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Артем Кондюков and Эмиль Магеррамов
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...
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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, формулирования бизнес-гипотез и визуализации данных. Курс ориентирован на практику и позволит вам сразу приступить к работе с данными и построению моделей
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Good to know

Know what's good
, what to watch for
, and possible dealbreakers
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

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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 Программирование на Python для анализа данных with these activities:
Review of Data Analysis Basics
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
Organize and gather helpful resources, such as tutorials, articles, and tools, to further enhance your learning.
Show steps
  • 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
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
Connect with fellow students to discuss concepts, share insights, and engage in collaborative problem-solving.
Show steps
  • 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
Sharpen your Python skills through targeted practice exercises focused on data manipulation techniques.
Show steps
  • 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
Apply your knowledge to a real-world scenario by analyzing a marketing campaign and presenting your findings.
Show steps
  • 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
Gain practical experience and contribute to the community by participating in open-source data analysis projects.
Show steps
  • 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
Reinforce your understanding by sharing your knowledge and expertise with junior data analysts seeking guidance.
Show steps
  • 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.

Reading list

We've selected 13 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 Программирование на Python для анализа данных.
This comprehensive book on Python data science that covers a wide range of topics, including data cleaning, visualization, machine learning, and statistical modeling. It's well-suited for both beginners and experienced data scientists.
Provides a practical guide to machine learning using Python, with a focus on the popular libraries Scikit-Learn, Keras, and TensorFlow. It covers all the essential topics of machine learning, from data preprocessing to model evaluation.
Teaches you how to build machine learning models from scratch using Python. It starts with the basics of Python programming and then covers the core concepts of machine learning, such as supervised learning, unsupervised learning, and deep learning.
Provides a comprehensive introduction to machine learning using Python. It covers all the major machine learning algorithms, including linear regression, logistic regression, decision trees, random forests, and support vector machines.
Provides a practical guide to data analysis using Python. It covers the basics of Python programming and then shows how to use Python libraries such as Pandas and NumPy for data analysis.
Provides a comprehensive introduction to statistical learning. It covers a wide range of topics, including linear regression, logistic regression, decision trees, and random forests.
Provides a critical look at the field of data science. It discusses the ethical issues and challenges involved in using data to make decisions.
Provides a gentle introduction to Python programming. It's a good starting point for those who are new to programming.
Provides a practical guide to automating tasks using Python. It covers a wide range of topics, from web scraping to data analysis.
Provides a simple and easy-to-follow introduction to Python programming. It's a good choice for those who are new to programming and want to learn Python quickly.

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