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29 - DMS - Data Mining and Statistics

DMS 201 - Statistical Techniques Using STATA Package

Code Start Date Duration Venue Fees
DMS 201 24 January 2022 5 Days Istanbul $ 3950 Registration Form Link
DMS 201 21 March 2022 5 Days Istanbul $ 3950 Registration Form Link
DMS 201 16 May 2022 5 Days Istanbul $ 3950 Registration Form Link
DMS 201 18 July 2022 5 Days Istanbul $ 3950 Registration Form Link
DMS 201 05 September 2022 5 Days Istanbul $ 3950 Registration Form Link
DMS 201 31 October 2022 5 Days Istanbul $ 3950 Registration Form Link
DMS 201 26 December 2022 5 Days Istanbul $ 3950 Registration Form Link


Course Description

Statistical analysis is broken down into five discrete steps of; a) describing the nature of the data to be analysed, b) exploring the relationship between the selected data and the underlying population, c) creating a model in order to summarize the understanding of how the data relates to the underlying population, d) proving (or disprove) the validity of the model and the last step e) employing predictive analytics to run scenarios that will help guide future actions. Handling all these five steps which every person might experience it in different fields such as economics, social science, management, political science and etc. might be cumbersome without using a power full statistical package.

STATA is one of the powerful statistical software which is commonly used in many disciplines. It is a known as a general-purpose statistical software package created in 1985 by StataCorp. STATA's capabilities include data management, statistical analysis, graphics, simulations, regression analysis (linear and multiple), and custom programming.

This course will show how to program and employ the powerful statistical commands available in STATA package by recalling some statistical concepts. The course include but not limited to , introducing STATA windows environment. It discusses how to create a dataset, importing data from other sources, data manipulations, descriptive statistics and regression analysis. It will assist trainees in developing the data analysis skills necessary for autonomous and efficient computer processing, manipulation, and analysis of empirical data various fields such as social science and business.

Course Objectives

  • Recalling a basic understanding of statistics,
  • Being able to efficiently and professionally use of STATA package for statistical analysis and reporting the results,
  • Gaining the self-confidence for the statistical solutions

Who Should Attend?

  • Data miners or researchers
  • Statistical Fresh Researchers studying  in different areas
  • Statistical Managers
  • Managers
  • Anyone who wants to learn the statistical concepts by using statistical methods with STATA

Course Details/Schedule

Day 1

Getting to know STATA, getting data into STATA and data manipulation    

  • Background, advantages, and disadvantages of STATA,
  • Working with STATA: menu vs. command line vs. do files,
  • Help files, online PDF documentation,
  • Creating datasets and Data import: different ways of importing data,
  • Import data from main public data sources and dealing with missing values,
  • Generating new variables. Generate, Egen, Replace, Dummy variables, Lags and leads,
  • Dropping, sorting, recoding, grouping variables,
  • Combining different datasets.

Day 2

Working with basic statistical concepts

  • Talking about Statistics, recalling some basics,
  • Recalling the standard research phases, including but not  limited to proposing  a methodology, analyzing and reporting the results,
  • Emphasizing on the importance of good data,
  • Recalling what the descriptive statistics; frequencies and measure of tendency are and how to handle these all using STATA command such as describe, summarize, tab, display, fre and ….,
  • Recalling the concept of statistical significance and confidence interval , mean, standard deviation, correlation, one-way anova, two-way anova, percentiles, (t-)test on mean difference, comparing groups within one variable, compare two variables, using ci and etc.

Day 3

Linear Regression

  • Review of Least Squares Estimation, OLS with one explanatory variable,
  • Review of Least Squares Estimation, OLS with multiple explanatory variables,
  • Working with regress, estat hettest, imtest, whitetst,….
  • Post estimation commands such as prediction, hypothesis testing, extracting results such as ttest,
  • Regression including dummy variables; using xi: reg and  i. commands, 

Logistic Regression

  • Review of logistic Regression concepts,
  • Working with logit, probit, margins, …. 

Day 4

Panel data analysis

  • Data structure: Wide vs. long
  • Reshape
  • Describe pattern of xt data
  • Summarize xt data
  • Tabulate xt data
  • Panel regressions using xtreg, 

Time Series data 

  • Stata Date and Time-series Variables
  • Getting dates into Stata format
  • Using the time series date variables
  • Making Use of Dates
  • Lag and forward operator
  • First difference and dlog
  • Time Series Tricks Using Dates

Day 5

 Graphing and programming in do files

  • Line plot,  Legend, labels, shapes, colors, using tsline…
  • Scatter plot using scatter,
  • Combining graphs: ”twoway”, e.g. scatter with regression line
  • Working with Histogram, kdensity commands,
  • Plot the residuals versus the fitted values or predictor of y using rvfplot and rvpplot commands,
  • Creating and working with do files clear, set, cd and use commands
  • Commenting and Presenting results