DLE Course-6: Advanced Data Analysis using industry accepted and widely popular statistical package

(Course Instructor)
Prof. Dr. Md. Abdus Salam Akanda
Department of Statistics
Faculty of Science
Email: akanda[at]du.ac.bd
Phone: +880-1620-964148

Course Title: Advanced Data Analysis using industry accepted and widely popular statistical package


  • Digital Certificate will be delivered to successful Candidates.
  • Top 15 performer will receive award 50 Euro each.


The purpose of this course is to prepare participants for a possible future role as a practicing statistician. This course introduces how to present, analyze and interpret data using industry standard software package. In every field of work, to be confident and competent in analyzing data and in drawing conclusions are extremely crucial and phenomenally important. This course will help the trainees to develop the skills using statistical software package/s. The package/s that will be used to demonstrate is/are widely used in business, industry, government, commerce and the education and health sectors. As mentioned, the classes will be conducted using industry renowned software package/s but the student might follow any standard package/s to complete the given assignments.

Who can attend?

The course is recommended for faculty members, graduate students, business analysts and other researchers who want to enhance their data analysis capability.


  • The course is built from scratch so no prior knowledge on any Software Package or Statistics is required. The course will cover all the required details involving theoreticl and practical aspects of statistics.
  • The participants must have a laptop to practice the lessons to be covered under this course.
  • The participants need to install coveted software package/s on their laptops.

Venue: Virtual through Zoom Application

 Course Duration: One month, Total 8 Classes, 1 Classes/Week

 Class Duration: 2 Hours

Class Time: Saturday 04:00PM—06:00PM

Learning Objectives:

The objective of this course is to train participants in conducting statistical analysis using software package/s to analyze data – as data summaries, as predictive instruments, and as tools for scientific inference. The participants of this course are expected-

  • to develop advanced expertise in formulating and implementing statistical approaches to practical problems in a wide variety of subject areas.
  • to integrate materials covered in various lecture courses utlising skills developed through practical work
  • to solve real-life problems using computerized statistical application/s
  • to gain advanced knowledge on statistical computing using the statistical package Applications.

Intended Learning Outcomes:

At the end of this course participants will be able to:

  • familiarize the various statistical tools and techniques.
  • gain proficiency in how to analyze a number of statistical procedures using computerized Application/s.
  • implement the various stages of advanced statistical analysis appropriately using computer applications and interpret the output of the procedures of the application.
  • present the main results and conclusions in concise manners.
  • work independently on practical data analysis problems.

 Course Syllabus: 

Week Lesson Topics Type
1. Introduction to Statistics
  • Definition of Statistics
  • Population versus sample
  • Parameter versus statistic
  • Variable and its type
  • Scale of measurement
  • Data and its type
  • Data collection
2. Introduction to Computer Applications on Statistical Analysis and Data Entry
  • Functions of the Application
  • Operating Application
  • Different Functionalities and How to perform
  • Types of files in the Application
  • Creation of Data Files
  • Variable Naming convention
  • An illustrative example
Theory & Practical
3. Computing Measures of Central Tendency Using Computer Application/s
  • Mean
  • Median
  • Mode
  • Quartiles
  • Deciles
  • Percentiles
Theory & Practical
4. Computing Measures of Dispersion Using Computer Application/s
  • Range
  • Variance
  • Standard deviation
  • Skewness
  • Kurtosis
Theory & Practical
5. Creating Graphs and Cross Tables Using Computer Application/s
  • Pie chart
  • Bar chart
  • Cluster bar chart
  • Line graph
  • Cross table
6. Correlation Analysis Using Computer Application/s
  • Definition of correlation
  • Types of correlation
  • Interpretation of correlation coefficient
  • Finding correlation coefficient using Computer Application/s
Theory & Practical
7. Regression Analysis Using Computer Application/s
  • Definition of regression analysis
  • Formulation of regression model
  • Computing simple regression coefficients using Computer Application/s
  • Computing multiple regression coefficients using Computer Application/s
Theory & Practical
8. Test of Hypothesis Using Computer Application/s
  • Definition and example of hypothesis
  • Types of hypothesis
  • Level of significance
  • z-test
  • t-test
  • Chi-square test
Theory & Practical

 Evaluation Criteria:

  • Assignments 50 marks
  • Midterm exam 20 marks
  • Final exam 30 marks

Course Material:
Lecture sheets/Slides/Book

Text Book:
M. A. Salam Akanda (2018). RESEARCH METHODOLOGY- A Complete Direction for Learners, 2nd Edition, Akanda & Sons Publications, Dhaka, Bangladesh