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Data Analysis and Statistical Analysis with Excel - Course 2

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Course type: Online Instructor led Course

Platform: Google Hangout

Course ID: 27992

Course type: Online Instructor led Course

Platform: Google Hangout

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Singam Ravi Babu picture
Singam Ravi Babu


5 Years of Experience

About Singam Ravi Babu

I have been teaching Microsoft Excel to students from different backgrounds since 6 years. My students include government employees, academic students, private employees, etc.
I taught at APARD (Andhra Pradesh Academy for Rural Development) for NACO employees and ASOs(Assistant Statistical Officers). And at few other places.
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About the Course

Master the advanced concepts of Excel Data Analysis and Statistical Analysis in 90 Hours with plenty of examples.
Students will be receive soft copy of the course material, and practice Excel sheets.

Topics Covered

1. Fundamentals
2. Range Names
3. Functions In-depth: Text, Date, Financial, IF, Time, 3-dimensional, COUNT, SUM, AVERAGE, OFFSET, INDIRECT, and Array
4. Using net present value criteria to evaluate investments
5. Circular references
6. Sensitivity analysis with data tables
7. The Goal Seek command
8. Using the Scenario Manager for sensitivity analysis
9. Conditional formatting
10. Sorting, Filtering data and removing duplicates
11. Tables
12. The analytics revolution
13. Optimization with Excel Solver
14. Solver to: determine the optimal product mix, to schedule your workforce, to solve transportation or distribution problems, for capital budgeting, for financial planning
15. Importing data from a text file or document and from the Internet
16. Validating data
17. Summarizing data by using histograms and by using descriptive statistics
18. PivotTables and slicers to describe data
19. The Data Model
20. Summarizing data with database statistical functions
21. Consolidating data
22. Creating subtotals
23. Estimating straight-line relationships
24. Modeling exponential growth
25. Using correlations to summarize relationships
26. Introduction to multiple regression
27. Incorporating qualitative factors into multiple regression
28. Modeling nonlinearities and interactions
29. Analysis of variance: one-way ANOVA
30. Randomized blocks and two-way ANOVA
31. Using moving averages to understand time series
32. Winters’s method
33. Ratio-to-moving-average forecast method
34. Forecasting in the presence of special events
35. Random variables
36. The binomial, hypergeometric, and negative binomial random variables
37. The poisson and exponential random variable
38. The normal random variable
39. Modeling machine life and duration of a project: Weibull and beta distributions
40. Making probability statements from forecasts
41. Using the lognormal random variable to model stock prices
42. Monte Carlo simulation
43. Optimal bid
44. Simulating stock prices and asset allocation modeling
45. Using resampling to analyze data
46. Pricing stock options
47. Determining customer value
48. The economic order quantity inventory model
49. Inventory modeling with uncertain demand
50. Queuing theory: the mathematics of waiting in line
51. Estimating a demand curve
52. Pricing products by using tie-ins and by using subjectively determined demand
53. Nonlinear pricing

Who should attend

Any one who want to increase the chances of employ-ability and get promoted faster in his/her career.


A student should know the basic concepts of MS Excel

What you need to bring

Presence of Mind.
And Passion to Learn.""

Key Takeaways

Notes of the subject.
Practice material.


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