Программирование на Python для анализа данных from Coursera

Анализ данных крайне востребован в современном бизнесе. Многие компании обладают большими массивами данных, а инсайты о взаимосвязях внутри этих данных становятся серьезным преимуществом в конкурентной борьбе. Python — наиболее популярный инструмент для анализа данных. Дизайн этого языка позволяет одинаково удобно проверять гипотезы и разрабатывать полноценные приложения. Популярность языка и огромное сообщество его пользователей обеспечивает простой поиск ответов на возникающие вопросы и актуальность необходимых библиотек. В этом курсе вы узнаете, как применять свои навыки программирования для работы с маркетинговыми данными,...

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Teaches how to formulate business hypotheses, which is a critical skill in many business domains

Utilizes Python, which is the most popular tool for data analysis, ensuring learners are using an industry standard tool

Builds a strong foundation for working with marketing data, which is a valuable skill

Involves building and deploying models, which provides hands-on, practical experience

Taught by Artem Kondyukov and Emil Magerramov, who are well respected in the field of data analysis, ensuring learners are being taught by experienced professionals

Requires no explicit prerequisites, making it accessible to learners with varying backgrounds

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 Программирование на Python для анализа данных with these activities:

Review of Data Analysis Basics

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Revisit the fundamental concepts and techniques to ensure that you begin the course with a solid foundation.

Browse courses on Data Analysis

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Review basic data types, variables, and structures.
Refresh concepts of probability and statistical inference.

Resource Compilation for Data Analysis

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Organize and gather helpful resources, such as tutorials, articles, and tools, to further enhance your learning.

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Identify relevant resources and articles on data analysis techniques.
Organize the resources into a logical structure (e.g., by topic or resource type).

Python for Marketing Analysis

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Enhance your Python skills by following tutorials designed specifically for marketing analysis.

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Explore tutorials on data wrangling and cleaning techniques.
Learn about data visualization libraries for creating compelling visuals.

Five other activities

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Data Analysis Study Group

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Connect with fellow students to discuss concepts, share insights, and engage in collaborative problem-solving.

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Join or form a study group with peers enrolled in the course.
Meet regularly to review course content, work on assignments, and prepare for assessments.

Coding Exercises on Data Manipulation

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Sharpen your Python skills through targeted practice exercises focused on data manipulation techniques.

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Attempt coding exercises on topics such as data cleaning, feature engineering, and data aggregation.
Review solutions and discuss with classmates or the instructor to reinforce understanding.

Marketing Campaign Analysis Report

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Apply your knowledge to a real-world scenario by analyzing a marketing campaign and presenting your findings.

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Choose a marketing campaign to analyze and gather relevant data.
Use Python to analyze the data and identify key insights.
Prepare a comprehensive report summarizing your findings and recommendations.

Contribute to Open Source Data Analysis Projects

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Gain practical experience and contribute to the community by participating in open-source data analysis projects.

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Identify open-source projects related to data analysis and marketing.
Make contributions to the project by reporting bugs, suggesting features, or writing documentation.

Mentor Junior Data Analysts

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Reinforce your understanding by sharing your knowledge and expertise with junior data analysts seeking guidance.

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Volunteer to mentor junior data analysts within the university or online platforms.
Provide guidance, answer questions, and share resources to support their learning and development.

Career center

Learners who complete Программирование на Python для анализа данных will develop knowledge and skills that may be useful to these careers:

Data Analyst

Data Analysts study data using advanced analytics techniques and interpret the results of their analysis to design solutions and drive decision-making. They need a strong background in programming languages, statistics, and machine learning to effectively analyze and visualize data to create actionable insights. The course, *Programming on Python for Data Analysis*, is an ideal choice for Data Analysts looking to advance their careers, as it provides a deep understanding of Python, a popular language for data analysis, and its application in real-world scenarios.

See salaries and explore the career path for Data Analyst

Data Scientist

Data Scientists use their knowledge of programming languages, statistics, and machine learning to build models and algorithms to analyze data and solve business problems. The course, *Programming on Python for Data Analysis*, provides a strong foundation in Python, a core skill for Data Scientists, and its application in data analysis and modeling. Additionally, the course's focus on real-world applications will help Data Scientists develop the skills necessary to effectively contribute to data-driven decision-making.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers design, build, and maintain machine learning models and systems. They need a strong foundation in programming languages, statistics, and machine learning to develop and implement effective solutions. The course, *Programming on Python for Data Analysis*, provides a solid foundation in Python, a key language for Machine Learning Engineers, and its application in data analysis and model development. Additionally, the course's focus on real-world applications will help Machine Learning Engineers develop the skills necessary to build and deploy effective machine learning systems.

