مجموعة أدوات عالم البيانات from Coursera

What's inside

Syllabus

الأسبوع الأول

ستتعرف خلال الأسبوع الأول على أهداف ومقاصد تخصص علوم البيانات وعلى كل عنصر من عناصره. ستتلقى أيضًا نظرة عامة عن المجال بالإضافة إلى إرشادات حول كيفية تثبيت برنامج R.

الأسبوع الثاني: تثبيت مجموعة الأدوات

وهذا هو الأسبوع الأكثر كثافة في المحاضرات من الدورة التدريبية. يتمثل الهدف الأساسي في إعدادك لبرامج R وRstudio وGithub والأدوات الأخرى التي سنستخدمها في تخصص علوم البيانات وخلال عملك المستمر كعالِم بيانات.

الأسبوع الثالث: المسائل النظرية

تركز محاضرات الأسبوع الثالث على المسائل النظرية الكامنة وراء تصميم الدراسة وتحويل البيانات إلى معرفة. إذا كنت تواجه مشكلة أو ترغب في استكشاف المسائل بعمق أكثر، فيُرجى البحث عن الإجابات على المنتديات. فهي مصدر ممتاز! وإذا كنت أحد الخبراء الذين على علم بهذه المسائل بالفعل، فيُرجى تخصيص بعض الوقت لمساعدة زملائك في الدراسة من خلال الإجابة عن أسئلتهم أيضًا. ويعتبر هذا من أفضل الطرق لممارسة استخدام مهاراتك وشرحها للآخرين. وهاتان اثنتان من الخصائص الرئيسية لعلماء البيانات الماهرين.

الأسبوع الرابع: تقديم مشروع الدورة التدريبية وتقييمه

في الأسبوع الرابع، سنركز على مشروع الدورة التدريبية. هذه هي فرصتك لتثبيت الأدوات وإعداد الحسابات التي ستحتاجها لبقية التخصص وللعمل في علوم البيانات.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Provides foundational knowledge for data scientists and analysts

Focuses on the practical application of data science tools and technologies

Taught by experienced instructors from Johns Hopkins University

Requires students to have some programming experience

Covers a wide range of topics, from data wrangling to modeling

Reviews summary

Data science toolkit well-received

The Data Science Toolkit course is well-received by learners. While some students found the material to be challenging, the overall consensus among students is that this course effectively introduces learners to the core ideas of data science. Students particularly appreciate the practical component of this course as it introduces learners to tools commonly used by data scientists.

Learners find the course to be informative and engaging.

"معولومات ثريه وممتعه"

"جيدة جدا"

"ممتعة"

Introduces core data science ideas with practical instruction.

"... مقدمة نظرية عن الأفكار الكامنة وراء تحويل البيانات إلى معلومات قابلة للتطبيق."

"... مقدمة عملية عن الأدوات التي سيتم استخدامها في البرنامج مثل التحكم في النُسَخ ولغة Markdown وGit وGitHub وR وRStudio."

Some students found the theoretical material to be challenging.

"VERY DIFFICULT"

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 مجموعة أدوات عالم البيانات with these activities:

Review foundational concepts in linear algebra and calculus

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Strengthen your mathematical foundation by reviewing key concepts in linear algebra and calculus, which are essential for data analysis.

Browse courses on Linear Algebra

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Revisit textbooks or online resources on linear algebra and calculus.
Solve practice problems to refresh your memory and reinforce your understanding.

Review basic statistics concepts

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Brush up on basic statistical concepts to strengthen your foundation for data analysis.

Browse courses on Statistical Concepts

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Revisit textbooks or online resources on introductory statistics.
Go through practice problems to test your understanding.

Build a resource hub for data science tools and technologies

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Organize and compile a comprehensive list of valuable resources for data science, including tools, libraries, and platforms.

Browse courses on Data Science Tools

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Research and identify reputable sources of information.
Create a centralized repository, such as a spreadsheet or wiki.
Categorize and organize the resources based on their functionality or purpose.

