Data analysis
Data analysis is the systematic examination, transformation, and interpretation of data to derive information, identify patterns, or support conclusions and decisions.
Data for All
Data analysis is the examination and interpretation of data to identify information, patterns, or conclusions.
- How it is used
- Shows how organizations turn collected data into decisions about customers, locations, targeting, and markets.
- How deep it goes
- Explains collection, cleaning, integration, and a worked finding; distinguishes advanced analytics from reporting without teaching algorithms.
- Examples
- Uses facility siting across multiple data sources and a pricing study that found discounts undermined sales goals.
- What you can do after
- Evaluate/understand how an analysis can shape a decision, challenge an assumption, or support targeting that warrants scrutiny.
Starting Data Analytics with Generative AI and Python
The process of examining and interpreting data to produce insights for understanding or decision-making.
- How it is used
- Supports business decisions by drawing conclusions from relevant data rather than merely producing charts.
- How deep it goes
- Outlines collection, cleaning, processing, and presentation, with descriptive measures; leaves specialized pipeline details to the business question.
- Examples
- Uses restaurant orders by item, location, and time, alongside a cost breakdown by process.
- What you can do after
- Practice/perform analysis that informs decisions while checking AI-suggested methods and code rather than accepting them untested.
Read how Starting Data Analytics with Generative AI and Python explains data analysis →
The Well-Grounded Data Analyst
Data analysis is the systematic process of inspecting, transforming, and interpreting data to produce findings or support decisions.
- How it is used
- Deploys analysis to answer uncertain stakeholder questions, test business ideas, and deliver pragmatic findings.
- How deep it goes
- Works through framing, data-quality checks, transformations, aggregation, visualization, and iteration, while deferring in-depth machine learning.
- Examples
- Uses cinema admissions, genre revenue, distributor market share, extracted PDFs, and a vehicle-market proof of concept.
- What you can do after
- Apply analysis to translate questions, assess data, explore patterns, communicate findings, and prototype data-driven ideas.
Read how The Well-Grounded Data Analyst explains data analysis →
Data Analysis with LLMs
Data analysis is the process of examining, transforming, and interpreting data to discover patterns, answer questions, and derive insights.
- How it is used
- Deploys natural-language models to process data directly or generate code for specialized data-processing tools.
- How deep it goes
- Explains data types, analysis tasks, and two workflows, but defers detailed processing implementations.
- Examples
- Uses text classification, image questioning, video titling, audio transcription, spoken queries, and a generated poem and picture.
- What you can do after
- Apply it via ChatGPT to process supplied data or translate analysis descriptions into executable formal queries or code.
The Complete Obsolete Guide to Generative AI
Data analysis is the examination and transformation of data to discover values, patterns, or other useful findings.
- How it is used
- Uses AI to find patterns, key details, and opinion signals in existing data.
- How deep it goes
- Moves from CSV questions and spreadsheet formulas to large-dataset patterns and sentiment analysis, without detailing analytic algorithms.
- Examples
- Uses a highest-S&P-500-date CSV question, spreadsheet formulas, social-media posts, and hiring data.
- What you can do after
- Apply it via LLM-assisted spreadsheets and AI tools to investigate datasets and inform decisions.
Read how The Complete Obsolete Guide to Generative AI explains data analysis →
Data Without Labels
The systematic examination of data to generate insights, identify trends, and support decisions.
- How it is used
- Generates insights and trends after data engineering prepares data for analysis and business intelligence turns results into reports or dashboards.
- How deep it goes
- Frames data management, quality, and disciplinary relationships, but stops short of a detailed analysis algorithm or implementation workflow.
- Examples
- Uses case studies, Python code, reports, dashboards, and high-dimensional datasets as practical analytical contexts.
- What you can do after
- Evaluate data quality and understand how analysis supports insights, trends, models, reports, and data-driven solutions.
Effective Data Analysis
Analytics is the practice of using data to discover and communicate patterns, trends, and insights that inform decisions.
- How it is used
- Guides questions, methods, and communications that help stakeholders make data-informed decisions.
- How deep it goes
- Defines the practice and names common methods, but does not detail their calculations, implementation, or field-specific selection.
- Examples
- Uses business performance, product usability, marketing return on investment, and analyst headcount scenarios.
- What you can do after
- Evaluate stakeholder questions and communicate summaries, analyses, and insights that provide strategic direction.
Data Storytelling with Altair and AI
The process of examining, transforming, and interpreting data to produce findings or insights.
- How it is used
- Uses analysis to turn explored data into findings that can be communicated and understood by an audience.
- How deep it goes
- Defines analysis within a three-phase workflow but does not detail specific analytical methods or implementations.
- Examples
- Uses data exploration, analysis, and presentation as stages, spanning government, education, finance, entertainment, and healthcare contexts.
- What you can do after
- Evaluate/understand findings and prepare them for compelling communication to an audience.
Read how Data Storytelling with Altair and AI explains data analysis →
Machine Learning Platform Engineering
Data analysis is the systematic examination and interpretation of data to discover patterns, assess quality, and support decisions.
- How it is used
- Uses interactive exploration and preparation pipelines to establish reliable inputs for machine-learning projects.
- How deep it goes
- Explains infrastructure constraints and Kubeflow notebook integration, then stops short of detailed analytical techniques.
- Examples
- Uses ID card detection, movie recommendation, object-detection inspection, and ad hoc model inference.
- What you can do after
- Operate and configure Kubeflow notebooks for dataset inspection, resource access, and exploratory model inference.
Read how Machine Learning Platform Engineering explains data analysis →
Get Programming: Learn to code with Python
The systematic examination of stored data to derive summaries, comparisons, or other information from it.
- How it is used
- Uses programs to examine information supplied in files instead of requiring users to enter large amounts of data manually.
- How deep it goes
- Explains a file-reading workflow and several simple analyses, but does not develop advanced statistical methods.
- Examples
- Uses friends’ names and phone numbers to derive locations and count represented states.
- What you can do after
- Build it from scratch by reading structured files, storing their contents, and computing simple summaries.
Read how Get Programming: Learn to code with Python explains data analysis →
Related concepts
- datauses · Data is represented information, observations, measurements, or other values that can be stored, processed, transmitted, analyzed, or interpreted.
- pandasused by · pandas is an open-source Python library that provides labeled data structures and tools for manipulating and analyzing tabular and other structured data.
- data sciencepart of · Data science is an interdisciplinary field that uses statistical, computational, analytical, and domain-specific methods to extract knowledge and insights from data.
- results-driven approachused by · A results-driven approach is an analytical or problem-solving workflow that prioritizes useful outcome-oriented results and refines work as needed.
- predictive analyticsincludes · Predictive analytics is the use of data and statistical or computational methods to estimate future or otherwise unknown outcomes.
- information retrievalcontrasts with · Information retrieval is the process of locating and selecting relevant information from an available collection in response to a query or information need.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.