courses covering these algorithms.
-
Follow the tutorials and implement the algorithms in a programming language.
-
Experiment with different algorithms and datasets.
Build a Machine Learning Model for a Real-World Problem
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Apply your knowledge by building a machine learning model to solve a real-world problem. This will provide hands-on experience and demonstrate your proficiency.
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Machine Learning Applications
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-
Define a real-world problem that can be solved with machine learning.
-
Gather and prepare the necessary data.
-
Choose and train a machine learning model.
-
Evaluate the model's performance.
-
Deploy the model and monitor its performance.
Review Linear Algebra Concepts
Show steps
Refresh your understanding of linear algebra concepts, which are fundamental to many machine learning algorithms, to ensure a strong foundation for the course material.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review notes or textbooks from previous courses or online resources.
- Practice solving linear algebra problems and exercises.
- Seek additional support if needed through online tutorials or forums.
Organize and Review Course Materials
Show steps
Enhance your learning by organizing and reviewing course materials, including notes, assignments, and quizzes, to reinforce your understanding and retention.
Browse courses on
Study Habits
Show steps
Show steps
Show steps
- Organize notes and materials into a structured format.
- Review and summarize key concepts and information.
- Create flashcards or study guides for efficient memorization.
Connect with Experts in the Field
Show steps
Seek guidance and mentorship from experienced professionals in the field of data science to enhance your learning and gain insights beyond the classroom.
Show steps
Show steps
Show steps
- Identify experts in the field through online platforms, conferences, or industry events.
- Reach out to potential mentors and express your interest in their guidance.
- Meet with mentors to discuss your goals, ask questions, and receive feedback.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Read 'Data Mining: Practical Machine Learning Tools and Techniques'
Show steps
Reinforce your understanding of machine learning concepts and techniques by reading this comprehensive book, providing a deeper dive into the material covered in the course.
View
Data Mining: Practical Machine Learning Tools...
on Amazon
Show steps
Show steps
Show steps
- Read selected chapters or sections of the book.
- Summarize key concepts and techniques.
- Apply the knowledge gained to your own projects or assignments.
Practice with Data Visualization Libraries
Show steps
Practice working with data visualization libraries to solidify your understanding of data visualization techniques and build proficiency in using these tools.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a data visualization library (e.g., Python's matplotlib or seaborn).
- Find a dataset to work with.
- Visualize the data using the chosen library.
- Repeat the process with different datasets and visualization types.
Join a Study Group
Show steps
Collaborate with peers to discuss course material, solve problems, and reinforce your understanding through peer-to-peer learning.
Show steps
Show steps
Show steps
- Find or create a study group with other students taking the course.
- Meet regularly to discuss assignments, concepts, and projects.
- Share resources, notes, and insights with each other.
Explore Advanced Machine Learning Algorithms
Show steps
Seek out and follow guided tutorials to learn about advanced machine learning algorithms and techniques beyond the scope of the course.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Identify advanced machine learning algorithms (e.g., deep learning, ensemble methods).
- Find online tutorials or courses covering these algorithms.
- Follow the tutorials and implement the algorithms in a programming language.
- Experiment with different algorithms and datasets.
Build a Machine Learning Model for a Real-World Problem
Show steps
Apply your knowledge by building a machine learning model to solve a real-world problem. This will provide hands-on experience and demonstrate your proficiency.
Browse courses on
Machine Learning Applications
Show steps
Show steps
Show steps
- Define a real-world problem that can be solved with machine learning.
- Gather and prepare the necessary data.
- Choose and train a machine learning model.
- Evaluate the model's performance.
- Deploy the model and monitor its performance.
Career center
Learners who complete ما علم البيانات؟ will develop knowledge and skills
that may be useful to these careers:
Statistician
Statistician
A Statistician collects, analyzes, interprets, and presents data. They work in various industries, including finance, healthcare, and government. This course may be useful for aspiring Statisticians by offering foundational knowledge in data analysis and visualization.
Data Scientist
Data Scientist
A Data Scientist cleans, analyzes, and interprets datasets using a variety of statistical and machine learning techniques. Businesses use discoveries made by Data Scientists to develop new products and streamline processes. This online course may be useful for prospective Data Scientists, providing foundational knowledge in data analysis, visualization, and interpretation.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build, deploy, and maintain machine learning and deep learning models. This course may be helpful for aspiring Machine Learning Engineers by teaching core data science concepts that underlie machine learning technologies.