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Business Intelligence Analyst

Business Intelligence Analysts use data analysis and visualization techniques to identify trends and patterns in business data. They work closely with business stakeholders to understand their needs and develop data-driven insights and solutions. The course, *Programming on Python for Data Analysis*, provides Business Intelligence Analysts with the skills necessary to effectively analyze and visualize data, and its focus on real-world applications will help them develop the expertise necessary to provide valuable insights to stakeholders.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. They need a strong foundation in programming languages, statistics, and financial modeling to effectively analyze and interpret data. The course, *Programming on Python for Data Analysis*, provides a solid foundation in Python, a key language for Quantitative Analysts, and its application in data analysis and financial modeling. Additionally, the course's focus on real-world applications will help Quantitative Analysts develop the skills necessary to make informed investment decisions.

See salaries and explore the career path for Quantitative Analyst

Market Research Analyst

Market Research Analysts study market trends and customer behavior to provide insights for businesses. They need a strong foundation in data analysis and visualization techniques to effectively collect, analyze, and interpret data. The course, *Programming on Python for Data Analysis*, provides Market Research Analysts with the skills necessary to effectively analyze and visualize data, and its focus on real-world applications will help them develop the expertise necessary to provide valuable insights to stakeholders.

See salaries and explore the career path for Market Research Analyst

Marketing Analyst

Marketing Analysts use data analysis and visualization techniques to measure the effectiveness of marketing campaigns and make recommendations for improvement. They need a strong foundation in data analysis and visualization techniques to effectively collect, analyze, and interpret data. The course, *Programming on Python for Data Analysis*, provides Marketing Analysts with the skills necessary to effectively analyze and visualize data, and its focus on real-world applications will help them develop the expertise necessary to provide valuable insights to stakeholders.

See salaries and explore the career path for Marketing Analyst

Operations Research Analyst

Operations Research Analysts use data analysis and visualization techniques to improve the efficiency of business operations. They need a strong foundation in data analysis and visualization techniques to effectively collect, analyze, and interpret data. The course, *Programming on Python for Data Analysis*, provides Operations Research Analysts with the skills necessary to effectively analyze and visualize data, and its focus on real-world applications will help them develop the expertise necessary to make informed decisions about business operations.

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Risk Analyst

Risk Analysts use data analysis and visualization techniques to identify and assess risks to businesses. They need a strong foundation in data analysis and visualization techniques to effectively collect, analyze, and interpret data. The course, *Programming on Python for Data Analysis*, provides Risk Analysts with the skills necessary to effectively analyze and visualize data, and its focus on real-world applications will help them develop the expertise necessary to provide valuable insights to stakeholders.

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Product Manager

Product Managers are responsible for the development and launch of new products. They need a strong understanding of data analysis and visualization techniques to effectively analyze market trends and customer feedback. The course, *Programming on Python for Data Analysis*, provides Product Managers with the skills necessary to effectively analyze and visualize data, and its focus on real-world applications will help them develop the expertise necessary to make informed decisions about product development and launch.

See salaries and explore the career path for Product Manager

Financial Analyst

Financial Analysts use data analysis and visualization techniques to analyze financial data and make investment recommendations. They need a strong foundation in data analysis and visualization techniques to effectively collect, analyze, and interpret data. The course, *Programming on Python for Data Analysis*, provides Financial Analysts with the skills necessary to effectively analyze and visualize data, and its focus on real-world applications will help them develop the expertise necessary to make informed investment recommendations.

See salaries and explore the career path for Financial Analyst

Management Consultant

Management Consultants use data analysis and visualization techniques to help businesses improve their performance. They need a strong foundation in data analysis and visualization techniques to effectively collect, analyze, and interpret data. The course, *Programming on Python for Data Analysis*, provides Management Consultants with the skills necessary to effectively analyze and visualize data, and its focus on real-world applications will help them develop the expertise necessary to provide valuable insights to clients.

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Data Engineer

Data Engineers design and build the infrastructure and systems that store and process data. They need a strong foundation in programming languages, databases, and data engineering tools to effectively manage and analyze data. While the course, *Programming on Python for Data Analysis*, does not directly cover data engineering concepts, it provides a solid foundation in Python, a key language for Data Engineers, and its application in data analysis. This knowledge can be a valuable asset for Data Engineers looking to advance their careers.

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Software Engineer

Software Engineers design, develop, and maintain software systems. They need a strong foundation in programming languages, software development tools, and software engineering principles to effectively build and maintain software systems. While the course, *Programming on Python for Data Analysis*, does not directly cover software engineering concepts, it provides a solid foundation in Python, a popular language for software development, and its application in data analysis. This knowledge can be a valuable asset for Software Engineers looking to advance their careers.

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Computer Scientist

Computer Scientists study the theory and practice of computation. They need a strong foundation in programming languages, algorithms, and data structures to effectively design and analyze computational systems. While the course, *Programming on Python for Data Analysis*, does not directly cover computer science concepts, it provides a solid foundation in Python, a popular language for computer science, and its application in data analysis. This knowledge can be a valuable asset for Computer Scientists looking to advance their careers.

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