How Long Does it Take to Learn Python for Data Science?

Like many aspiring tech professionals, you might want to learn Python for data science but worry that it will take too much time. While the field is complex, most students can learn Python for data science fundamentals in about six months.

Of course, this depends on several factors. Keep reading to learn about how you can learn Python for data science and some resources to help speed the process along.

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

Average Time it Takes to Learn Python for Data Science

Because Python for data science is a wide-ranging field, the amount of time it takes to learn depends on factors like your current level of programming proficiency, goals, and availability. Estimates for mastering data science range from six months to several years. However, you may be able to learn Python fundamentals in a few months—even less if you study full-time.

Another aspect affecting the amount of time required is the kind of training you select. Self-paced tutorials can provide meaningful information in a short time frame, but if you’re starting with no experience, you’ll most likely need six to nine months to get comfortable enough with Python to code something new on your own.

Check out Noble Desktop’s guide to learning Python in the Learn Hub.

Other Factors

If you’re studying Python for data science, the education you need also depends on how and where you’ll use the knowledge you gain. For example, a Business Analyst will not require the same training as a Software Engineer or Data Scientist. Consider the following additional factors.

Current Level of Expertise

Your prior experience with related or complementary topics is the most obvious factor in the time it takes to learn Python for data science. If you come to training with some Python programming experience, or even another language like Java or JavaScript, that will help you learn faster.

Still, the fact remains that Python is one of the easiest languages to learn, especially for those new to programming. Chances are good that you can start a beginner-friendly class and succeed, whether you have some programming experience or come to class with no Python knowledge.

Availability

Scheduling can be challenging for anyone adding Python for data science training to an already busy calendar. Are you enrolling in an immersive Python for data science bootcamp or certificate? These programs can take a few weeks or several months, and many are open either part-time or full-time. A full-time commitment may require students to attend eight hours a day—not an option for most professionals already working Monday through Friday.

Your speed of learning can also affect how long you take to train. If you start with free tutorials or seminars, these will add time, although they may help you get oriented to the subject.

Goals

Of all possible factors, your ultimate goal in learning Python for data science is the most important. For this reason, you should have a solid plan in place for what you’ll do after you learn this essential skill set.

If you’re planning to seek an entry-level role as a Financial Analyst or Data Analyst, you may be able to get all the training needed in several months on a part-time basis. However, if your ambition is that of a Data Scientist or Machine Learning Engineer, you’ll most likely need more. Check out courses like Noble Desktop’s Python for Data Science Bootcamp or Data Science Certificate program.

Level of Difficulty, Prerequisites, & Cost 

You may think Python for data science will be challenging to learn, particularly if you have no coding experience. However, many students are surprised to learn that Python is the most popular programming language among data science professionals and one of the easiest to master. Python’s open-source license means an entire universe of libraries can be used free of charge, and its worldwide support community is always a mouse click away.

Before you start learning Python for data science, you should have a plan for how and where you’ll apply the knowledge you gain. Python programming fundamentals for a Data Analyst may differ from those of a Software Engineer. You should have basic computer skills, but you can use Python on Mac OS, Linux, or Windows.

You can download Python’s libraries and frameworks for free and find many online Python seminars and tutorials at no cost. However, you’ll eventually want to enroll in formal, paid training. If you learn Python as part of a broader data science curriculum, your approach will be different than if you want to focus on Python in a narrower sense.

Read more about how difficult it is to learn Python for data science.

Watch a Free Python for Data Science Online Course

Those not yet ready to dive into a full-scale bootcamp or certificate program can still get an overview of Python for data science. Start learning Python for data science online for free. In this introductory course, you’ll learn fundamentals like:

• How to install Python using Anaconda
• Numeric data types
• Integers
• Pseudocodes
• Variable names
• Best practices

Additional free classes include Data Processing Using Python from Nanjing University, Data Science Math skills from Duke University, and the University of London’s Foundations of Data Science: K-Means Clustering in Python.

Read about more free Python and data science videos and online tutorials.

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 for data science is a wide-ranging field encompassing math, computer programming, and artificial intelligence (AI).

• Python for data science can take years to master, but most students can master fundamentals in about six months.

• Top Python for data science roles include:
• Data Scientist
• Data Engineer
• Software Engineer
• Business Analyst
• 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
• Important factors that influence how long it will take to learn Python for data science include:
  • Your current level of expertise
  • Availability
  • Goals
• You can receive comprehensive Python for data science training through an in-person or live online course with Noble Desktop.