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A Practical Guide to Python for Data Science

Working on real data science projects is a rewarding experience. But how do you get to the point where you can make a real contribution? What skills and experience do you need? What challenges might occur along the way? In this article, we’ll address all these questions.

Data has become ubiquitous in our modern world. It’s generated from many sources, including social media, IoT devices, business transactions, finance, government and public records, academic research, communication systems, and even satellites and remote sensing technology. Some estimates suggest that 90% of the world’s data has been generated in the previous two years alone, with over 300 million terabytes being created every day! How is it possible to understand and draw insights from all this data?

This is the job of a data scientist. In this article, we’ll give you an overview of how to become a data scientist, what a data scientist actually does, and what tools they use. Hint: It’s Python! Python has become an indispensable tool in the tech world for many reasons, and it’s particularly powerful for data science projects.

If you’re new to Python and are looking for some hands-on learning material, consider taking our Python Basics track; it combines three beginner-friendly courses to get you on your feet. For more in-depth material, the Learn Programming with Python track bundles together 5 interactive courses and includes 135 interactive coding challenges. There has never been a better time to learn Python than in 2024.

A Brief History of Data Science

The roots of data science lie in the fields of statistics and computer science. In the 1960s and 1970s, statisticians and computer scientists began working on methods to analyze and interpret different kinds of datasets. However, it wasn’t until the recent growth of digital data that the term «data science» emerged.

In the early 2000s, William S. Cleveland created an action plan to expand the field of statistics to incorporate data analysis. The report, titled Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics is often credited with popularizing the term and emphasizing the interdisciplinary nature of the field.

Practical Guide to Python for Data Science

Today, data science encompasses a broad range of techniques and approaches to extract valuable insights from complex and vast datasets. Modern data scientists are responsible for collecting and cleaning data and then analyzing it to uncover patterns, trends, and actionable insights. They use a combination of statistical methods, machine learning algorithms, and domain knowledge to make informed decisions and predictions. Data scientists work across various industries – including finance, healthcare, technology, and more.

Key skills for a data scientist include proficiency in programming languages like Python, experience with data manipulation and visualization tools, a strong understanding of statistical concepts, and the ability to communicate findings effectively to both technical and non-technical people. The role of a data scientist continues to evolve with advancements in technology and the increasing importance of data-driven decision-making in various sectors.

The Path to Becoming a Data Scientist

The ways to become a data scientist are as varied as the datasets you can be expected to analyze. My personal journey began with a degree in mathematics and physics, where I discovered a love of research. This led to a PhD where I was required to learn programming with Python and was expected to start working with real-world data of atmospheric measurements. It was here I discovered a knack for using Python to do statistical analyses of different datasets. It was also my first exposure to machine learning – a powerful tool used to find hidden patterns in data. After a position as a postdoctoral researcher, I found my way into industry, where I worked as a professional data scientist.

However, there is not necessarily a typical path to becoming a data scientist. It usually involves a combination of educational background, specific subjects, and a diverse set of skills. Many data scientists hold a bachelor’s degree or an advanced degree in fields like computer science, statistics, mathematics, or a related quantitative discipline. Relevant coursework may include statistics, machine learning, data analysis, and programming. Proficiency in programming languages like Python is crucial, and any subjects that expose you to data manipulation and visualization are valuable.

Strong analytical and problem-solving skills are essential, as data scientists need to extract meaningful insights from complex datasets. It’s not always clear which questions to ask, which techniques to use, and which tools to reach for. Additionally, effective communication skills are vital to convey findings to non-technical people. Continuous learning is also crucial in this dynamic field, as technologies are continuously evolving.

A great way to stand out among any group of people with diverse skills is by gaining hands-on experience through projects, internships, or online data science competitions, which can enhance practical skills. You can download your own dataset and start practicing data analysis in Python or take part in data science challenges. Certifications in data science and participation in the open-source community further provide experience working on real-world problems.

Python in Real-World Data Science

Data science isn’t just about writing Python code to handle data, develop predictive models, and produce nice visualizations. It has to have real-world impact. Data science matters because it empowers organizations to turn raw data into actionable insights, driving informed decision-making. In various domains – from healthcare and finance to marketing and technology – data science plays a crucial role in optimizing processes, predicting trends, and solving complex problems.

In healthcare, for example, data science has made a significant impact in the interpretation of medical images. Healthcare professionals rely on images from X-rays, MRIs, and CAT scans to get an idea of what’s happening inside a patient’s body. However, the interpretation of these images is done by humans, who could miss identifying microscopic features. Machine learning models can be trained on huge datasets of medical images and be used to automatically identify any areas of concern.

