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Big Data

procesamiento y análisis

Llorenç Badiella and Isabel Serra

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Introduces basic methods and techniques for processing and analyzing data in the context of Big Data

Provides a general overview of the options offered by data analysis for exploring, confirming indications, and extracting conclusions

Taught by instructors Llorenç Badiella and Isabel Serra

Suitable for students and professionals who wish to approach data processing and analysis in Big Data

May be particularly interesting for students with certain knowledge of data analysis who want to enter the Big Data environment

Also useful for students with experience in Big Data environments who want to acquire a greater analytical vision

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Reviews summary

Big data analysis with pyspark overview

According to students, this course provides a solid introduction to Big Data processing and analysis using PySpark. Many appreciate the practical approach and the use of a virtual machine and Jupyter notebooks for hands-on learning. Learners frequently mention that it covers fundamental techniques like regression, decision trees, and neural networks, along with unsupervised methods. While some find it a good starting point and easy to follow, others with more experience consider it too basic or lacking in sufficient depth on certain topics. The difficulty of assignments compared to lectures is noted by some as a potential challenge.

Some find it too basic, others find it sufficient.

"While it covers the fundamentals, I felt some topics could have gone into more detail."

"For someone with a bit of background, this course might feel too superficial."

"It offers a good high-level view, but don't expect deep dives into each algorithm."

"I was hoping for more advanced techniques and optimization discussions."

"Sufficient depth for an introductory course, but not for mastery."

Generally positive experience with the VM.

"The provided virtual machine worked flawlessly and made getting started easy."

"Using the VM prevented many potential setup headaches I've had with other courses."

"The virtual environment was stable and user-friendly."

"It was convenient having all the necessary tools pre-installed on the VM."

"Initial technical issues seem to have been resolved; the VM worked great for me."

Serves as a valuable overview for newcomers.

"This course provides a great starting point for anyone new to Big Data analysis techniques."

"As an introduction, it covers the necessary basics without being overwhelming."

"It gave me a clear overview of the main methods used in Big Data analysis."

"It was a good first step into the world of processing and analyzing large datasets."

"Provides a decent foundation for understanding the concepts."

Appreciation for hands-on PySpark experience.

"The practical exercises using PySpark on the virtual machine were incredibly helpful for understanding the concepts."

"Learning PySpark through the course's case study made the abstract concepts much more concrete."

"The hands-on labs with PySpark in the Jupyter environment were the highlight for me."

"I really liked working directly with PySpark; it's essential for Big Data."

"Using the provided VM with PySpark pre-configured saved a lot of setup time."

Quizzes/assignments can be challenging for some.

"Some of the quizzes seemed significantly harder than what was taught in the videos."

"The assignments require more external knowledge than the lectures provide."

"I struggled with the final assignments, they felt like a big jump in difficulty."

"Be prepared to do some extra research to complete the graded tasks."

"The gap between lecture material and assignment requirements is notable."

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 Big Data: procesamiento y análisis with these activities:

Organize Course Notes and Resources

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Organize and expand on course notes and resources to enhance understanding and retention.

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Review and summarize lecture notes after each class.
Compile relevant materials from textbooks, articles, and online resources.
Create a structured system for organizing notes and resources.

Review Data Mining Practical

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Review a practical guide to data mining to solidify the understanding of data analysis and Big Data concepts.

View New Challenges in Rock Mechanics and Rock... on Amazon

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Read chapters 1-3 to gain an overview of data mining concepts and techniques.
Complete the exercises in chapters 4-6 to apply data mining techniques to real-world datasets.
Summarize key concepts and techniques in your own words to enhance retention.

Solve Data Analysis Puzzles

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Engage in data analysis puzzles to reinforce problem-solving skills and deepen understanding of data analysis techniques.

Browse courses on Data Analysis

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Attempt to solve data analysis puzzles on platforms like Kaggle or HackerRank.
Analyze the solutions to identify patterns and improve problem-solving strategies.
Collaborate with peers to discuss and learn from different approaches.

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Participate in Data Analysis Study Group

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Engage with peers in a study group to discuss course concepts, share resources, and reinforce understanding.

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Join or create a study group with fellow students.
Regularly meet to discuss course material, work on assignments together, and ask questions.
Collaborate on projects or case studies to apply data analysis techniques.

Create a Data Analysis Case Study

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Develop a data analysis case study to demonstrate the application of data analysis techniques to a specific problem.

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Choose a business problem that can be addressed using data analysis.
Collect and clean the necessary data.
Apply data analysis techniques to explore and analyze the data.
Write a report that summarizes the findings and provides recommendations.

Career center

Learners who complete Big Data: procesamiento y análisis will develop knowledge and skills that may be useful to these careers:

Data Analyst

Data Analysts collect, clean, and analyze data to help organizations make informed decisions. This course can help you develop the skills necessary to succeed in this role, including data exploration, pre-processing, modeling, and analysis. You will also learn how to use common tools and techniques in the field, such as Jupyter and PySpark.

