📈 Data Analytics
0.5.dev0+git.20260720.c8f33de - July 20, 2026 16:01 UTC

📈 Data Analytics

A hands-on data-analytics curriculum in eight sections, from first principles to the job hunt — rewritten and cross-linked for scikit-plots.

216 of 216 lessons written across 8 of 8 sections. Each section below is its own browsable mini-course; use the filter to search every lesson at once.

🌱 1. Foundations

The case for data, the analysis process and data life cycle, analytical thinking, and the core tools of the trade. — 27/27 lessons. Open the section

• 001 · Why Data Analytics Matters Today — why organisations that decide with data outperform those that decide by instinct

• 002 · How Data Analytics Improves the Workplace — where analytics pays off day to day: operations, decisions, and shared facts

• 003 · Data-Driven Decision-Making — the loop from question to data to decision, and the evidence it works

• 004 · Detectives and Data Analysts — the investigator’s mindset: questions, evidence, and conclusions that hold up

• 005 · The Six Phases of the Data Analysis Process — ask, prepare, process, analyze, share, act — the map for every project in this course

• 006 · The Origins of Data Analysis and the Many Ways to Structure It — from early statistics to EDA and CRISP-DM: one process, many framings

• 007 · Understanding the Data Ecosystem — the interlocking pieces — sources, storage, tools, people — data moves through

• 008 · Understanding the Data Analysis Process and the Data Life Cycle — two different journeys: what the analyst does vs. what happens to the data

• 009 · Understanding the Data Life Cycle — plan, capture, manage, analyze, archive, destroy — the data’s own biography

• 010 · A Review of the Six Stages of the Data Life Cycle — the six stages consolidated, with what can go wrong at each

• 011 · The Stages of the Data Analysis Process and Their Roles — what each analysis phase contributes, and what it hands to the next

• 012 · Practical Application of the Data Analysis Process — the six phases run end-to-end on a real-shaped business case

• 013 · Analytical Skills and Their Core Components — the five skills — curiosity, context, technical mindset, data design, data strategy

• 014 · Applying Analytical Skills in a Business Context — the five skills at work on a real business problem, phase by phase

• 015 · Analytical Thinking and Its Core Components — the five aspects — visualization, strategy, problem-orientation, correlation, big picture + detail

• 016 · Analytical Thinking and Questions for Problem Solving — turning the aspects into questions: root causes, gaps, and the unconsidered

• 017 · Root Cause Analysis and Business Applications of the Five Whys — Toyota’s why-times-five: digging past symptoms to causes worth fixing

• 018 · Data-Driven Decision-Making and the Role of Analytical Skills — how the five skills power each step of the decision loop

• 019 · Case Studies in Data Analysis and the Practical Impact of Data-Driven Decision-Making — the evidence in action: research findings and worked cases where data changed the outcome

• 020 · Overview of Core Tools Used by Data Analysts — the working toolkit — spreadsheets, SQL, visualization tools, and code — and when each fits

• 021 · The Role of Spreadsheets in Data Analysis and Basic Concepts — the analyst’s first instrument: cells, formulas, and functions in a visible grid

• 022 · The Concept and Basic Use of SQL (Query Language) — asking databases questions: SELECT, FROM, WHERE and why they scale

• 023 · The Role and Importance of Data Visualization — why pictures beat tables: seeing structure that summary numbers hide

• 024 · Industries Where Data Analysts Work and How Data Is Used — the same craft in many rooms: retail to healthcare to public service

• 025 · The Role of Business Tasks in Data Analysis — the question behind the work: how business tasks anchor every analysis

• 026 · Fairness in Data Analysis — analysis that does not create or reinforce bias — and a famous failure to learn from

• 027 · Key Factors to Consider When Choosing a Data Analytics Role — industry, company size, specialisation, growth: weighing where to start

🎯 2. Data-Driven Decisions

Turning questions into decisions: stakeholders, metrics, and communicating results that drive action. — 27/27 lessons. Open the section

• 001 · Using Data Analysis to Choose the Right Advertising Strategy — a worked prediction problem: past campaign data steering the next ad spend