In manufacturing, data science contributes to improving product quality by analyzing data from production processes to identify factors influencing product defects and variability. Data might include physical measurements from sensors in the production process (such as temperature, pressure, and vibration) as well as quantitative or qualitative estimates of product quality. By leveraging techniques such as statistical analysis and anomaly detection, manufacturers can detect deviations from optimal operating conditions and take corrective actions in real time to ensure consistent product quality.

For numerous use cases like these, Python is an indispensable tool because of its versatility and readability. The number of open-source Python libraries – which contain extra functionality outside of the usual Python built-in functions and can be imported into your programs – makes Python incredibly useful in data science. For working with medical images, open-cv can be used to process and analyze many types of image files. For statistical analysis and anomaly detection, libraries such as pandas, NumPy, and scikit-learn are indispensable.

Python Libraries for Data Science

In the previous section, we mentioned some common Python libraries for data science. These have also appeared in our article Top 15 Python Libraries for Data Science. Libraries like pandas, NumPy, SciPy, Matplotlib, and scikit-learn form the backbone of Python-based data science projects. During the technical development of a project, these libraries are often used daily.

The pandas library offers powerful data structures and functions for data manipulation and analysis, making tasks like cleaning, filtering, and transforming datasets efficient and intuitive. And although it’s a standalone tool, SQL is also important when working with large datasets.

NumPy provides support for numerical computing with arrays, enabling fast and efficient operations on large datasets. SciPy complements NumPy by offering a wide range of scientific computing functions, including optimization, integration, and interpolation. Matplotlib facilitates the creation of high-quality visualizations, which are crucial for exploring and communicating insights from data. Lastly, scikit-learn offers a comprehensive suite of machine learning algorithms and tools for tasks such as classification, regression, clustering, and dimensionality reduction, allowing data scientists to build and deploy predictive models with ease.

Python’s capability in data handling and manipulation can be invaluable in various data science projects. In a project involving the sentiment analysis of customer reviews, Python can be used to clean and preprocess text data, removing noise and extracting relevant features via libraries like pandas and NLTK (Natural Language Toolkit). Exploratory data analysis (EDA) can be performed using Matplotlib and Seaborn. This allows data scientists to visualize patterns and trends in the data, aiding in the identification of different sentiments in text data and the key themes being described.

Data visualization plays an important role in data science. It’s a powerful tool for understanding complex datasets, communicating insights, and guiding decision-making. We already mentioned popular libraries like Matplotlib and Seaborn. These allow data scientists to effectively communicate their findings, facilitate collaboration across teams, and drive informed decision-making in various domains.

A Real-World Python for Data Science Example

For a real-world example of using Python for data science, consider a dataset of atmospheric soundings which we downloaded and prepared in the article 7 Datasets to Practice Data Analysis in Python. Follow the article link to download the data, then load the data into a pandas DataFrame called df. We’ll start from where we left off in that article.

Say we want to determine the height of the tropopause, where the temperature changes from decreasing with altitude to increasing with altitude. We first want to smooth the data to remove any small-scale variations. In the article How to Plot a Running Average in Python Using Matplotlib, we explain how to do this with pandas. Here’s the code:

t_average = df['TEMP'].rolling(window=5).mean() df['T_AVE'] = t_average

Now, we want to determine the height at which the minimum temperature occurs. To get some inspiration on how to implement this, go to your favorite search engine and search for something like: ‘Python pandas find position of minimum’. After a little reading, you’ll find the built-in method argmin(). It returns the index of the minimum value in a series:

min_temp_index = df['T_AVE'].argmin() print('Tropopause occurs around: <> m'.format(df['HGHT'].iloc[min_temp_index])) Tropopause occurs around: 11037.0 m

Be sure to plot the temperature profile to check that the results make sense:

df.plot('TEMP', 'HGHT')

Practical Guide to Python for Data Science

From here, you could run this analysis for different seasons to see how the structure of the atmosphere changes over time.

Challenges of a Data Science Career

Depending on your path to becoming a data scientist, you’ll have different skills and experiences. Since the job is so multi-faceted, there will inevitably be gaps in your knowledge that you’ll have to fill. Coming from an academic background where everyone was an expert in a similar field, I was required to learn how to work effectively with people from a variety of backgrounds – many of whom were non-technical.

It’s also common for there to be organizational and communication hurdles, such as aligning what is technically possible with business objectives; management might want to optimize a process, but the available dataset might be insufficient to get there.

