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?

Which analytics tools you should know before you get into this field

1) Excel

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

2) R programming

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

3) Statistics

StatisticsÂ 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

4) Linear Regression

Linear regressionÂ is used to model a linear relationship between an outcome variable,Â y, and a set ofÂ predictor variablesÂ x1, x2, etc. Thus, it is a supervised learning technique.

Uses of Regressions: RegressionsÂ are commonly used in the machine learning field to predict continuous value. However, Regression task can predict the value of aÂ dependent variableÂ based on a set ofÂ independent variables.

Real Applications in Industries

• Predicting stock prices based on company performance, historical prices, etc.
• Predicting rainfall based time, temp, humidity, etc.
• Â Predicting sales of a retail store using parameters such as promotion budget, type of store, time of the year
• Predicting call volume for a telecom company
• To predict the number of insurance claims to identify ideal workforce requirement
• Also, predicting the number of items that need to be stocked for an ecommerce company for a month

5) Logistic Regression

Logistic regression is the appropriate regression analysis to conduct when the dependent variable is dichotomous (binary).Â  Like all regression analyses, the logistic regression is a predictive analysis.Â  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.

Real Applications in Industries

• Predicting if an email is a spam or not
• Predicting if customer will default on his/her credit card payment/loan payment
• Predicting if a person will open my email
• Prediction if someone will make a purchase or not
• Predicting the likelihood if a customer will port mobile connection (for telecom)
• Predicting if this machine will break down in the next 3 months

6) Time Series Modelling

Time series analysis is aÂ statistical techniqueÂ that deals with time series data, or trend analysis.Â  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.

Real Applications in Industries

• Predicting stock prices
• For a production company, forecasting future demand
• Prediction the number of units of every category purchased in a retail store
• Estimating the number of births for a location
• Estimate the number of new books to be purchased by a bookstore

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.

Real Applications in Industries

• Identifying products require to purchased together in a retail store
• Also, Identify fraud through identifying transactions that are not similar
• Identify which products need to be stocked together on a kiosk
• Of course in medicine, identifying probability of the occurrence of an illness concerning various factors and symptoms
• Also, Identifying what offers need to be send to what customers
• Suggesting a course to a student which s/he is likely to take, based on course viewed
• Recommendations by YouTube on videos that you may like

8) Decision Trees

Decision treeÂ is a type of supervised learning algorithm (having a pre-defined target variable). It is mostly use inÂ 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 inÂ input variables. Hence, it is useful whenever you need to predict a continuous or a categorical outcome

Real Applications in Industries

• Identify which demographic segment a customer belongs to
• Predicting if customer will default on his/her credit card payment/loan payment
• Predicting is a person will open my email or not
• Prediction if someone will make a purchase or not
• Predicting the number of insurance claims to identify ideal workforce requirement
• Predict the number of items required to be stocked for an ecommerce company for a month

9) Clustering (K means)

ClusterÂ analysis orÂ clusteringÂ is the task of grouping a set of objects in such a way that objects in the same group (called aÂ 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

Real Applications in Industries

• Creation of customer segments for marketing
• Identifying groups of similar outlets/stores
• Geospatial analysis – specifically identifying taxis that are within your geography

Why learn Predictive Business Analytics with EduPristine

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.