- 1 Welcome
- 2 About Us
- 3 Analytics Toolbox
- 4 Jobs
- 5 Application Materials
- 6 Networking
- 7 Interviews
- 8 GO 4WARD!
3.3 Data Analytics
Data analytics (DA) is the process of examining data sets in order to find trends and draw conclusions about the information they contain. Increasingly, data analytics is done with the aid of specialized systems and software.
3.3.1 Tableau
Tableau Software is an American interactive data visualization software company focused on business intelligence. Tableau products query relational databases, online analytical processing cubes, cloud databases, and spreadsheets to generate graph-type data visualizations. The software can also extract, store, and retrieve data from an in-memory data engine.
Free Installation for student:
3.3.2 SAS
SAS (previously “Statistical Analysis System”) is a statistical software suite developed by SAS Institute for data management, advanced analytics, multivariate analysis, business intelligence, criminal investigation, and predictive analytics.
Free Installation for Students:
3.3.3 SPSS
SPSS is a widely used program for statistical analysis in social science. It is also used by market researchers, health researchers, survey companies, government, education researchers, marketing organizations, data miners, and others.
3.3.4 A/B Testing
A/B testing (also known as bucket testing or split-run testing) is a user experience research methodology. A/B tests consist of a randomized experiment with two variants, A and B. It includes application of statistical hypothesis testing or “two-sample hypothesis testing” as used in the field of statistics. A/B testing is a way to compare two versions of a single variable, typically by testing a subject’s response to variant A against variant B, and determining which of the two variants is more effective.
3.3.5 Machine Learning
Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.
3.3.6 Scikit Learn
Scikit-learn (formerly scikits.learn and also known as sklearn) is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.
Some frequently-used machine learning classifier/regression models:
- Linear Regression
- Logistic Regression
- RidgeCV
- DecisionTree
- RandomForest
- SVM
- KNN
3.3.7 Feature Engineering
Feature engineering is the process of selecting, manipulating, and transforming raw data into features that can be used in supervised learning. In order to make machine learning work well on new tasks, it might be necessary to design and train better features. It is usually performed before using machine learning models.
3.3.8 Cross-Validation
Cross-validation is a resampling method that uses different portions of the data to test and train a model on different iterations. It is mainly used in settings where the goal is prediction (e.g., machine learning models), and one wants to estimate how accurately a predictive model will perform in practice.