Managing expectations from others is important; some have the idea that machine learning can solve everything. To navigate these challenges, it’s important to prioritize clear and concise communication, focusing on storytelling techniques to convey the significance of data insights. Developing strong interdisciplinary collaboration, cultivating domain expertise, and actively engaging with other team members throughout the project lifecycle can help ensure alignment with organizational goals.

Besides developing the necessary soft skills, technical challenges can pose additional hurdles. One common challenge is debugging code, especially when dealing with complex algorithms or integrating multiple libraries and frameworks. To overcome this challenge, it’s necessary to adopt systematic debugging practices, such as using print() statements and the logging and debugging tools available in many integrated development environments (IDEs). We go into more detail on this in 4 Best Python IDE and Code Editors. Additionally, making use of online forums and community resources such as Stack Overflow can provide new perspectives into solving challenging technical issues.

Handling large datasets is another prevalent challenge in data science, particularly in terms of memory management and processing speed. To address this, techniques like data sampling, parallel processing, and distributed computing frameworks can be used. Optimizing code efficiency and minimizing memory usage is a critical factor in many applications, e.g. when processing large numbers of images or videos.

What’s Next in Your Python and Data Science Path?

Embarking on a journey in data science with Python opens doors to endless possibilities and opportunities for growth and innovation. Python’s rich ecosystem of libraries, tools, and community support provides a solid foundation for data scientists to tackle complex challenges and make meaningful contributions across diverse domains. A good foundation in Python will make you not only proficient in working with data but also a solid Python developer.

As you continue your journey, remember to embrace curiosity and a growth mindset. Constantly seek new resources to get extra practice in Python and use courses and documentation to expand your knowledge and skills. There are some great books to help you learn. Dive into online communities, forums, and meetups to connect with fellow data enthusiasts, exchange ideas, and collaborate on projects. We discuss these topics in How to Master Python: A Guide for Beginners.

Don’t hesitate to explore specialized areas within data science – such as machine learning, natural language processing, and deep learning – to deepen your expertise and stay at the forefront of innovation.

Whether you’re a seasoned practitioner or just starting out, your journey in data science with Python will open the door to learning opportunities, impactful discoveries, and diverse career paths. So, keep coding and exploring what you can achieve with Python and data science.

A Practical Guide to Python for Data Science

Working on real data science projects is a rewarding experience. But how do you get to the point where you can make a real contribution? What skills and experience do you need? What challenges might occur along the way? In this article, we’ll address all these questions.

Data has become ubiquitous in our modern world. It’s generated from many sources, including social media, IoT devices, business transactions, finance, government and public records, academic research, communication systems, and even satellites and remote sensing technology. Some estimates suggest that 90% of the world’s data has been generated in the previous two years alone, with over 300 million terabytes being created every day! How is it possible to understand and draw insights from all this data?

This is the job of a data scientist. In this article, we’ll give you an overview of how to become a data scientist, what a data scientist actually does, and what tools they use. Hint: It’s Python! Python has become an indispensable tool in the tech world for many reasons, and it’s particularly powerful for data science projects.

If you’re new to Python and are looking for some hands-on learning material, consider taking our Python Basics track; it combines three beginner-friendly courses to get you on your feet. For more in-depth material, the Learn Programming with Python track bundles together 5 interactive courses and includes 135 interactive coding challenges. There has never been a better time to learn Python than in 2024.

A Brief History of Data Science

The roots of data science lie in the fields of statistics and computer science. In the 1960s and 1970s, statisticians and computer scientists began working on methods to analyze and interpret different kinds of datasets. However, it wasn’t until the recent growth of digital data that the term «data science» emerged.

In the early 2000s, William S. Cleveland created an action plan to expand the field of statistics to incorporate data analysis. The report, titled Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics is often credited with popularizing the term and emphasizing the interdisciplinary nature of the field.

Practical Guide to Python for Data Science

Today, data science encompasses a broad range of techniques and approaches to extract valuable insights from complex and vast datasets. Modern data scientists are responsible for collecting and cleaning data and then analyzing it to uncover patterns, trends, and actionable insights. They use a combination of statistical methods, machine learning algorithms, and domain knowledge to make informed decisions and predictions. Data scientists work across various industries – including finance, healthcare, technology, and more.

Key skills for a data scientist include proficiency in programming languages like Python, experience with data manipulation and visualization tools, a strong understanding of statistical concepts, and the ability to communicate findings effectively to both technical and non-technical people. The role of a data scientist continues to evolve with advancements in technology and the increasing importance of data-driven decision-making in various sectors.

