Splunk for Analytics and Data Science

Length: 2 days

Course Overview

This intensive 2-day course introduces data scientists and analysts to Splunk's capabilities for data exploration, analysis, and visualization. Participants will learn to leverage Splunk's search processing language (SPL) and analytics features to extract insights from machine-generated data.

Prerequisites

• Basic understanding of data analysis concepts

• Familiarity with command-line interfaces

• No prior Splunk experience required

Learning Objectives

By the end of this course, participants will be able to:

• Navigate the Splunk interface and perform basic data searches

• Write effective SPL queries for data analysis

• Create statistical summaries and perform aggregations

• Build visualizations and dashboards

• Apply machine learning techniques within Splunk

• Implement best practices for analytics workflows

Foundations and Data Exploration

Module 1: Splunk Fundamentals

• What is Splunk and why use it for analytics

• Splunk architecture overview

• Data ingestion and indexing concepts

• Splunk Web interface navigation

• Basic search concepts and syntax

Module 2: Search Processing Language (SPL) Basics

• SPL command structure and pipeline concept

• Essential search commands: search, where, eval, sort

• Field extraction and manipulation

• Hands-on lab: Basic data exploration

Module 3: Data Discovery and Field Operations

• Field discovery and extraction techniques

• Working with structured and unstructured data

• Regular expressions in Splunk

• Field aliases and calculated fields

• Data model concepts

Module 4: Statistical Operations and Aggregations

• Statistical commands: stats, chart, timechart

• Group-by operations and aggregation functions

• Mathematical and statistical functions

• Hands-on lab: Sales data analysis exercise

Module 5: Data Transformation Techniques

• Data cleaning and normalization

• Handling missing values and outliers

• Data type conversions

• Case study: Log file analysis

Advanced Analytics and Visualization

Module 6: Advanced SPL and Analytics Commands

• Advanced search techniques: subsearches, joins, append

• Lookups and external data integration

• Transaction analysis and sessionization

• Geospatial analysis capabilities

• Hands-on lab: Customer journey analysis

Module 7: Machine Learning with Splunk

• Machine Learning Toolkit (MLTK) overview

• Common algorithms available in Splunk

• Anomaly detection and forecasting

• Clustering and classification examples

• Best practices for ML in Splunk

Module 8: Data Visualization and Dashboards

• Visualization types and when to use them

• Creating charts, graphs, and tables

• Dashboard design principles

• Interactive elements and drill-downs

• Hands-on lab: Building an executive dashboard

Module 9: Advanced Analytics Use Cases

• Security analytics and threat detection

• IT operations analytics

• Business intelligence applications

• Real-time monitoring and alerting

• Performance optimization techniques

Module 10: Best Practices and Next Steps

• Search optimization and performance tuning

• Data governance and security considerations

• Scaling analytics workflows

• Integration with other data science tools

• Career paths and certification options

• Resources for continued learning