Why Learn Python for Data Science?

Have you explored everything Python for data science can do?

If your career is based in data science, Python will most likely be one of your most important tools. According to Forbes, Python is the preferred programming language of data science, and tech professionals of all types know Python as easy to learn, powerful, and versatile.

Here, you’ll learn more about the types of careers that benefit from Python for data science and how it can enhance your professional life. Regardless of your overall goals, learning Python for data science is a valuable and impressive skill to add to your resume. 

What is Python for Data Science?

Python is among the most popular programming languages in the world, and many tech professionals learn it before moving on to other languages. According to leading publications, data science and machine learning pros consider Python their go-to programming language. Python is an essential skill for many development and data science roles, including:

• Data Scientist
• Data Engineer
• Software Engineer
• Data Analyst
• Python Developer

Artificial intelligence (AI) and machine learning (ML) are areas where Python for data science rules the roost. Building ML models and applying ML algorithms typically includes libraries like Scikit Learn or PyBrain. Data analysis requires Python libraries like Pandas and NumPy. And visualization with Matplotlib or Seaborn is popular in Python for data science. 

Read more about what Python is and why you should learn it for data science. 

What Can You Do with Python for Data Science?

Python is advantageous for data science professionals of all kinds. Its ease of use and scalability make it the top choice for Data Scientists, Data Engineers, and Data Analysts in virtually every sector of the economy.

Because Python is both easy to learn and powerful, its libraries and frameworks can be ideal for dealing with mathematical functions, data structures, and visualization. Here are some of the most common uses for Python in data science.

• Data Analysis - Python is easy to read and write, so it’s commonly used for complex data analysis—particularly handling large datasets. Top Python libraries for data analysis include:

• NumPy
• Pandas
• SciPy

• Data Visualization - Data science often requires visualization tools. Data professionals use charts, graphs, and even maps to present data in easy-to-digest ways. Top Python libraries for data visualization include:

• Matplotlib
• Plotly
• Seaborn

• Artificial Intelligence and Machine Learning - Machine learning, or ML, is a subset of artificial intelligence (AI). Data science pros use ML libraries like Scikit Learn for data classification and linear regression. Top Python libraries for AI and ML include:

• Scikit Learn
• PyBrain
• TensorFlow

Common Professional Uses for Python for Data Science

Python for data science is ubiquitous today, with virtually every industry using it where possible. According to the Bureau of Labor Statistics, industries with the highest level of Data Scientist employment include:

• Computer Systems Design and Related Services
• Management of Companies and Enterprises
• Management, Scientific, and Technical Consulting Services
• Scientific Research and Development Services
• Credit Intermediation and Related Activities

These categories illustrate several things.

First, companies need Python data science expertise at the highest levels, including management positions like Senior Engineer, Tech Lead, and Product Manager. Second, C-suite executives need data science expertise at their fingertips from trusted managers. And third, science is only one area where data science skills are needed, while business and finance, along with high tech, lead the pack.

A complete list of what industries use Python for data science and for what tasks is beyond the scope of this article. Some of the most well-known examples include:

• Healthcare imaging analysis
• Speech or facial recognition
• Drug development in the pharmaceutical industry

Targeted Search and Advertising

Targeted search and advertising encompass a wide range of aspects of data science. If you’ve ever used a tool like Amazon’s Top Picks For You, Netflix’s Because You Watched recommendation engine, or Apple Music’s Stations for You playlists, you’ve been on the user end of targeted advertising. Companies use machine learning algorithms to predict future decisions based on past behavior, often with astonishingly accurate results. Google’s search engine is among the most well-known of them all, but many companies use search to assist customers as they seek answers to questions or products to solve challenges.

Targeted advertising casts a broader net than search, as it looks to sell products or services rather than answer informational questions. Python for data science helps here, too: predictive analytics can find patterns and determine the likelihood of outcomes. Sales, marketing, ecommerce, and traditional retail can all benefit from machine learning algorithms, a trend likely to continue growing in the near future.

To learn more about ML models, check out a course like Noble Desktop’s Data Science Certificate or Python Machine Learning Bootcamp.

Virtual Assistants and Other AI Customer Service Tools

We’ve all seen and heard them: the chatbots and virtual assistants companies use to engage customers in human-like conversations. To avoid confusion, let’s consider the names of some of these tools, which are nearly always powered by artificial intelligence (AI).

• Virtual Assistants - Sometimes designated as Virtual Agents, Intelligent Virtual Assistants, or Intelligent Personal Assistants, Virtual Assistants offer a chat-like interface, typically by phone, laptop, or desktop.

• Chatbots - More often used via web-based applications or messaging channels, chatbots usually provide a conversational user interface without necessarily responding directly to voice commands. Chatbots may be rule-based, textual, or intellectually independent.

Whether you find yourself interacting with a chatbot or a VA, the technology behind either includes machine learning (ML) and natural language processing (NLP).

Fraud and Risk Detection

As banking and finance increasingly go online, the risks associated with fraud and identity theft rise along with them. Ecommerce, mobile banking, and rewards-based apps—like those Starbucks and other vendors offer—can be subject to hacking.

That’s where Python for data science comes in. Privacy and data protection go hand in hand, and companies must protect customers’ data more than ever. Identity theft, credit card skimming, and password protection are just a few of the top concerns for everyone from insurance companies to medical providers.

Two kinds of machine learning methods assist tech pros with fraud detection: supervised learning and unsupervised learning algorithms.