The Path to Becoming a Data Scientist

The ways to become a data scientist are as varied as the datasets you can be expected to analyze. My personal journey began with a degree in mathematics and physics, where I discovered a love of research. This led to a PhD where I was required to learn programming with Python and was expected to start working with real-world data of atmospheric measurements. It was here I discovered a knack for using Python to do statistical analyses of different datasets. It was also my first exposure to machine learning – a powerful tool used to find hidden patterns in data. After a position as a postdoctoral researcher, I found my way into industry, where I worked as a professional data scientist.

However, there is not necessarily a typical path to becoming a data scientist. It usually involves a combination of educational background, specific subjects, and a diverse set of skills. Many data scientists hold a bachelor’s degree or an advanced degree in fields like computer science, statistics, mathematics, or a related quantitative discipline. Relevant coursework may include statistics, machine learning, data analysis, and programming. Proficiency in programming languages like Python is crucial, and any subjects that expose you to data manipulation and visualization are valuable.

Strong analytical and problem-solving skills are essential, as data scientists need to extract meaningful insights from complex datasets. It’s not always clear which questions to ask, which techniques to use, and which tools to reach for. Additionally, effective communication skills are vital to convey findings to non-technical people. Continuous learning is also crucial in this dynamic field, as technologies are continuously evolving.

A great way to stand out among any group of people with diverse skills is by gaining hands-on experience through projects, internships, or online data science competitions, which can enhance practical skills. You can download your own dataset and start practicing data analysis in Python or take part in data science challenges. Certifications in data science and participation in the open-source community further provide experience working on real-world problems.

Python in Real-World Data Science

Data science isn’t just about writing Python code to handle data, develop predictive models, and produce nice visualizations. It has to have real-world impact. Data science matters because it empowers organizations to turn raw data into actionable insights, driving informed decision-making. In various domains – from healthcare and finance to marketing and technology – data science plays a crucial role in optimizing processes, predicting trends, and solving complex problems.

In healthcare, for example, data science has made a significant impact in the interpretation of medical images. Healthcare professionals rely on images from X-rays, MRIs, and CAT scans to get an idea of what’s happening inside a patient’s body. However, the interpretation of these images is done by humans, who could miss identifying microscopic features. Machine learning models can be trained on huge datasets of medical images and be used to automatically identify any areas of concern.

In manufacturing, data science contributes to improving product quality by analyzing data from production processes to identify factors influencing product defects and variability. Data might include physical measurements from sensors in the production process (such as temperature, pressure, and vibration) as well as quantitative or qualitative estimates of product quality. By leveraging techniques such as statistical analysis and anomaly detection, manufacturers can detect deviations from optimal operating conditions and take corrective actions in real time to ensure consistent product quality.

For numerous use cases like these, Python is an indispensable tool because of its versatility and readability. The number of open-source Python libraries – which contain extra functionality outside of the usual Python built-in functions and can be imported into your programs – makes Python incredibly useful in data science. For working with medical images, open-cv can be used to process and analyze many types of image files. For statistical analysis and anomaly detection, libraries such as pandas, NumPy, and scikit-learn are indispensable.

Python Libraries for Data Science

In the previous section, we mentioned some common Python libraries for data science. These have also appeared in our article Top 15 Python Libraries for Data Science. Libraries like pandas, NumPy, SciPy, Matplotlib, and scikit-learn form the backbone of Python-based data science projects. During the technical development of a project, these libraries are often used daily.

The pandas library offers powerful data structures and functions for data manipulation and analysis, making tasks like cleaning, filtering, and transforming datasets efficient and intuitive. And although it’s a standalone tool, SQL is also important when working with large datasets.

NumPy provides support for numerical computing with arrays, enabling fast and efficient operations on large datasets. SciPy complements NumPy by offering a wide range of scientific computing functions, including optimization, integration, and interpolation. Matplotlib facilitates the creation of high-quality visualizations, which are crucial for exploring and communicating insights from data. Lastly, scikit-learn offers a comprehensive suite of machine learning algorithms and tools for tasks such as classification, regression, clustering, and dimensionality reduction, allowing data scientists to build and deploy predictive models with ease.

Python’s capability in data handling and manipulation can be invaluable in various data science projects. In a project involving the sentiment analysis of customer reviews, Python can be used to clean and preprocess text data, removing noise and extracting relevant features via libraries like pandas and NLTK (Natural Language Toolkit). Exploratory data analysis (EDA) can be performed using Matplotlib and Seaborn. This allows data scientists to visualize patterns and trends in the data, aiding in the identification of different sentiments in text data and the key themes being described.

