Data Mining and Big Data Analytics Using SPSS

Why this Training Course?

The volume of data accumulated in recent years has grown exponentially, resulting in the collection of datasets related to customer interactions, sales, logistics, and production processes. Large retail companies handle up to ten million transactions and five thousand items per second. This has opened up opportunities for companies to analyse this data and achieve reductions in lead times, reduced overheads, streamlined workflows, increased customer satisfaction, and enhanced profitability. While numerous software packages exist for Big Data mining and Big Data Analytics, many of these tools primarily involve programming languages, which, although free, demand a solid foundation in computer programming.

With the escalating data landscape, there is a corresponding need for an increased workforce proficient in Data Mining and Big Data analytics. One of the most user-friendly tools for this purpose is SPSS.

This Training Course Will Encompass:

• Utilisation of SPSS software packages

• Data mining and visualisation using SPSS

• Big Data analytics with SPSS packages

• Advanced applications of SPSS and its interoperability with other software

• Leveraging Big Data analytics for the benefit of companies

What are the Objectives?

• Upon completion of this training course, participants will have the ability to:

• Comprehend the potential of Big Data and Big Data Analytics

• Harness the advantages of the graphical interface of SPSS for Big Data Analytics

• Gain proficiency in data analysis and visualisation concepts

• Apply drag-and-drop functionality for data visualisation

• Attain knowledge in advanced Big Data analysis, including sentiment analysis in SPSS

Who Should Attend this Training Course?

This course is ideal for those who desire to harness the power of Big Data but lack programming expertise. This training course is suitable for a diverse range of professionals, including but not limited to:

• Chief Technology Officers (CTOs),

• Chief Information Officers (CIOs), and Engineers Data Scientists and Data Analysts

• Statisticians and IT personnel Marketing and research specialists

• Researchers

• Data analysts

How Will this Training Course be Delivered?

Al-Majd Pathways Centre (APC) will employ a variety of established adult learning methods to ensure optimal comprehension, retention, and understanding of the material presented. The course will include theoretical presentations of concepts, with a primary focus on hands-on exercises conducted by participants under the guidance of the instructor. Participants will learn through practical application of the software to real-world problems and actual data, thereby gaining direct experience. Delivery methods will include presentations, group exercises, training e-manuals, interactive seminars, and group discussions to review exercise outcomes.

Course Outline

Day One: Introduction to SPSS Familiarisation with SPSS

• Initiating SPSS and Managing Data Files

• Descriptive Statistics Frequency Distributions (Categorical Variables)

• Measures of Central Tendency, Standard Deviations, and Range (Continuous Variables)

• Accessing Help and Tutorials

Day Two: Data Manipulation and Initial Data Visualisation

• Utilising Graphs to Describe and Explore Data

• Histograms

• Bar Graphs

• Boxplots

• Line Graphs

• Computing Total Scale Scores

• Variable Transformation

• Recoding Procedures

• Computational Procedures

• Case Selection Procedures

• File Splitting Procedures

• Reliability Analysis Using Coefficient Alpha (Cronbach’s Alpha)

Day Three: Inferential Statistics

• T-Tests, One-Sample T-Tests, Independent and Dependent Sample T-Tests

• Analysis of Variance (ANOVA)

• Correlation and Pearson's Correlation Coefficient

• Linear Regression - Simple and Multiple Linear Regression

• Chi-Square Goodness of Fit and Test of Independence Procedures

• Autocorrelation and Time Series Analysis

Day Four: Advanced Statistics with SPSS Modeler

• SPSS Modeler and Its Key Components

• Cluster Analysis Using K-Means

• Association Rules and Apriori Algorithm

• Logistic Regression Forecasting with Autoregressive Integrated Moving Average (ARIMA) Model

• Building Decision Tree Models

• Sensitivity Analysis of Text Data in SPSS

Day Five: Integrating SPSS with Python and R

• Big Data Analytics Programs Brief

• Introduction to R SPSS Modeler-R Integration

• Introduction to Python

• SPSS Modeler Python Integration

Language(s): English and Arabic 

Duration: One Week

Certificate of Completion: Upon successful completion of the course, participants will receive a Certificate of Completion from Al-Majd Pathways Centre (APC).