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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

9 Productivity Boosting Tips for SAS Enterprise Guide Users

Kunal Jain
Kunal Jain Last Updated : 30 Mar, 2023
6 min read

SAS Enterprise Guide (EG) is a versatile tool for everyone from novice analysts to experienced programmers. It has revolutionized the way people use and access SAS (especially new users). With use of Enterprise Guide, it is possible for any newbie to start using capabilities of SAS without spending too much time on training.

During my early days with SAS, Enterprise Guide turned out to be a huge help, using which I could not only accelerate my learning, but also make sure that there is little or no impact on delivery timelines of projects.

enterprise-guide_full

What is SAS Enterprise Guide (EG)?

Simply put, SAS Enterprise Guide (EG) is a Graphical User Interface (GUI) to access capabilities of SAS. It is a point-and-click, menu- and wizard-driven tool that empowers users to analyze data and publish results. It provides fast-track learning for quick data analysis, generates code for productivity and speeds your ability to deploy analyses and forecasts in real time. EG enables you to preview the code it writes at the back end, thus enabling the user to learn SAS in parallel.

While EG is easy to use and learn, there are basic tips, which can boost your productivity while using it. These tips will ensure that you use EG in efficient and effective manner. So, go ahead and read them:

Tip 1: Layout Process Flow on Paper Before Starting on EG

As mentioned in other articles, it is a good practice to plan and structure your analysis on paper before you actually use the tool. In case of EG, you can take this to next level. Before starting any project, I draw out the expected process flow with great details on paper. This not only helps me structure my thoughts, but also clears up any confusion before starting the work and also helps me sense check the outcome at a later stage.

A typical process flow would look something like this:

paper_process_flow

As you can see, I even include things like number of rows expected, filter applied, nature of joins etc. The more details you can add at this stage, the easier it becomes to execute.

Tip 2: Brainstorm the Process Flow with a Colleague

This is especially important if you are new to EG. I would suggest to continue doing this for first 3 – 6 months at least and then for any big project. Some of the aspects you should consider in this brainstorming are:

  • Is this process flow efficient? Are there steps which could be eliminated, combined, broken to make it more efficient.
  • Which steps will take the longest time to run? Are there ways to reduce time taken by these steps? Can you join tables in a different order? Can you use an alternate indexed table which has the same information?
  •  What are the checks you can do after every big step to make sure that the outcome is correct?

Tip 3: Compress the Data Sets

While EG makes data manipulations easy, data is stored ineffectively. Use following system option to compress at start of your project and it will be applied to all data sets:

  1. Open SAS Enterprise Guide and go to Tools -> SAS Enterprise Guide Options.
  2. In the left-hand pane, select SAS Programs.
  3. In the right-hand pane, under System options, scroll down and find the COMPRESS option.
  4. Check the box next to COMPRESS to enable data set compression.
  5. Click OK to save your changes.

Once this option is set, all data sets that are created or modified during your project will be compressed by default. This can help reduce the size of your SAS data files and improve performance when reading and writing data.

Tip 4: Give Names to Queries and Data Sets

One of the downside of using SAS EG is that it is really bad at giving names to queries (or query builder step) and data sets. If you continue with the names provided by EG, you will not be able to make any sense out of process flow, when you open next.

A valuable addition here is to add names which describe the query or data set better. For example, a query filtering data basis city can be named something like “filter_city_dataset1” rather than the default “query_for_dataset1”. This helps immensely when you open any code at a later date.

Tip 5: Import a csv file (.csv)

As a common situation, we typically import a lot of data from Excel (and Access) because traditionally these tools have been used for data storage and manipulation. One of the best practice is to store the data you want to import as Comma Separated Values (.csv) and then import it in SAS. Doing so, typically takes only fraction of time compared to importing data from Excel or Access native formats.

Tip 6: Store Data in Permanent Memory After Every Big Step

If you have finished a step which has takes a lot of time to run, you should store the outcome into permanent memory rather than keeping it in WORK.

How does it help?

  • In case you lose your connection with the server, you don’t need to run the entire big step again
  • If you want to make changes in this project at a later date, you can manipulate only the part in which change is required, rather than running the entire project again.