• 002 · Understanding Common Problem Types in Data Analytics — the six shapes of analyst problems, from predictions to patterns

• 003 · Applying Data Analytics Problem Types in Real Business Scenarios — recognising the six types in the wild — and why naming the type speeds the work

• 004 · Why Asking the Right Questions Matters in Data Analytics — SMART questions, and the leading/closed/assuming questions to avoid

• 005 · The Relationship Between Data and Decision-Making — data-driven vs data-inspired: how evidence and judgement actually combine

• 006 · Quantitative and Qualitative Data in Decision-Making — what/how many/how often meets why: two data kinds, one decision

• 007 · Data Creates Value Only When It Is Communicated — an insight nobody hears changes nothing: communication as the last mile

• 008 · The Difference Between Data and Metrics, and the Role of Metrics — from raw facts to quantified goals: what makes a number a metric

• 009 · Dashboards — live metrics in one place: what dashboards are for, and when a report beats one

• 010 · Mathematical Thinking — step-by-step decomposition, orders of magnitude, and choosing data sized to the decision

• 011 · Spreadsheets in Data Analysis — where the spreadsheet sits in the decision workflow, phase by phase

• 012 · Building and Organizing a Spreadsheet — one row per record, clean headers, raw kept raw: layouts that survive analysis

• 013 · How Data Analysts Use Spreadsheets — the everyday spreadsheet toolkit — sort, filter, pivot, chart — mapped to real tasks

• 014 · Spreadsheet Calculations with Formulas — formulas, cell references, and the absolute-vs-relative distinction that makes fill-down work

• 015 · Common Spreadsheet Errors and How to Fix Them — reading #DIV/0!, #VALUE!, #REF!, #NAME?, #N/A — and fixing causes, not symptoms

• 016 · Spreadsheet Functions — named operations — SUM, AVERAGE, IF, VLOOKUP — the analyst’s core vocabulary

• 017 · Defining the Problem Domain — scoping before analysing: problem, stakeholders, deliverables, timeline, success

• 018 · Context and Bias in Data Analysis — context makes data meaningful; unexamined context is where bias enters

• 019 · Stakeholder Expectations in Data Analysis — who has a stake, what they need, and how to align before the work begins

• 020 · Staying Focused on the Project Objective — guarding scope: keeping every step tied to the one question that matters

• 021 · Clear Communication with Stakeholders and Teams — the five Cs of clarity: making findings land with the people who act on them

• 022 · Adapting to Communication Expectations at Work — reading the room: matching channel, register, and detail to each audience

• 023 · Managing Stakeholder Expectations and Project Constraints — the iron triangle of scope, time, and resources — and honest trade-offs

• 024 · Balancing Speed and Accuracy in Data Analysis — fast-enough vs right-enough: matching rigour to the decision’s stakes

• 025 · Sharing Data to Drive Impact — the difference between reporting numbers and actually changing a decision

• 026 · Effective Meetings — purpose, preparation, and follow-up: making the time analysts spend together count

• 027 · Conflict Resolution in the Workplace — when the data disagrees with a person: disagreeing productively and separating issue from ego

📦 3. Data Preparation

Sourcing, structuring, and organising data before analysis: types, formats, databases, and sampling. — 25/25 lessons. Open the section

• 001 · How Data Is Generated and Collected — where data comes from: first/second/third-party sources and how it is produced

• 002 · Choosing the Right Data to Collect — relevance, coverage, and cost: deciding what data a question actually needs

• 003 · Understanding Data Types and Data Formats — nominal, ordinal, discrete, continuous — and the file formats data travels in

• 004 · Structured Data and Data Models — structured vs semi- vs unstructured, and the models that give data its shape

• 005 · Data Types in Spreadsheets — text, number, date, boolean — why a spreadsheet cares what type a cell holds

• 006 · Data Tables (Tabular Data) — the row-and-column table: the workhorse shape of analysable data

• 007 · Wide Data vs. Long Data — two tidy layouts of the same data, and when each one serves

• 008 · Understanding Bias in Data Analysis — what bias is, where it hides, and why unbiased data is the goal not the default

