Day 1 - Team C

Welcome to our FIRST blog about our FIRST BA class. Before we bombard you with snippets from today’s class, let us quickly fly through the very basics of BA.

What is Business Analytics?

Business Analytics (BA) refers to the skills, technologies, applications and practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning.

Well, that is the ‘definition’ of BA.

To understand in simple terms, BA
- Uses quantitative and computer techniques
- to optimize decision-making
- through working on large amounts of data

BA evolved from being just a forecasting-tool to a significantly influential-tool i.e. when a specific customer type is considering a purchase, an analytics-enabled enterprise can modify the sales pitch to appeal to that consumer.

Companies like Capital One, Progressive, P&G, UPS and Marriott International use sophisticated data collection and processing to stay abreast with consumer preferences, stay profitable and make decisions

Now that we all got an idea of what BA is; we shall now look into SPSS, the tool we use:

SPSS consists of an integrated series of computer programs which enable the user to read data from questionnaire surveys and other sources (e.g. medical and administrative records),to manipulate them in various ways and to produce a wide range of statistical analyses and reports, together with documentation

Again, decoding into layman's language, SPSS can take almost any type of file and use it to generate reports, charts, plots etc.
It can transform highly complex data manipulation and analysis with simple instructions.

Working-Today, we started with SPSS 15.0 in our first BA session. SPSS data editor provided us the platform to enter data from excel. It had 2 tabs, data view and variable view. Initially the data is stored in the data view mode, and gradually we move to variable view mode. Here, name to various variables are given. Then their type was decided like numeric, string, comma, dot, currency etc. Width helped us adjusting the input back in data sheet. Then came the label which actually gives us an opportunity to give reference to the variable. Followed by missing which allow us mention areas where respondents have not answered due to certain reasons. Lastly comes the measure, which we have to set among nominal, ordinal or scale based on values given earlier. We also discussed the categorization of value into category (1^st level analysis)/ continuous (2^nd label analysis) which further divided into continuous & discrete.

We started with GSS93 subset which is nothing but the general social survey. We began with the univariate analysis, via going to:

Analyze- Descriptive Statistics- Frequencies

Frequency helps in generate hypothesis at later stage. Later, bivariate and multivariate techniques were also studied, via going to:

 Analyze- Descriptive Statistics- Crossstabs

Crosstab provides us the opportunity to compare multiple parameters. Rows, columns and layer are used to select intended parameters; rows are kept for those who have to be compared. Cells tab in crosstab provided us the opportunity of selecting percentages in rows & columns. Similarly, statistics provided us with the opportunity to see the level of significance while accepting or rejecting a hypothesis using chi square test.  

In case of dispersed variable like, age, we also learnt how to categorize it in range using:

Transform- record into different variables

Selecting variable and giving new name (cat. original) and label, we go to old & new value, giving a desired range and assigning its new value.

UTILITY- lastly, we saw the application part of the comparison of various variables. From frequency we created various hypotheses, being the possible reason for it. As a thumb rule, we keep variables in row that we want to compare and use only row percentage. We start with a hypothesis, then we form a null hypothesis, then we use cross tab and use chi test to obtain level of significant. If chi test shows value < 0.05 then reject null hypothesis or else accept it. In case we reject null hypothesis, rectified hypothesis has to be mentioned again.

And that is what we learnt on our first day in BA, using SPSS tool.

Bloggers Name (Team C):

Rahat S. Dhir