July 24, 2018
Ever wondered what a Business Analytics Professional does in real life. What platform or software he/she uses? What are the analytics tools and techniques that make his/her life easier? Why is it important to have Business Analytics Training from a good institute?
Excel is an MS office tool for data manipulation and visualization. Lot of basic pre- and post-analysis from the tool are best done in excel and hence excel comes in very handy for any data scientist. However, most of the companies still use Excel for the initial level of analytics.
Uses of Excel: pivots for quick analysis, charting, filtering and visualizing data, presenting the recommendations, output
R is a programming language. It is free software environment for statistical computing and graphics that is supported by the R Foundation for Statistical Computing. First of all, for running any machine learning algorithm, you will need a tool. Further, R is very easy to use, is open source and is very powerful in terms of its machine learning capability.
Uses of R: Running linear, logistic regression, neural nets, random forest, basic statistical analysis and any other machine learning algorithms
Statistics is a branch of mathematics dealing with the collection, organization, analysis, interpretation, and presentation of data. Statistical understanding, especially inferential and applied statistics.
Uses of Statistics: It forms fundamentals of many machine learning algorithms. Hence to understand the output and make sense out of it, basic statistical learning is mandatory
Linear regression is used to model a linear relationship between an outcome variable, y, and a set of predictor variables x1, x2, etc. Thus, it is a supervised learning technique.
Uses of Regressions: Regressions are commonly used in the machine learning field to predict continuous value. However, Regression task can predict the value of a dependent variable based on a set of independent variables.
Logistic regression is the appropriate regression analysis to conduct when the dependent variable is dichotomous (binary). Like all regression analyses, the logistic regression is a predictive analysis. Logistic regression is used to describe data and to explain the relationship between one dependent binary variable and one or more nominal, ordinal, interval or ratio-level independent variables. It is used when you want to solve a binary classification problem i.e. predict an outcome of a binary dependent variable.
Time series analysis is a statistical technique that deals with time series data, or trend analysis. Time series data means that data is in a series of particular time periods or intervals. Thus, it is used when you have time series data, that is data with respective to specific time durations.
Association rule mining is a procedure which aims to observe frequently occurring patterns, correlations, or associations from datasets found in various kinds of databases such as relational databases, transactional databases, and other forms of repositories. Hence, it is useful whenever you need to find frequent occurring patterns within a data.
Decision tree is a type of supervised learning algorithm (having a pre-defined target variable). It is mostly use in classification problems. It works for both categorical and continuous input and output variables.
Indeed in this technique, we split the population or sample into two or more homogeneous sets (or sub-populations) based on most significant splitter / differentiator in input variables. Hence, it is useful whenever you need to predict a continuous or a categorical outcome
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters). In fact, it is an unsupervised algorithm. It is useful whenever you required to club similar entities together based on some characteristics
Our students can expect a mix of case studies, analytics tools, and the hallmark pedagogy of learning through a plethora of hands-on and real-life scenarios. Finishing this business analytics course with our state-of-the-art Capstone project helps the participants to season their skills as a Business Analytics professional; thus, making them ready for the job role.
Moreover, the methodology offers real-life challenges, compelling learners to apply concepts in the class and prepare for career realities. Moreover, EduPristine offers Business Analytics Training and certifications. Thus, good who wish to upgrade their analytical skills with the right analytics tools in order to benefit for a long-term in their careers.