• 009 · Sampling Bias and Unbiased Data — when the sample misrepresents the population — and how random sampling guards against it

• 010 · Common Types of Data Bias — sampling, observer, interpretation, and confirmation bias, with how each distorts

• 011 · Identifying Good Data Sources (ROCCC Framework) — Reliable, Original, Comprehensive, Current, Cited — the marks of trustworthy data

• 012 · Identifying Bad Data Sources (When Data Does Not ROCCC) — reading ROCCC in reverse to spot the data you should not trust

• 013 · Data Ethics in Data Analysis — ownership, transaction transparency, consent, and currency — the ethics of using data

• 014 · Data Privacy in Data Ethics — protecting people’s information: what privacy requires of an analyst

• 015 · Open Data and Openness in Data Ethics — when data should be freely available — and the tension with privacy

• 016 · Databases and Relational Database Concepts — tables, keys, and relationships: how organisational data is really stored

• 017 · Metadata in Databases — data about data: descriptive, structural, and administrative context that makes data usable

• 018 · Metadata Repositories and Data Governance — where metadata is catalogued, and the governance that keeps data trustworthy

• 019 · Accessing Data: Internal and External Sources — getting to the data: what lives inside the organisation versus outside

• 020 · Importing Data into Spreadsheets — getting external data into a sheet cleanly — and the type traps to watch

• 021 · Sorting and Filtering Data in Spreadsheets — ordering and narrowing rows: the two most-used moves for making data legible

• 022 · BigQuery Account Types — sandbox, free tier, and paid — how to access a cloud data warehouse for practice

• 023 · Querying Data with SQL — SELECT, FROM, WHERE: retrieving exactly the rows and columns a question needs

• 024 · Organizing Data for Personal and Work Projects — folder, file, and naming conventions that keep a data project findable and safe

• 025 · Data Security in Spreadsheets — protecting a shared sheet: access control, protected ranges, and safe sharing

🧽 4. Data Cleaning & Preparation

Finding and fixing dirty data: missing values, duplicates, outliers, and validation for trustworthy inputs. — 32/32 lessons. Open the section

• 001 · The Importance of Clean Data — why clean data is the non-negotiable foundation every analysis stands on

• 002 · Data Integrity and Its Risks in Data Analysis — keeping data accurate and consistent through its life — and what threatens it

• 003 · Aligning Data with Business Objectives — checking that the data you have actually fits the question you must answer

• 004 · Handling Insufficient Data in Data Analysis — recognising when there is not enough data, and the honest options when there isn’t

• 005 · Population, Sample Size, and Random Sampling — the whole vs. the part: population, sample, and how to sample fairly

• 006 · Statistical Power in Data Analysis — the chance of detecting a real effect when there is one — and why 0.8 is the norm

• 007 · Sample Size and Data Integrity — how much data is enough: confidence, precision, and the cost of too little

• 008 · Margin of Error — the plus-or-minus around a sample estimate, and how to read it honestly

• 009 · Dirty Data vs. Clean Data — the concrete contrast: what makes data dirty, and what clean looks like beside it

• 010 · The Importance of Clean Data (revisited) — the business cost of dirty data — why cleaning is worth the time it takes

• 011 · Common Issues in Dirty Data — the recurring defects — duplicates, missing, inconsistent, wrong type, outliers

• 012 · Data Cleaning with Spreadsheets — the practical spreadsheet cleaning workflow, defect by defect

• 013 · Cleaning and Merging Multiple Datasets — combining data from several sources cleanly — matching keys, reconciling formats

• 014 · Spreadsheet Tools for Data Cleaning — the built-in cleaning toolkit: dedupe, split, find/replace, validation, formatting

• 015 · Using Spreadsheet Functions for Data Cleaning — TRIM, CLEAN, UPPER, SUBSTITUTE, VALUE and friends — cleaning by formula

• 016 · Viewing Data Differently for More Effective Data Cleaning — sort, filter, pivot, conditional formatting as lenses that reveal hidden defects

• 017 · Data Mapping and the Big Picture of Clean Data — matching fields between sources so merged data stays coherent — cleaning’s big picture