Six other activities

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Show all nine activities

Join or form study groups with fellow learners

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Connect with other learners, share knowledge, and reinforce concepts through discussions and collaborative problem-solving.

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Reach out to fellow learners through online forums or social media.
Organize regular study sessions to discuss course materials and work on assignments together.

Follow tutorials on advanced data analysis techniques

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Expand your knowledge by exploring advanced data analysis techniques through online tutorials.

Browse courses on Advanced Data Analysis

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Identify specific advanced techniques you want to learn.
Search for tutorials on reputable platforms like Coursera or edX.
Follow the tutorials, taking notes and completing any exercises provided.

Solve data manipulation exercises

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Practice data manipulation techniques to enhance your proficiency in data preparation.

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Find online platforms or textbooks with data manipulation exercises.
Work through the exercises, experimenting with different data types and operations.

Attend industry webinars and conferences

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Connect with industry professionals and learn about current trends and best practices in data science.

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Identify webinars or conferences related to data science.
Register and attend the events.
Engage with speakers and attendees to gain insights and expand your network.

Participate in data science hackathons

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Test your skills and collaborate with others in a competitive environment, solving real-world data science challenges.

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Find data science hackathons in your area or online.
Form a team or join an existing team.
Work together to develop innovative solutions within the given timeframe.

Develop a data visualization dashboard

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Create an interactive dashboard to visualize and analyze data, demonstrating your understanding of data presentation.

Browse courses on Data Visualization

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Choose a dataset of interest and determine the key insights you want to convey.
Select appropriate visualization techniques and build the dashboard using tools like Tableau or Power BI.
Share your dashboard with others for feedback and iterate based on their suggestions.

Career center

Learners who complete مجموعة أدوات عالم البيانات will develop knowledge and skills that may be useful to these careers:

Data Analyst

A Data Analyst examines large datasets to uncover patterns and trends. They use statistical and analytical techniques to identify insights that can help businesses make better decisions. This course provides a solid foundation in the tools and techniques used by Data Analysts, including R, RStudio, and GitHub. It also covers the theoretical concepts behind data analysis, such as study design and data transformation. This course can help you develop the skills you need to become a successful Data Analyst.

See salaries and explore the career path for Data Analyst

Machine Learning Engineer

A Machine Learning Engineer develops and deploys machine learning models. They use their knowledge to solve a variety of problems, such as fraud detection and spam filtering. This course provides a solid foundation in the tools and techniques used by Machine Learning Engineers, including R, RStudio, and GitHub. It also covers the theoretical concepts behind machine learning, such as supervised and unsupervised learning. This course can help you develop the skills you need to become a successful Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

A Data Scientist uses their knowledge to extract meaningful insights from data. They use statistical models and machine learning algorithms to build predictive models and identify patterns. This course provides a comprehensive overview of the tools and techniques used by Data Scientists, including R, RStudio, and GitHub. It also covers the theoretical concepts behind data science, such as study design and data transformation. This course can help you develop the skills you need to become a successful Data Scientist.

See salaries and explore the career path for Data Scientist

Statistician

A Statistician collects, analyzes, and interprets data. They use their knowledge to draw conclusions about the world around them. This course provides a solid foundation in the tools and techniques used by Statisticians, including R, RStudio, and GitHub. It also covers the theoretical concepts behind statistics, such as probability and inference. This course can help you develop the skills you need to become a successful Statistician.

See salaries and explore the career path for Statistician

Business Analyst

A Business Analyst uses data to solve business problems. They work with stakeholders to identify the problem, collect data, and develop solutions. This course provides a solid foundation in the tools and techniques used by Business Analysts, including R, RStudio, and GitHub. It also covers the theoretical concepts behind business analysis, such as data management and process improvement. This course can help you develop the skills you need to become a successful Business Analyst.

