What to Learn After Python for Data Science

Python for data science is among the most complex, wide-ranging fields in today’s market. It can include areas as diverse as cybersecurity, software engineering, and machine learning.

If you already know how to use Python for data science, you might consider expanding your knowledge of data visualization tools like Tableau, artificial intelligence (AI), or cybersecurity. We’ll cover each of these below so you have a clear understanding of what skill you want to tackle next. 

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

Data Visualization

In Python for data science, data mining, visualization, and communicating analysis with key stakeholders are parts of the process. It’s especially true for Data Scientists but can also apply to professionals with roles like Data Analyst, Business Analyst, and Financial Analyst.

Using Python visualization packages like Matplotlib or Seaborn, Python for data science pros gather information and present it in charts, bar graphs, or maps.

Data visualization tools have many uses, and Python for data science may use different options beyond those included with Python visualization packages. For example, Tableau or Power BI can help data science pros create more interactive visualizations, while Matplotlib and Pandas may suffice for other tasks.

If you learn Python for data science without much data visualization training, consider studying Tableau or Power BI—especially if you want to create and share visualizations that your co-workers can edit.

Artificial Intelligence & Machine Learning

If you learn Python for data science through a bootcamp or certificate, you will pick up some knowledge of machine learning (ML), the best-known subset of artificial intelligence (AI).

Machine learning is increasingly important to data science and analysis positions of all types, including:

• Business Analyst
• Data Analyst
• Data Engineer
• Data Scientist
• Financial Analyst
• Machine Learning Engineer
• Software Engineer

If you’re already familiar with Python and its data science libraries, you can get exposure to ML by enrolling in Noble Desktop’s Python Machine Learning Bootcamp. Students can also save by taking the Python Machine Learning Bootcamp as part of Noble’s Data Science Certificate program, which covers topics ranging from Python for data science to machine learning and data visualization.

Cybersecurity

Security might not be the first thing you think of when you hear the phrase Python for data science. However, you might be surprised to learn that many tech pros in cybersecurity positions study Python, data science and analysis tools, and other related subjects.

Top sectors where risk and fraud detection are a top concern in cybersecurity include:

• Banking & Finance
• Insurance
• Healthcare & Pharmaceuticals
• Retail
• Public administration

Top programs for aspiring cybersecurity pros include Noble Desktop programs like:

• Python for Network Security
• Cybersecurity with Python
• Cybersecurity Bootcamp
• Offensive Security with Python
• FinTech Bootcamp

For more information, check out all of Noble’s Cybersecurity classes.

Key Insights

• Python for data science encompasses diverse sectors like cybersecurity, software engineering, and machine learning.

• Top roles in Python for data science include:

• Business Analyst
• Data Analyst
• Data Engineer
• Data Scientist
• Financial Analyst
• Machine Learning Engineer
• Software Engineer

• Top Python libraries for data analysis include:

• NumPy
• Pandas
• SciPy

• Top Python libraries for data visualization include:

• Matplotlib
• Plotly
• Seaborn

• Top Python libraries for AI and ML include:

• Scikit Learn
• PyBrain
• TensorFlow

• Additional skills for Python for data science students include data visualization, cybersecurity, and machine learning.

• You can get comprehensive Python for data science training through an in-person or live online course from Noble Desktop. Top courses include:

• Python for Data Science Bootcamp
• Data Science Certificate
• Cybersecurity Bootcamp

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.