Gauge Your Expertise in SAS, R and Python: An AI Expert‘s Guide
As an artificial intelligence and machine learning professional, I‘ve seen firsthand how the data science field has evolved in both the skills required and the tools of the trade. Among the myriad of programming languages and software packages available to data scientists, three have emerged as the clear front-runners: SAS, R and Python.
In a 2019 survey of over 1,000 data professionals by software firm Anaconda, Python was the most commonly used tool, employed by 90% of respondents. R and SAS usage clocked in at 64% and 34% respectively. An analysis by data science platform KDnuggets found a similar hierarchy in job postings, with Python mentioned in 75% of listings, R in 50%, and SAS in 30%.
But data science isn‘t just about picking up a language or two. It‘s a complex, multidisciplinary field drawing upon advanced statistics, predictive modeling, algorithm design, data engineering and more. True expertise requires both breadth and depth of knowledge across these domains.
That‘s why I‘ve developed this comprehensive assessment to help you gauge your skills in SAS, R and Python within an applied data science context. More than just a simple quiz, it‘s a diagnostic tool to pinpoint strengths and uncover areas for growth. After completing it, you‘ll have a clear roadmap for charting your ongoing development as an AI and analytics professional.
Assessing Your SAS Savvy
Kicking things off with SAS, a pioneer in the analytics space since the 1970s. This software suite offers modules for data manipulation, statistical modeling, machine learning, data visualization and more. Historically popular in the healthcare/pharma, financial services and government sectors, SAS remains a go-to for many large enterprises with deep legacy investments.
Answer the following questions to gauge your baseline SAS aptitude:
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What does the COMPGED function calculate?
a) The generalized edit distance between two strings
b) The longest common substring
c) The soundex encoding of a string
d) The Levenshtein distance between two strings -
Which of the following is not a valid SAS procedure?
a) PROC LOGISTIC
b) PROC RANDOM
c) PROC TABULATE
d) PROC ARIMA -
What is the purpose of the ODS statement in SAS?
a) Optimize disk space usage
b) Oversee data security
c) Open, manage, and close output destinations
d) Omit observations from analysis -
The CAT function in SAS performs:
a) Categorization of numeric values into groups
b) Concatenation of character strings
c) Catalog listing of files in a SAS library
d) Category formatting of variables -
True or False? By default, SAS uses a least squares algorithm to estimate regression model parameters.
Click here for the answers
1. a) The COMPGED function calculates the generalized edit distance between two strings.
2. b) PROC RANDOM is not a valid SAS procedure.
3. c) The ODS statement opens, manages, and closes output destinations.
4. b) The CAT function concatenates character strings.
5. True. The default algorithm for regression models like PROC REG is least squares.
Evaluating Your R Expertise
Next up is R – a darling of the open source data science world. R‘s ever-expanding universe of packages (over 17,000 on CRAN as of 2022) makes it incredibly versatile for data processing, visualization and modeling. It‘s particularly beloved by statisticians and researchers for its robust support of specialized analyses.
Put your R knowledge to the test:
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Which of the following is not part of the base R distribution?
a) data.frame
b) list
c) matrix
d) tibble -
The R command to get the current working directory is:
a) cwd()
b) pwd()
c) getdir()
d) getwd() -
Which of these R packages is not primarily used for machine learning?
a) caret
b) randomForest
c) e1071
d) shiny -
In R, what does the "paste" function do?
a) Copy and paste between variables
b) Concatenate strings
c) Paste plots from the clipboard
d) Add labels to a plot -
True or False? The "readr" package is more efficient for reading in large, raw data files than the built-in read.csv() function.
Click here for the answers
1. d) The tibble data structure is not part of base R, but is included in the tibble package.
2. d) getwd() returns the current working directory.
3. d) The shiny package is for building interactive web apps, not machine learning.
4. b) paste() concatenates character strings.
5. True. The readr package utilizes a faster C++ backend to efficiently read in large, raw data files.
Testing Your Python Proficiency
Last but certainly not least is Python – the versatile, general-purpose language that‘s skyrocketed to the top of the data science ranks. With a gentle learning curve yet powerful built-in capabilities and external libraries, Python can handle everything from data cleaning to deep learning. It‘s also the language of choice for many data engineering and production ML pipelines.
