How Difficult is it to Learn Python for Data Science?

Are you curious about learning Python for data science but worried that it might be too hard? Of course, the difficulty that comes with learning a new skill is somewhat subjective. The challenges of learning Python for data science depend on factors like your current programming skill set, the amount of time you can dedicate to learning, and the field or sector in which you work.

No matter your current schedule or comfort level with Python for data science, plenty of tools are available to help make learning easier than you might think.

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

What Are the Most Challenging Parts of Learning Python for Data Science?

Python has a near-universal reputation as one of the easiest programming languages in the world. Its open-source status means you can download and install its libraries and frameworks for free, and its massive international support community is only a mouse click away.

Of course, it can always be a challenge to learn something new. Here are some potentially challenging parts of learning Python for data science and how to overcome them.

• Planning how to apply Python for data science - If you come to Python for data science with a specific goal, this can help you learn faster. Data science is a vast, complex field; learning Python is only one aspect for most professionals.

• Communicating with key stakeholders - For many Data Scientists and other tech pros, Python’s main value is mining data, determining what it means, then conveying information to managers or C-suite executives. Beginners don’t need to worry about this factor, but it’s essential to keep it in mind long-term.

• Tools for industry - Python and its accompanying libraries and frameworks work alongside other tools, and the industry or sector involved will affect which ones to use and how. You may need to learn Python as part of a broader curriculum, including languages like SQL, data visualization tools like Tableau, and machine learning or other artificial intelligence tools.

How Does Learning Python for Data Science Compare to Other Languages?

Python is a primary component of data science, but not every data science professional uses it equally. Many programming beginners start with Java or R, but most learn Python before other languages.

In recent years, R has emerged as the other “big” data science language besides Python. While Python may be easier to learn, R has several advantages in the eyes of many data pros. Consider the following:

Python is typically the language of choice for handling large quantities of data. Python is also commonly tied to deep learning algorithms, web scraping, and running workflows. On the other hand, many data science pros prefer R for data visualization and statistical calculation. 

Data Scientists and other computer science pros may prefer Python for many tasks. However, Data Analysts, Financial Analysts, and Business Analysts may use R more. Both have advantages and drawbacks, so you may want to learn both, depending on your role and the sector or industry you choose.

For more on tools and skills in different positions, check out Noble Desktop’s Learning Resources.

Make the Process Easy

If you enroll in a bootcamp or certificate program to learn Python for data science, you can master fundamentals in a concentrated timeframe. This strategy works for many students and professionals, especially because your education won't take a lengthy period. You can learn lessons, complete assignments, and test your knowledge in a few months or even weeks.

Making the process easier for other students may entail starting with a free Python for data science tutorial or seminar as the first step. This approach works well for those whose work schedules or family obligations currently prevent them from committing to a full-length certificate program and can serve as a great introduction to Python for data science.

Why It’s Worth It

• According to the U.S. Bureau of Labor Statistics, the country’s most populous states have the highest employment rates for Data Scientists. That includes California, New York, and Texas, not limited by region.

• Demand for big data expertise is sky-high across the globe. Among the top positions are Data Scientist, Data Analyst, and Data Engineer. Other top roles include:

• Quantitative Analyst
• Data Warehouse Architect
• Machine Learning Engineer
• Business Analyst
• Statistician

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.