• 018 · Introduction to SQL — why SQL matters for cleaning: transformations that scale and stay reproducible

• 019 · Spreadsheets vs. SQL — when to reach for a sheet and when for a query — strengths, limits, and the handoff

• 020 · Core SQL Queries for Data Cleaning and Analysis — SELECT, WHERE, DISTINCT, GROUP BY — the query patterns that clean and summarise

• 021 · Cleaning Data with SQL: Removing Duplicates and Cleaning String Variables — DISTINCT, GROUP BY, and string functions to dedupe and standardise text in SQL

• 022 · Using CAST to Clean and Format Data in SQL — converting a value from one data type to another — SQL’s type-fixing tool

• 023 · Advanced SQL Functions for Data Cleaning — CASE, SUBSTR, TRIM, and pattern tools for the harder cleaning jobs

• 024 · COALESCE — returning the first non-null value — SQL’s standard handler for missing data

• 025 · Verifying and Reporting Data Integrity — confirming data is sound after cleaning — and telling stakeholders it is

• 026 · Verifying Data-Cleaning Efforts — checking that each cleaning step did what it should, and nothing it shouldn’t

• 027 · Verification Techniques: Using Spreadsheets and SQL to Catch Repeated Errors — concrete checks — counts, distinct values, ranges — that catch recurring defects

• 028 · Documenting Data-Cleaning Changes — recording what was changed and why, so cleaning is reproducible and reviewable

• 029 · Reporting Data-Cleaning Results — communicating what cleaning found and fixed, so stakeholders can trust the data

• 030 · Using Feedback from Data Cleaning to Improve Data Quality — closing the loop: feeding cleaning lessons back to fix problems at the source

• 031 · Refining a Resume for Data Analytics Roles — shaping a resume to show analytical skill — cleaning your own professional data

• 032 · Exploring Data Analyst Job Opportunities — where analyst roles are, and how to read them against your skills and goals

📊 5. Analyze Data

Organising, formatting, aggregating, and computing on data to surface patterns and answer the question. — 30/30 lessons. Open the section

• 001 · Understanding Data Analysis — what analysis actually is: the four phases that turn prepared data into insight

• 002 · Data Organization in Analysis — arranging data so analysis is possible — the organising step before any computation

• 003 · Sorting and Filtering in Data Analysis — the two foundational moves of analysis, and how they differ from cleaning uses

• 004 · Sorting Data in Spreadsheets — ordering rows by one or more columns to surface structure — done safely

• 005 · Sorting and Filtering Data in SQL Using ORDER BY and WHERE — the SQL twins of sort and filter: ORDER BY to order, WHERE to subset

• 006 · Data Formatting and Unit Conversion in Spreadsheets — consistent formats and units — making numbers comparable before analysing them

• 007 · Data Validation in Spreadsheets — rules that constrain what a cell may hold, catching and preventing bad data

• 008 · Combining Data Validation and Conditional Formatting in Spreadsheets — validation to enforce rules, formatting to reveal violations — used together

• 009 · Using CONCAT in SQL to Combine Text from Multiple Columns — joining text from several columns into one — CONCAT and the concatenation operator

• 010 · Working with Strings in Spreadsheets (LEN, LEFT, RIGHT, FIND) — the spreadsheet string toolkit for measuring, extracting, and locating text

• 011 · Problem-Solving and Seeking Help in Data Analysis — the analyst’s debugging mindset, and how to ask for help effectively

• 012 · How to Effectively Search for Solutions Online as a Data Analyst — turning an error or a stuck point into a search that actually finds the answer

• 013 · Choosing the Right Tool in Data Analysis — matching the task to spreadsheet, SQL, or programming — and combining them

• 014 · Preparing Data for VLOOKUP in Spreadsheets — the setup VLOOKUP demands: a clean lookup key in the leftmost column

• 015 · Using VLOOKUP to Combine Data Across Spreadsheets — pulling matching values from another table by a shared key

• 016 · Troubleshooting VLOOKUP and Building a Problem-Solving Framework — why VLOOKUP fails, how to fix it, and a reusable debugging framework