See how you stack up with these Python problems:
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Which of the following is not a valid Python data structure?
a) set
b) queue
c) dictionary
d) tuple -
What is the preferred library for data manipulation in Python?
a) NumPy
b) SciPy
c) Pandas
d) Matplotlib -
Which of these Python libraries is primarily used for natural language processing?
a) Keras
b) PyTorch
c) NLTK
d) Statsmodels -
In Python, what does the zip() function do?
a) Compress files
b) Aggregate elements from multiple iterables
c) Quickly iterate through a list
d) Join two data frames together -
True or False? Python supports object-oriented programming principles like inheritance and polymorphism.
Click here for the answers
1. b) Queue is not a built-in Python data structure (though it can be implemented or imported).
2. c) Pandas is the go-to for data wrangling and analysis in Python.
3. c) NLTK (Natural Language Toolkit) is designed for working with human language data.
4. b) zip() takes iterables, aggregates their elements based on the iterables‘ index, and returns an iterator of tuples.
5. True. Python has robust support for object-oriented design patterns.
Putting Your Scores in Context
So, how did you fare? Don‘t get too hung up on your raw scores. The goal here isn‘t acing every question, but rather gaining a realistic sense of where you stand. Here‘s a general guide for interpreting your results in each section:
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4-5 correct: You have an advanced understanding of this language – keep up the great work! Look for opportunities to apply your skills on complex, ambitious projects. Share your expertise by mentoring others.
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2-3 correct: You‘ve got a solid foundation, but there‘s room to level up. Prioritize hands-on practice and targeted learning in your weaker areas. Look for small ways to incorporate this language into your daily work.
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0-1 correct: You‘re still early in your journey with this language. Consider investing in a comprehensive introductory course or dedicating focused self-study time. Don‘t get discouraged – fluency will come with consistent practice!
It‘s also worth noting that each language has particular strengths and domains where it shines. For instance, SAS is known for its powerful data manipulation and ETL capabilities, R for its statistical rigor, and Python for its ML and AI libraries. Ultimately, a well-rounded data scientist should aim to be conversant in multiple languages, even if specializing in one.
As Jeanne Harris, a professor at Columbia University‘s Business Analytics program says: "The most successful data scientists are the ones who know how to match the right tool to the right problem. That means being multilingual – fluent not just in statistics and technology, but also in business needs and strategy."
Charting Your Growth Path
No matter your starting point, the key to mastering SAS, R, Python or any language is continuous practice and application. Carve out dedicated time for skill development, whether that‘s taking an online course, working through a textbook, or building your own projects.
As you plan your learning roadmap, here are a few resources I recommend:
- DataCamp: Offers interactive courses on R, Python and SQL
- edX Data Analysis & Statistics Courses: Includes programs from top universities like Harvard and MIT
- Coursera Data Science Courses: Features offerings from industry leaders like IBM and Google
- Swirl: An interactive learning environment for R and statistics
- Kaggle: Hosts data science competitions and a vibrant community for sharing knowledge
But don‘t just passively consume content. The real learning happens when you get your hands dirty with real-world datasets and business problems. Seek out opportunities to collaborate with others and get feedback from experienced practitioners.
The AI-Powered Future of Analytics
Looking ahead, the rapid advancement of AI and machine learning is poised to reshape the data science landscape. We‘re already seeing the rise of automated machine learning (AutoML) tools like H2O.ai and Google Cloud AutoML that can handle tasks like feature engineering, model selection and hyperparameter tuning.
As Gartner predicts, by 2025, half of all data scientist activities will be automated. But rather than replacing data scientists, these tools will likely supercharge their capabilities and free them up to focus on higher-level, strategic work.
In this AI-driven future, data scientists who can wield machine learning techniques while also deeply understanding the business context and actionability of their insights will be the most valuable. Technical skills in SAS, R and Python will remain critical, but they‘ll increasingly be paired with expertise in areas like data ethics, ML ops, and data storytelling.
As renowned data scientist DJ Patil put it: "Data science isn‘t just about data crunching. It‘s about problem solving, creating new opportunities and telling stories with data. The best data scientists combine technical skills with domain expertise, strong communication and a knack for influencing decision-making."
So as you continue to hone your programming chops, don‘t lose sight of the bigger picture. The most successful analytics professionals bring together technical fluency, domain knowledge, strategic thinking and a tireless commitment to learning. They know that true expertise is a journey, not a destination.
My advice? Embrace the lifelong learning mindset. Stay curious, take smart risks and never stop growing. And remember – your value as a data scientist lies not just in the languages you know, but in the real-world impact you create. So get out there and start translating those SAS, R and Python skills into game-changing insights.
