This book has covered almost all the materials to analyze data with Excel. There are several reasons, I have chosen this book as my top book to master data analysis with Excel. Read More: Best 14 Power BI Books for Business Intelligenceīest Data Analysis and Business Modeling Book (Excel 2016) On this page, I’ve collected the best books that will help you perform better data analysis with the business information.Įxcel was always the best and easy-going tool for data analysis and will be in the future. No, Excel is not the best tool and not the only one.īut as a starting tool and for small business decisions, it is the best tool. So, you might think that whether Excel is the best tool to be one of them. If you want to be one of them (high paying professionals), be a nerdy data analyst ? The demand for data analysts has made them the top-paying professionals in a business. Read More: Best 39 Excel Book for Advanced Users So, data and data analysis is the indispensable part of a business ? What they’re liking on Facebook, where they’re checking in for some foods or travel, what products they’re searching on Google all are important information for businesses now. Smartphone applications are tracking people everywhere. In the blink of an eye, computer-generated algorithms are making billions of dollars trades every day.īusinesses are monitoring every step of their customers, taking an important decision based on the collected information. Genius data analysts (with the help of world-class programmers) are making complex algorithms just based on every data they can collect. Pricing products by using subjectively determined demand.
Queuing theory: the mathematics of waiting in line. Inventory modeling with uncertain demand. The economic order quantity inventory model. Fun and games: simulating gambling and sporting event probabilities. Simulating stock prices and asset allocation modeling. Using the lognormal random variable to model stock prices. Making probability statements from forecasts. Weibull and beta distributions: modeling machine life and duration of a project. The Poisson and exponential random variable. The binomial, hypergeometric, and negative binomial random variables. Forecasting in the presence of special events. Using moving averages to understand time series. Modeling nonlinearities and interactions. Incorporating qualitative factors into multiple regression. Using correlations to summarize relationships. Summarizing data with database statistical functions. Using PivotTables and slicers to describe data. Summarizing data by using descriptive statistics. Importing data from a text file or document. Warehouse location and the GRG Multistart and Evolutionary Solver engines. Using Solver to solve transportation or distribution problems. Using Solver to determine the optimal product mix. Introducing optimization with Excel Solver. Spinner buttons, scroll bars, option buttons, check boxes, combo boxes, and group list boxes. The SUMIF, AVERAGEIF, SUMIFS, and AVERAGEIFS functions. The COUNTIF, COUNTIFS, COUNT, COUNTA, and COUNTBLANK functions. Using the Scenario Manager for sensitivity analysis. Evaluating investments by using net present value criteria.