• 017 · Using JOIN in SQL to Combine Tables — combining rows from multiple tables on a matching key — SQL’s core combine

• 018 · Subqueries in SQL — a query nested inside another — using one query’s result within a second

• 019 · Aggregating Data with Subqueries, HAVING, and CASE in SQL — combining aggregation, group filtering, and conditional logic for real analysis

• 020 · Using Spreadsheet Formulas for Sales Trend Analysis — formulas for change over time — growth rates, running totals, period comparisons

• 021 · Using COUNTIF and SUMIF for Conditional Aggregation in Spreadsheets — counting and summing only the rows that meet a condition

• 022 · Using SUMPRODUCT for Advanced Spreadsheet Calculations — multiplying and summing across arrays in one formula — weighted and multi-condition sums

• 023 · Using Pivot Tables for Calculations and Trend Analysis — the spreadsheet’s most powerful summarising tool — grouping and aggregating with drags

• 024 · Using Pivot Table Filters and Calculated Fields for Deeper Analysis — filtering a pivot and adding computed fields — pushing pivots past basic summaries

• 025 · Comparing Calculations in Spreadsheets and SQL — the same aggregate two ways — when each tool suits the calculation

• 026 · Embedding Calculations in SQL Queries — computing derived values inside a query — arithmetic and expressions in SELECT

• 027 · Using GROUP BY and ORDER BY for Aggregated Calculations in SQL — the SQL pivot: grouping to aggregate, ordering the summary

• 028 · Data Validation as an Ongoing Analytical Process — validation not as a one-time gate but a continuous check throughout analysis

• 029 · Temporary Tables and the WITH Clause in SQL — holding intermediate results — CTEs and temp tables that structure complex queries

• 030 · Creating Temporary Tables in SQL — Methods, Trade-offs, and Best Practices — the ways to make a temp table, when each fits, and how to use them well

🎨 6. Data Visualization

Turning results into visuals that inform: chart choice, design principles, and honest, accessible graphics. — 27/27 lessons. Open the section

• 001 · Data Visualization — representing data graphically so patterns and meaning become visible at a glance

• 002 · Connecting Data and Images — how a visualization maps data values to visual properties — the encoding that carries meaning

• 003 · Creating Powerful Data Visualizations: Focus, Structure, and Analytical Purpose — what makes a visualization effective — a clear focus, sound structure, and a purpose

• 004 · Static vs. Dynamic Data Visualizations: Design Tradeoffs, Control, and Interactivity — fixed images versus interactive views — when each serves, and their trade-offs

• 005 · Elements of Art in Data Visualization: Line, Shape, Color, Space, and Movement — the visual building blocks — line, shape, colour, space, movement — applied to charts

• 006 · Choosing the Right Visualization: Audience-Centered Design and Chart Selection — matching chart type to the data, the message, and the audience

• 007 · Design Thinking in Data Visualization: A User-Centered Framework — designing visualizations around the viewer’s needs — a user-centered process

• 008 · Accessibility in Data Visualization: Designing for Everyone — making charts readable by everyone, including people with visual differences

• 009 · Introduction to Tableau — what Tableau is and why analysts use it — visual analytics without heavy coding

• 010 · Getting Started with Tableau Public — the free version: connecting data, building a first view, publishing to the web

• 011 · Creating a CO₂ Emissions Visualization in Tableau Public — a worked Tableau example — turning an emissions dataset into a clear visualization

• 012 · Effective vs. Ineffective Data Visualizations in Tableau — the difference in practice — what makes a Tableau chart clear versus confusing

• 013 · Using Creativity in Tableau — going beyond default charts — custom, expressive visualizations that still communicate

• 014 · Linking Multiple Datasets in Tableau Public — combining data sources inside Tableau — joins and relationships for richer views

• 015 · Data Storytelling: Giving Numbers a Clear and Convincing Voice — wrapping data in narrative so it informs, persuades, and is remembered

• 016 · Engaging Your Audience in Data Storytelling: Identifying the Key Message — finding the one message that matters and building the story around it