With supervised learning algorithms, a system is trained to find behavior patterns. Examples include:

• Linear regression
• Logistic regression
• Decision trees

Unsupervised learning algorithms also look for patterns or data groupings, but without labeled datasets. As a result, unsupervised learning algorithms can perform more complex tasks. Examples of unsupervised learning include:

• K-means clustering in Python
• Association rule
• Principal component analysis (PCA)

To learn more about Python for data science as it pertains to cybersecurity, fraud, and risk detection, check out the following courses from Noble Desktop:

• Python for Network Security - This course covers Python programming, including network security and cryptography. Applicants should be familiar with Linux operating systems and bash scripting. See course listings for more detailed prerequisite information.

• Cybersecurity with Python - This bootcamp-style course trains students to solve common IT problems and automate tasks with Python. Students can save by taking the class as part of Noble’s Cybersecurity Bootcamp. See course listings for prerequisite information.

• Offensive Security with Python - Participants in this course learn to go on offense against cyber threats. Topics include testing network vulnerabilities with Python and Linux, executing penetration tests, and cyberattack prevention strategies. Applicants should have knowledge comparable to that gained in the Python Programming Bootcamp. 

How to Start Learning Python for Data Science

Python for data science novices often find it challenging when they try to determine the best way to master the popular programming language. Whereas busy professionals may already have some data science skills, students beginning their careers may want to get in-depth training in a concentrated time frame.

Fortunately, Python data science students can find many educational options, both in-person and live online. Bootcamps, certificate programs, and self-paced classes provide information and, in many cases, hands-on experience. Consider the following:

• Live Coursework - Most Python for data science training students prefer to learn in person or live online through teleconferencing. You can use Noble Desktop’s Classes Near Me tool to compare and contrast all the options, or look for live online courses if you’ve already narrowed your choices to virtual training alternatives.

• Self-Paced Programs - On-demand or self-paced courses can be helpful if you’re starting out with Python for data science. Some offer training at the beginner level, whereas others require intermediate skills. Check out options like Exchanging Excel for Python or Making a Twitter Bot in Python for more information.

• Seminars, Tutorials, and Articles - If you want to learn a few things about Python for data science before you commit to formal training, check out some free resources first. Noble Desktop’s YouTube channel includes a playlist of Python data science tutorials and webinars. You can also find helpful Python and data science training in the Free Seminars section of Noble’s website.

Read the full guide on how to learn Python for data science.

Learn Python for Data Science with Hands-on Training at Noble Desktop

Because Python for data science involves two potentially different disciplines—Python programming and the broader data science field—not every student approaches it the same way. How and where you plan to use the knowledge you gain from Python for data science training may dictate your approach.

Noble Desktop offers multiple avenues to learn data science. Their Data Science Certificate includes Python programming fundamentals, machine learning, SQL to query databases, and plotting and dashboard libraries. This program prepares attendees for entry-level positions in data science and Python engineering.

Another option is Noble’s Python for Data Science Bootcamp. A hands-on 30-hour course, the bootcamp includes training in Numpy, Pandas, Matplotlib, and linear regression. Students can save by taking the Python for Data Science Bootcamp as part of the Data Science Certificate program as well.

If you prefer to peruse all the Python for data science training Noble Desktop offers, check out the Python Classes page. Here you’ll find bootcamps and certificate programs as well as shorter courses. Top certificate programs include:

• Data Science Certificate
• Software Engineering Certificate
• Data Analytics Certificate

Popular bootcamp options include:

• Python for Data Science Bootcamp
• Python Programming Bootcamp
• FinTech Bootcamp
• Cybersecurity Bootcamp

Other training options include:

• Python for Automation
• Cybersecurity with Python
• Python for Network Security

Noble Desktop’s bootcamps and certificate programs earn high marks from graduates. They are available live online or in-person in New York City. Additional perks include a verified Certificate of Completion and free retakes within a year after graduation. Many certificates and bootcamps also include 1-on-1 mentoring: check course descriptions for more information, including any prerequisites.

Key Insights

• Python is among the most popular programming languages in the world.

• Python for data science roles include:

• Data Scientist
• Data Engineer
• Software Engineer
• Data Analyst

• Top Python libraries for data analysis include:

• NumPy
• Pandas
• SciPy

• Top Python libraries for data visualization include:

• Matplotlib
• Plotly
• Seaborn

• Top Python libraries for artificial intelligence (AI) and machine learning (ML) include:

• Scikit Learn
• PyBrain
• TensorFlow

• According to the Bureau of Labor Statistics, industries with the highest level of data science employment include:

• Computer Systems Design and Related Services
• Management of Companies and Enterprises
• Management, Scientific, and Technical Consulting Services
• Scientific Research and Development Services
• Credit Intermediation and Related Activities

• Best-known examples of Python for data science use include:

• Healthcare imaging analysis
• Speech or facial recognition
• Drug development in the pharmaceutical industry
• Targeted search and advertising
• Fraud and risk detection

• Examples of supervised learning algorithms include:

• Linear regression
• Logistic regression
• Decision trees

• Examples of supervised learning algorithms include:

• K-means clustering in Python
• Association rule
• Principal component analysis (PCA)

• You can receive comprehensive Python for data science training through an in-person or live online course with Noble Desktop. Among their most popular options are:

• Python for Data Science Bootcamp
• Data Science Certificate
• FinTech Bootcamp