Data visualization plays an important role in data science. It’s a powerful tool for understanding complex datasets, communicating insights, and guiding decision-making. We already mentioned popular libraries like Matplotlib and Seaborn. These allow data scientists to effectively communicate their findings, facilitate collaboration across teams, and drive informed decision-making in various domains.

A Real-World Python for Data Science Example

For a real-world example of using Python for data science, consider a dataset of atmospheric soundings which we downloaded and prepared in the article 7 Datasets to Practice Data Analysis in Python. Follow the article link to download the data, then load the data into a pandas DataFrame called df. We’ll start from where we left off in that article.

Say we want to determine the height of the tropopause, where the temperature changes from decreasing with altitude to increasing with altitude. We first want to smooth the data to remove any small-scale variations. In the article How to Plot a Running Average in Python Using Matplotlib, we explain how to do this with pandas. Here’s the code:

t_average = df['TEMP'].rolling(window=5).mean() df['T_AVE'] = t_average

Now, we want to determine the height at which the minimum temperature occurs. To get some inspiration on how to implement this, go to your favorite search engine and search for something like: ‘Python pandas find position of minimum’. After a little reading, you’ll find the built-in method argmin(). It returns the index of the minimum value in a series:

min_temp_index = df['T_AVE'].argmin() print('Tropopause occurs around: <> m'.format(df['HGHT'].iloc[min_temp_index])) Tropopause occurs around: 11037.0 m

Be sure to plot the temperature profile to check that the results make sense:

df.plot('TEMP', 'HGHT')

Practical Guide to Python for Data Science

From here, you could run this analysis for different seasons to see how the structure of the atmosphere changes over time.

Challenges of a Data Science Career

Depending on your path to becoming a data scientist, you’ll have different skills and experiences. Since the job is so multi-faceted, there will inevitably be gaps in your knowledge that you’ll have to fill. Coming from an academic background where everyone was an expert in a similar field, I was required to learn how to work effectively with people from a variety of backgrounds – many of whom were non-technical.

It’s also common for there to be organizational and communication hurdles, such as aligning what is technically possible with business objectives; management might want to optimize a process, but the available dataset might be insufficient to get there.

Managing expectations from others is important; some have the idea that machine learning can solve everything. To navigate these challenges, it’s important to prioritize clear and concise communication, focusing on storytelling techniques to convey the significance of data insights. Developing strong interdisciplinary collaboration, cultivating domain expertise, and actively engaging with other team members throughout the project lifecycle can help ensure alignment with organizational goals.

Besides developing the necessary soft skills, technical challenges can pose additional hurdles. One common challenge is debugging code, especially when dealing with complex algorithms or integrating multiple libraries and frameworks. To overcome this challenge, it’s necessary to adopt systematic debugging practices, such as using print() statements and the logging and debugging tools available in many integrated development environments (IDEs). We go into more detail on this in 4 Best Python IDE and Code Editors. Additionally, making use of online forums and community resources such as Stack Overflow can provide new perspectives into solving challenging technical issues.

Handling large datasets is another prevalent challenge in data science, particularly in terms of memory management and processing speed. To address this, techniques like data sampling, parallel processing, and distributed computing frameworks can be used. Optimizing code efficiency and minimizing memory usage is a critical factor in many applications, e.g. when processing large numbers of images or videos.

What’s Next in Your Python and Data Science Path?

Embarking on a journey in data science with Python opens doors to endless possibilities and opportunities for growth and innovation. Python’s rich ecosystem of libraries, tools, and community support provides a solid foundation for data scientists to tackle complex challenges and make meaningful contributions across diverse domains. A good foundation in Python will make you not only proficient in working with data but also a solid Python developer.

As you continue your journey, remember to embrace curiosity and a growth mindset. Constantly seek new resources to get extra practice in Python and use courses and documentation to expand your knowledge and skills. There are some great books to help you learn. Dive into online communities, forums, and meetups to connect with fellow data enthusiasts, exchange ideas, and collaborate on projects. We discuss these topics in How to Master Python: A Guide for Beginners.

Don’t hesitate to explore specialized areas within data science – such as machine learning, natural language processing, and deep learning – to deepen your expertise and stay at the forefront of innovation.

Whether you’re a seasoned practitioner or just starting out, your journey in data science with Python will open the door to learning opportunities, impactful discoveries, and diverse career paths. So, keep coding and exploring what you can achieve with Python and data science.