See salaries and explore the career path for Data Analyst

Data Scientist

Data Scientists use their knowledge of data analysis and machine learning to solve business problems. This course can help you build a foundation in the skills needed for this role, including data modeling, machine learning algorithms, and data visualization. You will also learn how to use popular tools and techniques in the field, such as Python and R.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers design and build machine learning models to solve business problems. This course can help you develop the skills necessary to succeed in this role, including data modeling, machine learning algorithms, and cloud computing. You will also learn how to use common tools and techniques in the field, such as TensorFlow and scikit-learn.

See salaries and explore the career path for Machine Learning Engineer

Business Analyst

Business Analysts use data and analysis to help organizations improve their performance. This course can help you develop the skills necessary to succeed in this role, including data collection, data analysis, and data visualization. You will also learn how to use common tools and techniques in the field, such as Microsoft Excel and Power BI.

See salaries and explore the career path for Business Analyst

Data Engineer

Data Engineers design and build data pipelines to support data analysis and machine learning. This course can help you develop the skills necessary to succeed in this role, including data warehousing, data integration, and data quality management. You will also learn how to use common tools and techniques in the field, such as Apache Hadoop and Apache Spark.

See salaries and explore the career path for Data Engineer

Statistician

Statisticians use statistical methods to analyze data and draw conclusions. This course can help you develop the skills necessary to succeed in this role, including data analysis, statistical modeling, and data visualization. You will also learn how to use common tools and techniques in the field, such as SAS and R.

See salaries and explore the career path for Statistician

Operations Research Analyst

Operations Research Analysts use mathematical and statistical methods to solve business problems. This course can help you develop the skills necessary to succeed in this role, including data analysis, optimization, and simulation. You will also learn how to use common tools and techniques in the field, such as linear programming and queuing theory.

See salaries and explore the career path for Operations Research Analyst

Financial Analyst

Financial Analysts use data and analysis to help organizations make investment decisions. This course can help you develop the skills necessary to succeed in this role, including data analysis, financial modeling, and valuation. You will also learn how to use common tools and techniques in the field, such as Bloomberg and Capital IQ.

See salaries and explore the career path for Financial Analyst

Marketing Analyst

Marketing Analysts use data and analysis to help organizations improve their marketing campaigns. This course can help you develop the skills necessary to succeed in this role, including data analysis, market research, and campaign optimization. You will also learn how to use common tools and techniques in the field, such as Google Analytics and Adobe Analytics.

See salaries and explore the career path for Marketing Analyst

Sales Analyst

Sales Analysts use data and analysis to help organizations improve their sales performance. This course can help you develop the skills necessary to succeed in this role, including data analysis, sales forecasting, and customer relationship management. You will also learn how to use common tools and techniques in the field, such as Salesforce and SAP.

See salaries and explore the career path for Sales Analyst

Customer Success Manager

Customer Success Managers use data and analysis to help organizations improve customer satisfaction. This course can help you develop the skills necessary to succeed in this role, including data analysis, customer segmentation, and churn prediction. You will also learn how to use common tools and techniques in the field, such as Salesforce and Zendesk.

See salaries and explore the career path for Customer Success Manager

Product Manager

Product Managers use data and analysis to help organizations develop and improve their products. This course can help you develop the skills necessary to succeed in this role, including data analysis, market research, and product roadmap development. You will also learn how to use common tools and techniques in the field, such as Jira and Asana.

See salaries and explore the career path for Product Manager

Project Manager

Project Managers use data and analysis to help organizations plan and execute projects. This course can help you develop the skills necessary to succeed in this role, including data analysis, project planning, and risk management. You will also learn how to use common tools and techniques in the field, such as Microsoft Project and Asana.

See salaries and explore the career path for Project Manager

Business Intelligence Analyst

Business Intelligence Analysts use data and analysis to help organizations make better decisions. This course can help you develop the skills necessary to succeed in this role, including data analysis, data visualization, and dashboard design. You will also learn how to use common tools and techniques in the field, such as Power BI and Tableau.

See salaries and explore the career path for Business Intelligence Analyst

Data Governance Analyst

Data Governance Analysts use data and analysis to help organizations manage their data. This course can help you develop the skills necessary to succeed in this role, including data analysis, data quality management, and data security. You will also learn how to use common tools and techniques in the field, such as Informatica and Collibra.

See salaries and explore the career path for Data Governance Analyst

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 Big Data: procesamiento y análisis.

Big Data Analytics

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Provides a comprehensive overview of big data analytics, from strategic planning to enterprise integration. It valuable resource for anyone looking to understand the potential of big data and how to use it to drive business value.

Big Data Analytics: From Strategic Planning to...

Kindle Edition

Deep Learning with Python, Second Edition

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Provides a comprehensive overview of deep learning, covering the basics of neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for anyone looking to learn how to use deep learning to solve complex problems.

Big Data

procesamiento y análisis

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Reviews summary

Big data analysis with pyspark overview

Activities

Career center

Reading list

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