Who Are Data Scientists and What Do They Use Python For?

Are you wondering if a data science career is a good fit for you? In this article, I will try to explain what data science is and who data science specialists are. Check out what skills you need to become one of them – including Python.

I have been observing a huge interest in data science for some time. Online forums and social media are constantly inundated with all kinds of information and questions on this topic. People want to know what data science really is, how to enter this world, and whether it pays off at all. What do you need to know? I will try to answer these questions below.

Let's start with the basics.

What Is Data Science?

Data science is about extracting actionable insights from data by applying a combination of tools from statistics and computer science. Data scientists use data to answer a variety of business questions. Which distribution channels are more effective? Which customers are likely to stop using your company’s products or services within the next year? How do you retain these customers?

To answer these kinds of questions, a data scientist usually takes a long journey starting with data collection and cleaning, moving to developing the models, interpreting the results, and finally, presenting these results to business leaders. Successful data scientists:

• understand the domain they are working in so that they can define the data requirements and possible approaches for addressing specific business problems.
• have good communication skills to understand the business questions and express clearly how data science may assist with these questions.
• know the most effective approaches to data collections and cleaning.
• have expertise in machine learning (ML), statistics, and programming tools (Python, R) to build, train, and evaluate models that address specific business questions.
• know how to interpret the results of the model developed.
• have good presentation skills to explain these results to business leaders.

Are you already excited about the power of data science? We will now go through the many benefits of a data science career.

Why Choose a Data Science Career?

It is not without reason that the role in data science has become so popular all around the world. Here are some of the most obvious advantages of a career in data science:

1. Data scientists are usually highly paid. Glassdoor research based on an anonymous survey of almost 16K data scientists shows that the average base pay of a data scientist in the U.S. is $114.5K a year. Similarly, Indeed reports the average salary for this role at $120K per year. What’s even more exciting is that even the entry-level data scientists with less than 1 year of experience get around $101.7K per year in the United States. Not many jobs offer such a high salary at the very beginning of a career.
2. Data science is in demand. Despite all the buzz around data science jobs and a significant growth in the number of data scientists, the supply is still scarce considering the existing demand. According to the S. Bureau of Labor Statistics, the employment of computer and information research scientists is projected to grow by 15% from 2019 to 2029, much faster than the average of all occupations (4%). This trend is likely to be relevant for other countries as well.
3. Job tasks in data science are versatile. Data scientists usually encounter interesting and diverse business problems to solve. One day you may be working on customer churn prediction, and on another day you can be moved to a team developing a recommender system.
4. Data scientists can choose an industry to work in. Companies across different sectors and industries are looking for data scientists. You may choose to work in healthcare, e-commerce, marketing, or banking. Furthermore, if you work as a freelancer, you can pursue several projects in different industries.

Check out this guide for some good recommendations on finding a data science job or any Python-related job.

Wondering if you have the required qualifications? Let’s find out!

What to Learn to Become a Data Scientist

Now that you know what data scientists do, you can probably guess what kind of skills are required for this role. Let’s summarize these together. The skills needed by a data scientist are:

1. Mathematics

All the math is done by computers these days. However, to be effective as a data scientist, you need to be good at mathematics. You should know how to perform vector and matrix operations, understand probabilities well, and have proficiency in statistics. Computers do the calculations, but data scientists build models and interpret the results, and this is where mathematics and statistics knowledge is crucial.

2. Computer Science

Some believe that a data scientist is just a fancy new name for a statistician. For me, these are two distinct roles, with a key difference in how these two professions use technology. Statisticians focus on research with significance testing, diagnostics plots, and time-series analyses. They use software packages like SAS or SPSS as tools to streamline model building and calculations.

In contrast, data scientists are native to technology. Their work is usually automated as much as possible. They use SQL querying and different Python libraries to automate the data collection process. Then, they use Python or R to visualize data with just a few lines of code. Data scientists can build machine learning models from scratch using Python, or they can use one of the many Python libraries for data science to make model building even more efficient. Finally, the models built by data scientists can be deployed, for example into a web application, using various software engineering tools. Therefore, Python skills are key to the data science profession.