Data Science и Python

Data Science with Python

Python стал основным языком программирования для профессионалов в области науки о данных по всему миру. Его простота и читаемость в сочетании с мощными доступными библиотеками делают его отличным выбором для анализа данных, машинного обучения и многого другого. Универсальность Python позволяет использовать его в широком спектре приложений, от простых задач манипулирования данными до сложных проектов глубокого обучения.

Сравнение с Другими Языками Программирования

Хотя языки, такие как R, MATLAB и Julia, также популярны в сообществе специалистов по данным, Python выделяется благодаря своей легкости в изучении и широкому распространению в индустрии разработки программного обеспечения. Это привело к созданию богатой экосистемы библиотек и инструментов, специально адаптированных для задач науки о данных. Кроме того, возможности интеграции Python с другими языками и инструментами делают его универсальным выбором для сложных проектов.

Библиотеки Python для Науки о Данных

Сила Python лежит в его огромном массиве библиотек, которые обслуживают различные аспекты науки о данных. Ключевые библиотеки включают в себя:

  • NumPy: Необходим для манипуляции числовыми данными и операций.
  • pandas: Предоставляет мощные структуры данных и функции для эффективной манипуляции данными и анализа.
  • Matplotlib и Seaborn: Широко используются для создания статичных, интерактивных и эстетически приятных визуализаций.
  • Scikit-learn: Обширная библиотека для машинного обучения, предлагающая широкий спектр алгоритмов для классификации, регрессии, кластеризации и многого другого.

Эти библиотеки являются основой большинства проектов науки о данных. Например, pandas обычно используется для очистки и подготовки данных, NumPy для операций с числовыми данными, Matplotlib и Seaborn для визуализации данных, а Scikit-learn для реализации моделей машинного обучения.

Манипулирование Данными и Анализ с Использованием Python

Очистка и подготовка данных являются критически важными шагами в любом проекте науки о данных. Pandas предлагает функции для обработки отсутствующих данных, объединения наборов данных и преобразования типов данных, которые необходимы для создания чистого набора данных, готового к анализу.

Статистический Анализ и Методы Исследования Данных

Python, особенно с помощью pandas и библиотек, таких как SciPy, поддерживает широкий спектр методов статистического анализа и исследования данных. Это включает в себя суммирование, анализ корреляции, проверку гипотез и многое другое, что необходимо для понимания основных закономерностей в данных.

Машинное Обучение с Python

Машинное обучение является ключевым аспектом науки о данных, и библиотеки Python, особенно Scikit-learn, обеспечивают поддержку широкого спектра алгоритмов машинного обучения. Эти библиотеки предлагают инструменты для предварительной обработки данных, выбора моделей, кросс-валидации и настройки параметров, что упрощает разработку надежных моделей машинного обучения.

Примеры Реальных Проектов Машинного Обучения, Реализованных на Python

Существует множество примеров успешных проектов машинного обучения, реализованных на Python, от прогнозной аналитики в здравоохранении до рекомендательных систем в электронной коммерции. Эти примеры подчеркивают гибкость и мощь Python в решении проблем реального мира.

Продвинутые Приложения и Будущие Тенденции

Python находится на передовой продвинутых приложений науки о данных, с библиотеками, такими как TensorFlow и PyTorch для глубокого обучения, NLTK и spaCy для обработки естественного языка и PySpark для аналитики больших данных. Эти инструменты открывают новые возможности в таких областях, как компьютерное зрение, распознавание речи и анализ больших объемов данных.

Будущие Тенденции в Науке о Данных и Эволюция Роли Python

Область науки о данных постоянно развивается, с появлением таких тенденций, как автоматизированное машинное обучение (AutoML), интерпретируемый ИИ (XAI) и вычисления на краю. Адаптируемость Python и активное сообщество вокруг него гарантируют, что он будет продолжать играть ключевую роль в будущем науки о данных, принимая новые технологии и методологии.

Python for Data Science: A Learning Roadmap

Python for Data Science

Python is the language of choice for most of the data science community. This article is a road map to learning Python for Data Science. It’s suitable for starting data scientists and for those already there who want to learn more about using Python for data science.

We’ll fly by all the essential elements data scientists use while providing links to more thorough explanations. This way, you can skip the stuff you already know and dive right into what you don’t know. Along the way, I’ll guide you to the essential Python packages used by the data science community.

I recommend you bookmark this page to return to it easily. And last but not least: this page is a continuous work in progress. I’ll be adding content and links, and I’d love to get your feedback too. So if you find something you think belongs here along your journey, don’t hesitate to message me.