See salaries and explore the career path for Business Analyst

Operations Research Analyst

An Operations Research Analyst uses mathematical models to solve complex problems. They work with businesses to improve efficiency and productivity. This course provides a solid foundation in the tools and techniques used by Operations Research Analysts, including R, RStudio, and GitHub. It also covers the theoretical concepts behind operations research, such as linear programming and network optimization. This course can help you develop the skills you need to become a successful Operations Research Analyst.

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Data Engineer

A Data Engineer builds and maintains the infrastructure that supports data analysis and machine learning. They work with data scientists and engineers to design and implement data pipelines. This course provides a solid foundation in the tools and techniques used by Data Engineers, including R, RStudio, and GitHub. It also covers the theoretical concepts behind data engineering, such as data storage and processing. This course can help you develop the skills you need to become a successful Data Engineer.

See salaries and explore the career path for Data Engineer

Software Engineer

A Software Engineer designs, develops, and maintains software systems. They use their knowledge to solve a variety of problems, such as data analysis and machine learning. This course provides a solid foundation in the tools and techniques used by Software Engineers, including R, RStudio, and GitHub. It also covers the theoretical concepts behind software engineering, such as software design and testing. This course can help you develop the skills you need to become a successful Software Engineer.

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Market Researcher

A Market Researcher collects and analyzes data about consumer behavior. They use their knowledge to help businesses develop marketing strategies. This course provides a solid foundation in the tools and techniques used by Market Researchers, including R, RStudio, and GitHub. It also covers the theoretical concepts behind market research, such as survey design and data analysis. This course can help you develop the skills you need to become a successful Market Researcher.

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Quantitative Analyst

A Quantitative Analyst uses mathematical and statistical models to analyze financial data. They work with investment banks and hedge funds to develop trading strategies. This course provides a solid foundation in the tools and techniques used by Quantitative Analysts, including R, RStudio, and GitHub. It also covers the theoretical concepts behind quantitative finance, such as financial modeling and risk management. This course can help you develop the skills you need to become a successful Quantitative Analyst.

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Insurance Analyst

An Insurance Analyst evaluates the risks and costs of insurance policies. They work with insurance companies to develop and price policies. This course provides a solid foundation in the tools and techniques used by Insurance Analysts, including R, RStudio, and GitHub. It also covers the theoretical concepts behind insurance, such as risk assessment and underwriting. This course can help you develop the skills you need to become a successful Insurance Analyst.

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Financial Analyst

A Financial Analyst evaluates the financial health of companies and industries. They use their knowledge to make investment recommendations. This course provides a solid foundation in the tools and techniques used by Financial Analysts, including R, RStudio, and GitHub. It also covers the theoretical concepts behind financial analysis, such as financial modeling and valuation. This course can help you develop the skills you need to become a successful Financial Analyst.

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Risk Analyst

A Risk Analyst identifies and manages risks to businesses. They work with businesses to develop risk management strategies. This course provides a solid foundation in the tools and techniques used by Risk Analysts, including R, RStudio, and GitHub. It also covers the theoretical concepts behind risk management, such as risk assessment and mitigation. This course can help you develop the skills you need to become a successful Risk Analyst.

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Actuary

An Actuary uses mathematical and statistical models to assess financial risks. They work with insurance companies and pension funds to develop and price products. This course provides a solid foundation in the tools and techniques used by Actuaries, including R, RStudio, and GitHub. It also covers the theoretical concepts behind actuarial science, such as mortality and morbidity tables. This course can help you develop the skills you need to become a successful Actuary.

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Economist

An Economist studies the production, distribution, and consumption of goods and services. They use their knowledge to analyze economic trends and make policy recommendations. This course provides a solid foundation in the tools and techniques used by Economists, including R, RStudio, and GitHub. It also covers the theoretical concepts behind economics, such as microeconomics and macroeconomics. This course can help you develop the skills you need to become a successful Economist.

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