Microsoft Excel 2019 Data Analysis and Business Modeling

¥5,121.00

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Descripción del curso

Este curso se imparte en modalidad online y consta de unidades.

La duración del curso es de 110 horas que se distribuye entre el contenido y las herramientas de colaboración. A su finalización, el alumno recibirá un diploma acreditativo.

La formación se realiza a través de nuestro Campus Virtual, con esta modalidad dispondrás de todo el contenido didáctico en la plataforma del curso y estará accesible, desde el día de inicio de curso, las 24 horas todos los días de la semana. El acceso al Campus estará disponible por 3 meses.

El alumno también dispondrá de foros de participación, así como una tutorización continua.

 

Contenido

  • Basic worksheet modeling
  • Range names
  • Lookup functions
  • The INDEX function
  • The MATCH function
  • Text functions and Flash Fill
  • Dates and date functions
  • Evaluating investments by using net present value criteria
  • IRR, XIRR, and MIRR functions
  • More Excel financial functions
  • Circular references
  • IF, IFERROR, IFS, CHOOSE, and SWITCH functions
  • Time and time functions
  • The Paste Special command
  • Three-dimensional formulas and hyperlinks
  • The auditing tool and the Inquire add-in
  • Sensitivity analysis with data tables
  • The Goal Seek command
  • Using the Scenario Manager for sensitivity analysis
  • The COUNTIF, COUNTIFS, COUNT, COUNTA, and COUNTBLANK functions
  • The SUMIF, AVERAGEIF, SUMIFS, AVERAGEIFS, MAXIFS, and MINIFS functions
  • The OFFSET function
  • The INDIRECT function
  • Conditional formatting
  • Sorting in Excel
  • Excel tables and table slicers
  • Spin buttons, scrollbars, option buttons, check boxes, combo boxes, and group list boxes
  • The analytics revolution
  • An introduction to optimization with Excel Solver
  • Using Solver to determine the optimal product mix
  • Using Solver to schedule your workforce
  • Using Solver to solve transportation or distribution problems
  • Using Solver for capital budgeting
  • Using Solver for financial planning
  • Using Solver to rate sports teams
  • Warehouse location and the GRG Multistart and Evolutionary Solver engines
  • Penalties and the Evolutionary Solver
  • The traveling salesperson problem
  • Importing data from a text file or document
  • Get & Transform (or s/b Get & Transform?)
  • Geography and Stock data types
  • Validating data
  • Summarizing data by using histograms and Pareto charts
  • Summarizing data by using descriptive statistics
  • Using pivot tables and slicers to describe data
  • The Data Model
  • Power Pivot
  • Filled and 3D Power Maps
  • Sparklines
  • Summarizing data with database statistical functions
  • Filtering data and removing duplicates
  • Consolidating data
  • Creating subtotals
  • Charting tricks
  • Estimating straight-line relationships
  • Modeling exponential growth
  • The power curve
  • Using correlations to summarize relationships
  • Introduction to multiple regression
  • Incorporating qualitative factors into multiple regression
  • Modeling nonlinearities and interactions
  • Analysis of variance:  One-way ANOVA 
  • Randomized blocks and two-way ANOVA
  • Using moving averages to understand time series
  • Winters method and the Forecast Sheet
  • Ratio-to-moving-average forecast method
  • Forecasting in the presence of special events
  • Introduction to Probability
  • An introduction to random variables
  • The binomial, hypergeometric, and negative binomial random variables
  • The Poisson and exponential random variables
  • The normal random variable and Z-scores
  • Weibull and beta distributions: Modeling machine life and project duration
  • Making probability statements from forecasts
  • Using the lognormal random variable to model stock prices
  • Importing Historical stock data into Excel
  • Introduction to Monte Carlo simulation
  • Calculating an optimal bid
  • Simulating stock prices and asset-allocation modeling
  • Fun and games: Simulating gambling and sporting event probabilities
  • Using resampling to analyze data 
  • Pricing stock options
  • Determining customer value
  • The economic order quantity inventory model
  • Inventory modeling with uncertain demand
  • Queueing theory: The mathematics of waiting in line
  • Estimating a demand curve
  • Pricing products by using tie-ins
  • Pricing products by using subjectively determined demand
  • Nonlinear pricing
  • Array formulas and functions
  • Recording macros 
  • Advanced Sensitivity Analysis

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