Table of Contents

  • 1 What is Data Science?
  • 2 Learn Python
  • 3 Learn the command-line
  • 4 A Data Science Working environment
  • 5 Reading data
  • 6 Crunching data
  • 7 Visualization
  • 8 Keep learning

What is Data Science?

Before we start, though, I’d like to describe what I see as data science more formally. While I assume you have a general idea of what data science is, it’s still a good idea to define it more specifically. It’ll also help us define a clear learning path.

As you may know, giving a single, all-encompassing definition of a data scientist is hard. If we ask ten people, I’m sure it will result in at least eleven definitions of data science. So here’s my take on it.

Working with data

To be a data scientist means knowing a lot about several areas. But first and foremost, you have to get comfortable with data. What kinds of data are there, how can it be stored, and how can it be retrieved? Is it real-time data or historical data? Can it be queried with SQL? Is it text, images, video, or a combination of these?

How you manage and process your data depends on a number of properties or qualities that allow us to describe it more accurately. These are also called the five V’s of data:

  • Volume: how much data is there?
  • Velocity: how quickly is the data flowing? What is its timeliness (e.g., is it real-time data?)
  • Variety: are there different types and data sources, or just one type?
  • Veracity: the data quality; is it complete, is it easy to parse, is it a steady stream?
  • Value: at the end of all your processing, what value does the data bring to the table? Think of useful insights for management.

Although you’ll hear about these five V’s more often in the world of data engineering and big data, I strongly believe that they apply to all of the areas of expertise and are a nice way of looking at data.

Programming / scripting

In order to read, process, and store data, you need to have basic programming skills. You don’t need to be a software engineer, and you probably don’t need to know about software design, but you do need a certain level of scripting skills.

There are fantastic libraries and tools out there for data scientists. For many data science jobs, all you need to do is combine the right tools and libraries. However, you need to know one or more programming languages to do so. Python has proven itself to be an ideal language for data science for several reasons:

  • It’s easy to learn
  • You can use it both interactively and in the form of scripts
  • There are (literally) tons of useful libraries out there

There’s a reason the data science community has embraced Python initially. During the past years, however, many new super-useful Python libraries came out specifically for data science.

Math and statistics

As if the above skills aren’t hard enough on their own, you also need a fairly good knowledge of math, statistics, and working scientifically.

Visualization

Eventually, you want to present your results to your team, manager, or world! For that, you’ll need to visualize your results. You need to know about creating basic graphs, pie charts, histograms, and potting data on a map.

Expert knowledge

Each working field has or requires:

  • specific terminology,
  • its own rules and regulations,
  • expert knowledge.

Generally, you’ll need to dive into what makes a field what it is. You can’t analyze data from a specific field of expertise without understanding the basic terminology and rules.

So what is a data scientist?

Coming back to our original question: what is data science? Or: what makes someone a data scientist? You need at least basic skills in all the subject areas named above. Every data scientist will have different levels of these skills. You can be strong in one, and weak in another. That’s OK.

For example, if you come from a math background, you’ll be great at the math part, but perhaps you’ll have a hard time wrestling with the data initially. On the other hand, some data scientists come from the AI/machine learning world and will tend toward that part of the job and less toward other parts. It doesn’t matter too much: ultimately, we all need to learn and fill in the gaps. The differences are what make this field exciting and full of learning opportunities!

Learn Python

The first stop when you want to use Python for Data Science: learning Python. If you’re completely new to Python, start learning the language itself first:

  • Start with my free Python tutorial or the premium Python for Beginners course
  • Check out our Python learning resources page for books and other useful websites

Learn the command-line

It helps a lot if you are comfortable on the command line. It’s one of those things you have to get started with and get used to. Once you do, you’ll find that you use it more and more since it is so much more efficient than using GUIs for everything. Using the command line will make you a much more versatile computer user, and you’ll quickly discover that some command-line tools can do what would otherwise be a big, ugly script and a full day of work.

The good news: it’s not as hard as you might think. We have a fairly extensive chapter on this site about using the Unix command line, the basic shell commands you need to know, creating shell scripts, and even Bash multiprocessing! I strongly recommend you check it out.

A Data Science Working environment

There are roughly two ways of using Python for Data Science:

Jupyter Lab interactive notebook example

  1. Creating and running scripts
  2. Using an interactive shell, like a REPL or a notebook

Interactive notebooks have become extremely popular within the data science community, but you should certainly not rule out the power of a simple Python script to do some grunt work. Both have their place.

Check out our detailed article about the advantage of Jupyter Notebook. You’ll learn about the advantages of using it for data science, how it works, and how to install it. There, you’ll also learn when a notebook is a right choice and when you’re better off writing a script.