3. Domain expertise

As a data scientist, you need to have a deep understanding of the industry in which you are working. You cannot effectively analyze the distribution channels of an online shop without understanding how the e-commerce sector works. You cannot provide useful recommendations for optimizing the construction process if you don’t know how the industry operates. Of course, all data science projects should be carried out in close cooperation with a business function that can provide the necessary domain expertise. However, it is still important for a data scientist to have some understanding of the industry – at the minimum, you should be able to ask the right questions.

4. Communication

Never overlook the importance of communication for a data scientist. To become one, you need to be good at listening and telling stories. You need to listen to business leaders to understand their problems. You should be able to communicate clearly how data science can help address their business needs. Even though quite powerful, data science is not a magic pill to solve all business problems, and it’s the data scientist’s responsibility to build realistic expectations.

Finally, once you have the results from your models, you need to communicate these results to business leaders. You cannot just send them a bunch of tables and graphs. Instead, you should translate your results into some actionable insights.

To summarize, a successful data scientist combines the skills of a statistician, a software engineer, and a business analyst. Indeed, a strong set of hard skills and soft skills are required to succeed in this field.

But where do you start? Let’s start with Python.

Why Is Python a Key Tool in Data Science?

The relationship between Python and data science is mutually beneficial. Data science has contributed significantly to Python’s booming popularity in recent years. On the other hand, Python facilitates the process of learning data science.

Python is a general-purpose, high-level programming language known for its code readability, productivity, and accessibility to programming newbies. Data scientists usually choose Python as their key tool for a reason:

• Python is easy to learn, read, and write. Due to its English-like syntax, Python is really easy to pick up and learn. A couple of weeks might be enough to learn how to process data and build models in Python. This holds even if you have zero programming background. Start with this Python for Data Science mini-track and see for yourself how accessible Python is.
• There are numerous open-source Python libraries supporting data science tasks. These packages allow you to process your data, create advanced data visualizations, and build complex machine learning models with just a few lines of code. For example, there is the Numpy library for handling multi-dimensional arrays and matrices, Pandas for data manipulation and analysis, Matplotlib for data visualization, and scikit-learn for building machine learning models. Learn about the top 15 libraries for data science here.
• Python-built models can be smoothly deployed into production. In business, you usually expect your data science models to be used in production. Python is very well suited for handling model deployment and support. Models built with Python are production-ready in contrast to models built with R, another popular programming language for data science but more research-oriented.

Read this article to learn about other advantages of using Python for data science.

How to Learn Python for Data Science

Are you ready to embark on your Python journey? Start today with the interactive Python for Data Science mini-track that lays the programming foundations needed for working in the field of data science. Here are the courses included in this track:

• Introduction to Python for Data Science (141 coding challenges): covers simple data visualizations and data analyses, basic calculations, variable creation and manipulation, and working with data frames in Python.
• Working with Strings in Python (57 coding challenges): covers joining, iterating, and slicing strings, formatting string values in Python, and using popular string functions.
• How to Read and Write JSON Files in Python (35 coding challenges): covers everything you need to know to work with data stored in JSON format (i.e., opening, reading, and writing JSON files).
• How to Read and Write CSV Files in Python (51 coding challenges): covers all the necessary basics to process data stored in CSV format, arguably one of the most popular data formats in data science.
• How to Read and Write Excel Files in Python (45 coding challenges): explains how to read Excel files with openpyxl and how to process them in for loops. You’ll also learn how to create Excel files and modify their content in Python.

After completing this mini-track, you will be able to write simple data processing scripts and build basic data visualizations. This would be a great start for a successful data science career! Even if you decide to pursue a different career, this track would be a good entry into the world of IT.

Are you Ready to Become a Data Scientist?

You now have a clear career path and know how to become a data scientist. Start by learning to use Python and keep going. I believe you will achieve your goals. See you in class!