About This Course
The SPSS Statistics course is designed to provide participants with comprehensive knowledge and practical skills in using SPSS Statistics, a powerful software tool for data analysis and statistical modeling. This course covers a wide range of topics, including data entry, manipulation, and management, as well as performing complex statistical analyses such as descriptive statistics, inferential statistics, regression, and ANOVA. Participants will learn to create and interpret data visualizations, generate reports, and make data-driven decisions. Ideal for researchers, data analysts, and business professionals, this course combines theoretical understanding with hands-on practice, ensuring that learners can apply SPSS techniques to real-world data sets and projects to extract meaningful insights and support evidence-based decision-making.
Skills You Will Learn
- Data Management: Efficiently enter, clean, and manipulate data within SPSS.
- Descriptive Statistics: Calculate and interpret measures such as mean, median, mode, standard deviation, and variance.
- Inferential Statistics: Conduct inferential statistical tests including t-tests, chi-square tests, ANOVA, and more.
- Data Visualization: Create and customize charts, graphs, and plots to visually present data insights.
- Syntax Programming: Write and utilize SPSS syntax for automating analyses and tasks.
- Hypothesis Testing: Formulate and test hypotheses using appropriate statistical methods.
- Data Interpretation: Interpret and draw meaningful conclusions from statistical results.
- Survey Data Analysis: Analyze survey data, including scaling and reliability testing.
- Report Generation: Generate and format comprehensive reports that summarize data analyses and findings.
- Practical Application: Apply learned techniques to real-world datasets and scenarios.
- Problem-Solving: Develop problem-solving skills for addressing and overcoming data analysis challenges.
- Best Practices: Learn and implement best practices for statistical analysis and data management within SPSS.
Key Highlights: SPSS Training
- Hands-On Practice: Engage in practical exercises and real-world case studies to apply SPSS skills to actual data.
- Expert Instruction: Benefit from guidance and insights from experienced instructors with deep expertise in statistical analysis and SPSS.
- Flexible Learning: Access self-paced modules and interactive sessions that fit your schedule and learning preferences.
- Certification: Earn a certificate upon course completion to validate your SPSS skills and enhance your professional credentials.
- Real-World Applications: Learn to apply SPSS techniques to a variety of fields, including business, healthcare, social sciences, and more.
- Career Support: Access resources for career advancement, including resume building, interview preparation, and job placement assistance.
Curriculum
Introduction to Data Science and SPSS
Overview of Data Science
Installation and Setup of SPSS
SPSS Interface and Navigation
Importing and Exporting Data
Data Preparation and Cleaning
Data Types and Measurement Levels
Data Entry and Variable View
Importing Data from Various Sources (Excel, CSV, SQL, etc.)
Data Cleaning Techniques
Handling Missing Values and Outliers
Descriptive Statistics and Data Exploration
Descriptive Statistics (Mean, Median, Mode, Standard Deviation)
Frequency Distributions
Crosstabs and Contingency Tables
Graphical Data Exploration (Histograms, Boxplots, Scatterplots)
Data Visualization Techniques
Data Transformation and Manipulation
Recoding Variables
Computing New Variables
Using SPSS Functions for Data Transformation
Sorting and Selecting Cases
Data Aggregation and Merging Datasets
Hypothesis Testing and Inferential Statistics
Introduction to Hypothesis Testing
T-Tests (Independent, Paired Samples)
ANOVA (One-Way, Two-Way)
Chi-Square Tests
Correlation Analysis (Pearson, Spearman)
Regression Analysis
Simple Linear Regression
Multiple Linear Regression
Assumptions of Regression Analysis
Model Diagnostics and Validation
Interpreting Regression Output
Advanced Statistical Analysis
Logistic Regression
Factor Analysis
Cluster Analysis
Discriminant Analysis
Time Series Analysis
Non-Parametric Tests
Overview of Non-Parametric Tests
Mann-Whitney U Test
Wilcoxon Signed-Rank Test
Kruskal-Wallis Test
Friedman Test
Data Mining and Machine Learning with SPSS Modeler
Introduction to SPSS Modeler
Data Preparation in SPSS Modeler
Building Predictive Models
Classification Algorithms (Decision Trees, Naive Bayes)
Regression and Forecasting Models
Advanced Topics in SPSS
Text Analytics with SPSS
Using SPSS Syntax for Automation
Custom Tables and Advanced Reporting
Working with Large Datasets in SPSS
Integrating SPSS with Other Data Tools
Project Management and Collaboration
Managing Data Science Projects
Documenting and Sharing SPSS Workflows
Collaborative Data Analysis
Ensuring Data Quality and Integrity
Ethical Considerations in Data Science
Capstone Project
Defining a Data Science Problem
Data Collection and Preparation
Exploratory Data Analysis
Building and Evaluating Statistical Models
Presenting Findings and Insights
Your Instructors

₹15,000.00
Material Includes
- Videos
- Booklets
- Guide
Course categories