Data Analyst
Data Analyst
A Data Analyst extracts meaningful insights from raw data. The insights provided by a Data Analyst can help organizations make better decisions. This course may be useful for aspiring Data Analysts, introducing them to the history of data analysis, as well as modern methods of extracting insights.
Risk Analyst
Risk Analyst
Risk Analysts identify, assess, and manage risks. They work in various industries, including finance, insurance, and healthcare. This course may be useful for aspiring Risk Analysts by teaching foundational data analysis skills and providing an overview of data science applications in risk management.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve complex business problems. This course provides foundational knowledge in data analysis and may be useful for aspiring Operations Research Analysts.
Data Architect
Data Architect
Data Architects design and build data management solutions. They ensure that data is accessible, reliable, and secure. This online course provides foundational knowledge in data management and may be useful for individuals looking to become Data Architects.
Data Engineer
Data Engineer
A Data Engineer is responsible for designing, constructing, and maintaining the data pipelines and infrastructure that make data available to data scientists and analysts. This online course may be useful for aspiring Data Engineers by providing a broad overview the data science field and the analysis lifecycle.
Data Visualization Specialist
Data Visualization Specialist
Data Visualization Specialists create visual representations of data. They help businesses communicate complex data insights to stakeholders. This online course provides an introduction to data analysis and visualization and may be useful for aspiring Data Visualization Specialists.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical modeling to analyze financial data. This course may be helpful for aspiring Quantitative Analysts, offering an introduction to data analysis and visualization.
Market Research Analyst
Market Research Analyst
Market Research Analysts collect, analyze, and interpret data to understand consumer behavior and market trends. This course may be helpful for aspiring Market Research Analysts by teaching data analysis and visualization fundamentals.
Business Analyst
Business Analyst
Business Analysts work with stakeholders across an organization to identify and define business needs. This course may be useful for aspiring Business Analysts, giving them an introduction to data analysis and its applications in the business world.
Actuary
Actuary
Actuaries analyze and assess financial risks. They use mathematical and statistical techniques to develop and price insurance and other financial products. This course provides foundational knowledge in data analysis and may be helpful for aspiring Actuaries.
Epidemiologist
Epidemiologist
Epidemiologists investigate the causes and spread of diseases. They use data analysis techniques to identify risk factors and develop prevention strategies. This course provides foundational knowledge in data analysis and may be helpful for aspiring Epidemiologists.
Fraud Analyst
Fraud Analyst
Fraud Analysts investigate and prevent fraudulent activities. They use data analysis techniques to detect suspicious patterns and identify potential fraud. This course provides foundational knowledge in data analysis and may be useful for aspiring Fraud Analysts.
For more career information including salaries, visit:
OpenCourser.com/course/5y1617/m
Reading list
We've selected 11 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
ما علم البيانات؟.
Provides a comprehensive overview of deep learning for natural language processing. It covers the latest deep learning techniques and algorithms for natural language processing.
Provides a comprehensive overview of statistical learning. It covers the latest statistical learning techniques and algorithms.
Save
Comprehensive reference on deep learning. It covers the latest deep learning techniques and algorithms.
Provides a practical guide to deep learning using Python. It covers the latest deep learning techniques and algorithms.
Provides a practical guide to machine learning using Python. It covers the latest machine learning libraries, such as Scikit-Learn and TensorFlow.
Provides a hands-on introduction to data science. It covers the basics of data science, such as data cleaning, data analysis, and data visualization.
Provides a practical guide to machine learning using Python. It covers the latest machine learning libraries, such as Scikit-Learn, Keras, and TensorFlow.
Save
Provides a comprehensive overview of data mining techniques. It covers the latest data mining algorithms and techniques.
Provides a comprehensive overview of data science techniques and their applications in business. It is written for business professionals with no prior knowledge of data science.
Provides a gentle introduction to machine learning. It is written for beginners with no prior knowledge of machine learning.
Provides a gentle introduction to machine learning concepts and algorithms. It is written for beginners with no prior knowledge of machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/5y1617/m
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Time
2 hours
Via
Coursera
Institutions
IBM
IBM Skills Network
Instructors
Rav Ahuja
Alex Aklson
Good to know
Taught by seasoned professionals Alex Aklson and Rav Ahuja, who have extensive experience in the field of data science
Covers a wide range of topics in data science, including data mining, machine learning, and statistical modeling
Emphasizes hands-on learning through interactive exercises and case studies
Suitable for individuals with both technical and non-technical backgrounds
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