Back to articles list Articles
11 minutes read

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.