Python for Data Science Prerequisites

Anyone interested in learning Python for data science can start by gaining some of the skills needed in this multidisciplinary field. Different sectors require different kinds of training, and a Data Scientist role demands skills unlike those of a Software Engineer.

You might worry that Python for data science will be too hard to learn. This guide will help you understand the best methods for learning Python for data science and what you should study first to make the learning process easier. This way, you’ll be successful however you choose to apply your new skills. 

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

Is Python for Data Science Easy to Learn?

While it would be inaccurate to say Python for data science is easy, it’s also fair to call Python itself one of the most beginner-friendly languages to learn.

Tech professionals in the Python for data science category can include Data Analysts and Financial Analysts, as well as higher-paying positions like Data Scientist and Python Engineer. Beyond Python programming, entrants in the field should have a range of skills, including:

• Solid mathematical skills, especially in statistics.
• Training in artificial intelligence (AI), machine learning (ML), or natural language processing (NLP).
• Good written and oral communication skills.

A popular method many beginners in Python for data science take is enrolling in an intensive bootcamp or certificate program. These courses offer immersive training in a hands-on environment, and many entry-level positions require no training beyond that level.

Noble Desktop, which hosts the Learn Hub, offers programs like the Python for Data Science Bootcamp, Data Science Certificate, and Python Machine Learning Bootcamp. All provide in-depth training in Python and its libraries and frameworks. Check course listings for more details, including any prerequisite information.

What to Know Before Learning Python for Data Science

Python is crucial to both data science and web development. If you want to specialize in either of these wide-ranging fields, you’ll ultimately want to have intermediate to advanced Python skills. However, that doesn’t necessarily mean you need to be an expert in Python programming to qualify for an entry-level data science position.

One thing is certain: you should have solid computer skills, a strong aptitude for probability and statistics, and excellent communication skills. Consider each of these prerequisites individually.

Basic Computer Skills

You don’t need to be a computer expert to learn Python for data science. Experts agree that Python is one of the easiest programming languages to learn, but they generally presume that novice programmers will come to Python with essential computer skills. Consider the following as basics, including examples:

• Data entry
• Email management (Outlook, Gmail)
• Word processing software (MS Word)
• Spreadsheet software (MS Excel)
• Digital calendars

Today most new Python for data science students use their mobile phones for everything from ordering food to online banking. While you might not need smartphone expertise to learn Python, it will be helpful if you are comfortable on either a mobile platform or a desktop or laptop platform.

Aptitude for Probability & Statistics

Most tech pros come to Python for data science from a background in either math or science. As in the computer literacy example above, you don’t have to be a physicist or mathematician to start learning Python. However, a strong aptitude for math can help you quite a bit.

Python for data science roles may require in-depth business or finance knowledge. Consider positions like Data Analyst, Business Analyst, or Financial Analyst. Many people who gravitate toward careers of this type enjoyed their algebra, calculus, or statistics classes. If that's you, a Python for data science role might be ideal. 

Excellent Communication Skills

The stereotype of science and math professionals as nerds has been outdated for many years. Still, it’s fair to say that excellent communication skills might be more frequently associated with professionals who studied English, education, or marketing rather than science or math.

Before you take on a professional Python for data science role, however, you will benefit greatly by improving your communication skills. Especially in the corporate world, it’s essential to have the ability to communicate effectively with everyone from the new temp to C-suite executives—and this holds true for both written and oral communication. Consider joining groups like Business Networking International (BNI) or Toastmasters to improve in these areas while expanding your connections.

Key Insights

• Python for data science is a rapidly-growing field.

• Top roles in Python for data science include:
  • Data Engineer
  • Software Engineer
  • Data Scientist
  • 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 AI and ML include:
  • Scikit Learn
  • PyBrain
  • TensorFlow
• Before learning Python for data science, you should have:
  • Solid computer skills
  • An aptitude for probability and statistics
  • Excellent communication skills
• 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
  • Python Machine Learning 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.