How do you analyze data with R, Python, and SAS?

R is a great language for data analysis. However, the nature of the language and it's community often prioritizes coding practices that result in a great end product with inconsistent and bad coding practices. It is used mostly in BI and academia. R is limited by a community which tends to support - almost exclusively - Rstudio and Shiny apps, except in enterprise environments. These are good, but portability to Linux systems, notebook style workflows, and any interaction with backend workflows is often very difficult. While R has great support for advanced statistical analysis, Python is better suited for workflows involving multiple parties who prioritize documentation and portability, not just the final product.

I learned R in my Masters of Data Analytics program. R has its origin in academia. However, it is growing in popularity for corporate data analysis. R is widely recognized for its capabilities in statistical analysis and data visualization. Tidyr and dplyr are your allies in cleaning and wrangling data into a format suited for analysis. For analysis, R's base package offers statistical tools, but packages like caret step in for more intricate tasks, useful for machine learning implementations. R also shines in data visualization with ggplot2, creating detailed, customizable plots. R also has challenges. Large data sets can pose memory management issues. Despite these, R has a comprehensive library and a thriving community.

Analyze data with Python using libraries like Pandas for data manipulation and cleaning. Use NumPy for numerical operations. Visualize using Matplotlib, Seaborn, or Plotly. Apply statistical analysis, regression, and classification with Scikit-learn. Perform natural language processing with NLTK or spaCy. Utilize Jupyter notebooks for interactive exploration. Ensure data quality, preprocess, and transform as needed. Leverage Python's extensive ecosystem to extract insights, make predictions, and inform decisions.

One key aspect when choosing a technology stack is considering its ecosystem. Each language has its unique syntax and different people will have different preferences over it - and yet, the open source libraries built on top of a language is what empowers developers to be more productive. There is an old joke that says that "Python is the second best language for everything", and this is today more visible than ever: one can perform all data science tasks, ranging from infrastructure-as-code, web serving, data engineering, all the way to exploratory data analysis, machine learning, visualisation, dashboarding, and more. Python has become the "lingua franca" of data.

Python is a general-purpose, flexible language. It is the go-to choice for machine learning and automation. In data analysis, I use packages like pandas and numpy for manipulation, scipy for scientific computation, Matplotlib and seaborn for visualization, and scikit-learn for machine learning. Python's advantage is its ability to integrate with other tools and platforms such as SQL or Spark. Python is scalable, and the learning resources make it a good choice for beginners. Python also has challenges. It requires careful version control. Python lacks some of the advanced statistical features available in R. Despite these drawbacks, Python's adaptability makes it a key player in data analytics.

Python is excellent for data analysis, and machine learning especially for a beginner learning these fields. The positives are: 1)There are a lot of data science resources online, and a good number of them use python as the language of choice to teach concepts associated with this field. With this, you are guaranteed a good learning curve as it's easier to access python related contents. 2)There are easier ways and options to set up a python IDE to get you started with python. An example is "Anaconda", which can be downloaded freely from the internet. Installing Anaconda gives you an instant access to multiple python IDE's like Jupyter notebook which is best suited for beginners in data analysis. 3) You can integrate python with PowerBi.

I earned my "SAS BASE PROGRAMMER" and "SAS STATISTICAL BUSINESS ANALYST" certifications in my Master's degree, but the program is not used in many organizations. It is a licensed product historically used by regulated industries. However, many of those models have moved to alternatives, like Python. SAS reliably handles complex datasets. It makes regression, ANOVA, or cluster analysis easy to execute. Because of the cost, SAS has excellent security, stability, high-quality technical support, and extensive documentation. SAS does have its limitations. It's expensive. Its interface is outdated. SAS is daunting for beginners. SAS's power and reliability make it a noteworthy option for regulated companies in the data analysis landscape.

Despite the roaring popularity of Python, excellent teams have a rich and diverse set of tools at their disposal: R has unmatched statistical algorithms and delightful visualization libraries, SQL expressivity and approachability is unparalleled, JavaScript provides endless flexibility to build interactive experiences, and Rust is becoming more and more popular for high performance data processing. Don't become attached to a specific tool or language, embrace diversity of technologies, and be open to other programming paradigms.

Using R or Python, as a data employee, is sometimes not even in your control. There may be engineering constraints, that mean you have to use what the team advises. I would advise spending some time on considering the language you use, but the choice can largely be expediated if you talk to your team. Further, a very good philosophy for data employees is to be 'problem oriented', rather than 'solution oriented'. Focus less on the 'how' and more of just solving the problem for the business. Optimisations of the how can come later on.