4. Reasons for
learning
data
analytics
1. Rising Popularity
• Companies get $10 for every $ invested in developing analytics skills
2. It is an ‘In Thing’
• HBR magazine labelled business analytics as the ‘sexiest’ job of the century
3. Decision making
• 85% of managers use data driven dashboards for decision making
4. Bright Career
• Jobs and research papers in the field of Data analytics and Machine learning
are increasing at a rapid pace
Business
Analytics
5. Intuition based v/s Data based
• Analytics takes the guesswork out of fresh management
approaches.
Intuition based
• At Big Bazzar, the management found that ‘high sales
turnover’ may be possible by increasing ‘employee
engagement’.
Data based
• At Big Bazzar the value of a 0.1% increase in employee
engagement at a particular store is Rs. 10,00,000 sales
turnover.
6. What should I do to achieve
mygoal?
Data products, data
validated actions, increased
success rate of strategic
initiatives
Approach to data Mode Benefits
What is likely to
happen?
Support for strategic
initiatives, forward
looking decision making
What
happened?
Marginal business
benefits, process
gap identification
Why did it
happen?
Significant
improvements from
status quo, data
backed management
Proactive
Action
Proactive
Decisions
Proactive
Consump
tion
Acti
ve
Different levels of Business Analytics
7. • Descriptive Analytics tells you what happened
in the past. Diagnostic Analytics helps you
understand why something happened in the
past. Predictive Analytics predicts what is
most likely to happen in the
future. Prescriptive Analytics recommends
actions you can take to affect those outcomes
10. Statistical Methods
Descriptive Statistics
Summary Statistics Summary Graphic tools Estimation Hypothesis Testing
Measures of central
tendency & Dispersion
1 Mean
2 Median
3 Mode
4 Range
5 Standard deviation
1.Bar Chart
2.Scatterplot
3.Pie chart
InferentialStatistics
Statistical Methods
11. Descriptive Statistics
• Descriptive Statistics: Descriptive statistics is the term given to the analysis of data
that helps describe, show or summarize data in a meaningful way such that, for
example, patterns might emerge from the data. Descriptive statistics do not,
however, allow us to make conclusions beyond the data we have analysed or reach
conclusions regarding any hypotheses we might have made. They are simply a way
to describe our data.
• For Eg.
Score Range Number of
Students
Below 40 20
40-50 22
50-60 33
60-70 21
70-80 13
>80 5
Total 114
27. Data
Visualization
80
70
60
50
40
30
20
10
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Sweater Towel
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Sweater 60 54 43 28 18 9 9 13 28 46 61 69
Towel 19 20 17 23 20 17 16 23 23 21 15 18
Analytics