• 017 · Data Dashboards: Organizing Insight for Real-Time Decision Making — combining key visualizations into one monitored view for ongoing decisions

• 018 · Using Filters to Create Compelling and Focused Visuals — filtering to sharpen a visualization’s message and enable exploration

• 019 · Structuring a Persuasive Data Presentation: Turning Insights into Story — arranging findings into a presentation that carries an audience to a conclusion

• 020 · Designing Effective Data Presentation Slides: Structure, Visuals, and Professional Impact — slide design that supports the message — clear, visual, uncluttered, professional

• 021 · Using a Strategic Framework to Structure Data Presentations — reusable structures that reliably organize a presentation toward its goal

• 022 · Weaving Data into Presentations: Hypotheses, Context, and the McCandless Method — integrating data with narrative — hypothesis, context, and a four-element method

• 023 · Presentation Skills for Data Analysts: Delivering Insights with Confidence — the delivery half of presenting — voice, pace, presence, and handling nerves

• 024 · Presenting Like a Pro: Best Practices for Data Analysts — the practices that mark a polished, credible data presenter

• 025 · Preparing for Q&A: Anticipating and Responding to Stakeholder Questions — readying for the questions a presentation will draw — anticipation as preparation

• 026 · Handling Objections in Data Presentations: Responding with Confidence and Clarity — responding to challenges and pushback without defensiveness, honestly and calmly

• 027 · Q&A Best Practices: Answering Questions with Clarity and Confidence — the habits of fielding questions well — listen, answer directly, stay honest

🐍 7. Data Analysis Using Python

Doing the whole workflow in Python: NumPy, pandas, and plotting for real analytical tasks. — 33/33 lessons. Open the section

• 001 · Introduction to Python and Programming Fundamentals — why analysts learn Python, and the programming ideas underneath it

• 002 · Python Fundamentals — the core building blocks of Python — values, expressions, statements, and flow

• 003 · Jupyter Notebook and Coding Environments — where analysts write and run Python — notebooks, IDEs, and the interactive workflow

• 004 · Object-Oriented Programming (OOP) in Python — the objects-and-methods model that shapes Python, including pandas and numpy

• 005 · Variables in Python — named containers for values — assigning, reassigning, and using them

• 006 · Naming Conventions and Restrictions in Python — the rules for valid names and the conventions for good ones (PEP 8)

• 007 · Data Types and Type Conversion in Python — the kinds of values Python holds, and converting between them safely

• 008 · Functions in Python — named, reusable blocks of code — defining, calling, arguments, and returns

• 009 · Code Reusability, Modularity, and Clean Code in Python — writing Python that is reusable, modular, and clean — the engineering virtues in code

• 010 · Comments, Algorithms, and Docstrings in Python — documenting code — comments, docstrings, and thinking in algorithms

• 011 · Boolean Data, Comparators, and Logical Operators in Python — True/False values and the comparisons and logic that produce them

• 012 · Branching and Conditional Statements in Python — making code decide — if, elif, and else direct which code runs

• 013 · While Loops and Iteration in Python — repeating code while a condition holds — the while loop and iteration basics

• 014 · For Loops in Python — repeating code over the items of a collection — the for loop

• 015 · range() Function and Loop Control in Python — generating number sequences and controlling loops with break and continue

• 016 · Strings in Python — working with text — the string type, its operations, and its immutability

• 017 · String Indexing and Slicing in Python — reaching into strings by position — single characters and substrings

• 018 · String Formatting with .format() in Python — building strings from values cleanly — the .format() method and f-strings

• 019 · Data Types vs Data Structures & Introduction to Lists — the difference between a value’s type and a structure that holds many, and the list

• 020 · Modifying Lists in Python — changing lists in place — adding, removing, and updating elements

• 021 · Tuples in Python — ordered, immutable collections — like lists that cannot change

• 022 · Advanced Use of Loops, Lists, Tuples & List Comprehension — combining loops and structures, and the concise list comprehension idiom

• 023 · Dictionaries in Python — key-value collections — looking values up by a meaningful key

• 024 · Advanced Dictionary Usage in Python — iterating, nesting, and safely accessing dictionaries for real data