Reading data

There are many ways to get the data you need to analyze. We’ll quickly go over the most common ways of getting data, and I’ll point you to some of the best libraries to get the job done.

Data from local files

Often, the data will be stored on a file system, so you need to be able to open and read files with Python. If the data is formatted in JSON, you need a Python JSON parser. Python can do this natively. If you need to read YAML data, there’s a Python YAML parser as well.

Data from an API

Data will often be offered to you through a REST API. In the world of Python, one of the most used and most user-friendly libraries to fetch data over HTTP is called Requests. With requests, fetching data from an API can be as simple as this:

>>> import requests >>> data = requests.get('https://some-weather-service.example/api/historic/2020-04-06') >>> data.json() []

This is the absolute basic use-case, but requests has you covered too when you need to POST data, when you need to login to an API, etcetera. There will be plenty of examples on the Requests website itself and on sites like StackOverflow.

Scraping data from the World Wide Web

Sometimes, data is not available through an easy-to-parse API but only from a website. If the data is only available from a website, you will need to retrieve it from the raw HTML and JavaScript. Doing this is called scraping, and it can be hard. But like with everything, the Python ecosystem has you covered!

Before you consider scraping data, you need to realize a few things, though:

  • A website’s structure can change without notice. There are no guarantees, so your scraper can break at any time.
  • Not all websites allow you to scrape them. Some websites will actively try to detect scrapers and block them.
  • Even if a website allows scraping (or doesn’t care), you are responsible for doing so in an orderly fashion. It’s not difficult to take down a site with a simple Python script just by making many requests in a short time span. Please realize that you might break the law by doing so. A less extreme outcome is that your IP address will be banned for life on that website (and possibly on other sites as well)
  • Most websites offer a robots.txt file. You should respect such a file.

Good scrapers will have options to limit the so-called crawl rate and will have the option to respect robots.txt files too. In theory, you can create your own scraper with, for example, the Requests library, but I strongly recommend against it. It’s a lot of work, and it’s easy to mess up and get banned.

Instead, you should look at Scrapy, which is a mature, easy-to-use library to build a high-quality web scraper.

Crunching data

One of the reasons why Python is so popular for Data Science are the following two libraries:

  1. NumPy: “The fundamental package for scientific computing with Python.”
  2. Pandas: “a fast, powerful, flexible, and easy-to-use open-source data analysis and manipulation tool.”

Let’s look at these two in a little more detail!

NumPy

NumPy’s strength lies in working with arrays of data. These can be one-dimensional arrays, multi-dimensional arrays, and matrices. NumPy also offers a lot of mathematical operations that can be applied to these data structures.

NumPy’s core functionality is mostly implemented in C, making it very, very fast compared to regular Python code. Hence, Aas long as you use NumPy arrays and operations, your code can be as fast or faster than someone doing the same operations in a fast and compiled language. You can learn more in my introduction to NumPy.

Pandas

Like NumPy, Pandas offers us ways to work with in-memory data efficiently. Both libraries have an overlap in functionality. An important distinction is that Pandas offers us something called DataFrames. DataFrames are comparable to how a spreadsheet works, and you might know data frames from other languages, like R.

Pandas is the right tool for you when working with tabular data, such as data stored in spreadsheets or databases. pandas will help you to explore, clean, and process your data.

Visualization

Every Python data scientist needs to visualize his or her results at some point, and there are many ways to visualize your work with Python. However, if I were allowed to recommend only one library, it would be a relatively new one: Streamlit.

Streamlit

Streamlit is so powerful that it deserves a separate article to demonstrate what it has to offer. But to summarize: Streamlit allows you to turn any script into a full-blown, interactive web application without the need to know HTML, CSS, and JavaScript. All that with just a few lines of code. It’s truly powerful; go read about Streamlit!

Streamlit uses many well-known packages internally. You can always opt to use those instead, but Streamlit makes using them a lot easier. Another cool feature of Streamlit is that most figures and tables allow you to easily export them to an image or CSV file as well.

Dash

Another more mature product is Dash. Like Streamlit, it allows you to create and host web apps to visualize data quickly. To get an idea of what Dash can do, head to their documentation.

Keep learning

You can read the book ‘Python for Data Science’ by Jake Vanderplas for free right here. The book is from 2016, so it’s a bit dated. For example, at the time, Streamlit didn’t exist. Also, the book explains IPython, which is at the core of what is now Jupyter Notebook. The functionality is mostly the same, so it’s still useful.

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