Tip 7: Arrange the Project in Multiple Process Flows

One of the best ways to keep process flows easy to understand is to divide them in functional process flows and keep them separate. For example, if you are creating a financial model, you can have one process flow for income, one for expenses, one for profit calculation and so on and so forth.

Tip 8: Document the Process Flow as soon as you Finish it

This is simply good house-keeping. Every time you finish a project, make sure you spend some time documenting it. Add a simple note in each process flow including at least following:

  • What does the process flow achieve?
  • What are the input and output data sets?
  • What are the assumptions you have made? Where to make changes in order to change them?

Tip 9: Disable Automatic Opening of Data

By default, EG opens any data set you bring in the process flow. While this helps you look and explore the data before you write a query, you end up using network and resources (especially for big data sets). If you want to explore any big data set, it is best done on a sample.

To disable automatic opening of data, go to Tools -> Options -> Data and then de-select the option which says “Automatically open data when added to project”. Press OK to save.

Wrapping Up

These were some tips which I have used and benefited from in my usage of EG. Please feel free to add any more  tips you might have used to boost your productivity while using SAS EG. If you like what you just read & want to continue your analytics learning, subscribe to our emails or like our facebook page.

Python vs. R vs. SAS – which tool should I learn for Data Science?

Frequently Asked Questions

Q1. What is the difference between SAS and SAS eg?

A. SAS is a programming language used for data analysis and statistical modeling, while SAS Enterprise Guide (SAS EG) is an integrated development environment (IDE) that provides a graphical user interface (GUI) for SAS programming.

Q2. Is SAS eg an ETL tool?

A. Yes, SAS EG is an ETL (Extract, Transform, Load) tool that can be used to extract data from various sources, transform it as per business needs, and load it into the desired destination.

Q3. What language does SAS eg use?

SAS EG uses the SAS programming language for coding and also supports drag-and-drop functionality for ease of use.

Q3. What is SAS eg log summary

A. SAS EG log summary provides a summary of all the log messages generated during the execution of SAS code in SAS EG. It can help identify errors, warnings, and other messages related to the execution of SAS code.

Q4. How do I create a process flow in SAS Enterprise Guide?

A. To create a process flow in SAS Enterprise Guide, you can use the drag-and-drop functionality to add different tasks, such as importing data, performing analyses, and generating reports. You can also customize the process flow as per your needs.

Q5. What is the difference between SAS and SAS Enterprise?

SAS is a programming language and suite of software tools used for statistical analysis, data management, and predictive modeling. SAS Enterprise is a platform that includes SAS software and other tools designed for enterprise-level data management and analytics. SAS Enterprise Guide is a component of SAS Enterprise.

Kunal Jain
Kunal Jain

Kunal Jain is the Founder and CEO of Analytics Vidhya, one of the world's leading communities of Al professionals. With over 17 years of experience in the field, Kunal has been instrumental in shaping the global Al landscape. His expertise spans diverse markets, from developed economies like the UK to emerging ones like India, where he has successfully led and delivered complex data-driven solutions. As a recognized thought leader, Kunal has empowered countless individuals to realize their Al ambitions through his visionary approach to Al education and community building. Before founding Analytics Vidhya, Kunal earned both his undergraduate and postgraduate degrees from IIT Bombay and held key roles at Capital One and Aviva Life Insurance across multiple geographies. His passion lies at the intersection of analytics, Al, and fostering a thriving community of data science professionals.

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Responses From Readers

Clear

Sanchit Dwivedi
Sanchit Dwivedi

i am base SAS certified and i have completed MSc with Statistics(2013 passout) .I am looking for job in analyst field.Are there any opening for my profile..plz help me.

123

Kunal Jain
Kunal Jain

Please send me your CV. I’ll circulate the CV in my circle and it will get evaluated on its merit and your skills. Thanks

123
Srishti Kapoor
Srishti Kapoor

Hello, I am new to SAS and looking for a good online learning platform for SAS BASE. I wanna do certification on the Base. Please guide me any good platforms. I know one which is as follows but not sure whether they are good enough to teach me to certification level. Please take a look one such course 1-1: Base SAS Certification preparation course Thanks in advance. Regards, Srishti

123

Kunal Jain
Kunal Jain

Srishti, If you stay in one of the metro cities, the best certification to do is from SAS institute itself. Please visit there site here. Thanks, Kunal

123

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