• 025 · Sets in Python — unordered collections of unique values — membership and set operations

• 026 · Libraries, Packages, and Modules in Python — reusing others’ code — importing modules, packages, and the library ecosystem

• 027 · Introduction to NumPy and Vectorization — the numerical library and vectorization — fast array operations without loops

• 028 · NumPy Arrays (ndarray) and Core Concepts — the ndarray, NumPy’s core structure, and how it differs from a list

• 029 · Introduction to Pandas (Data Analysis Library) — the DataFrame library that brings spreadsheet/SQL-style tables to Python

• 030 · Pandas DataFrame & Series — the two core pandas structures — the table (DataFrame) and the column (Series)

• 031 · Boolean Masking in Pandas — filtering rows by a condition — the pandas equivalent of WHERE and spreadsheet filters

• 032 · Grouping and Aggregation in Pandas (groupby, agg) — grouping rows and computing per-group aggregates — pandas’ GROUP BY

• 033 · Combining Data in Pandas (concat and merge) — bringing DataFrames together — stacking with concat, joining on keys with merge

💼 8. Job Search

From portfolio to offer: resumes, the analyst interview, case studies, and landing the role. — 15/15 lessons. Open the section

• 001 · Transferable Skills — abilities that carry across roles and industries — the foundation of a career pivot

• 002 · Career Identity Statement — a concise statement of the unique value you bring to the workforce

• 003 · Career Dreamer (AI Tool for Career Exploration) — an experimental Grow with Google AI tool that maps your experience to career paths

• 004 · Job Search Plan (Using AI Tools) — a structured, deliberate plan for the job search, assisted by AI tools

• 005 · Tailoring Your Resume — adapting a resume to each specific role rather than sending one generic version

• 006 · Using AI to Improve and Tailor Your Resume — using AI assistants to draft, refine, and tailor a resume — with human judgement

• 007 · Building a Professional Online Presence (Personal Brand) — shaping how you appear online — profile, presence, and personal brand

• 008 · Choosing the Right Job Platforms — selecting the job platforms and channels that fit your target roles

• 009 · Job Application Tracking (Using AI + Spreadsheets) — systematically recording applications and their status, with AI and spreadsheets

• 010 · Networking for Job Search — building and using professional relationships to find and reach opportunities

• 011 · Interview Preparation — readying for interviews — research, practice, and knowing your material

• 012 · STAR Method (Behavioral Interview) — a structure for behavioural answers — Situation, Task, Action, Result

• 013 · Using AI (NotebookLM) for Interview Preparation — using a source-grounded AI research tool to prepare from role and company material

• 014 · Practicing Interviews with AI (Gemini Live) — rehearsing interviews aloud in real-time voice conversation with an AI

• 015 · Post-Interview Strategy — what to do after an interview — follow up, reflect, and handle the outcome

🔤 Every lesson, A–Z index

🔠 Open the full alphabetical index

A Review of the Six Stages of the Data Life Cycle (Foundations) Accessibility in Data Visualization: Designing for Everyone (Data Visualization) Accessing Data: Internal and External Sources (Data Preparation) Adapting to Communication Expectations at Work (Data-Driven Decisions) Advanced Dictionary Usage in Python (Data Analysis Using Python) Advanced SQL Functions for Data Cleaning (Data Cleaning & Preparation) Advanced Use of Loops, Lists, Tuples & List Comprehension (Data Analysis Using Python) Aggregating Data with Subqueries, HAVING, and CASE in SQL (Analyze Data) Aligning Data with Business Objectives (Data Cleaning & Preparation) Analytical Skills and Their Core Components (Foundations) Analytical Thinking and Its Core Components (Foundations) Analytical Thinking and Questions for Problem Solving (Foundations) Applying Analytical Skills in a Business Context (Foundations) Applying Data Analytics Problem Types in Real Business Scenarios (Data-Driven Decisions) Balancing Speed and Accuracy in Data Analysis (Data-Driven Decisions) BigQuery Account Types (Data Preparation) Boolean Data, Comparators, and Logical Operators in Python (Data Analysis Using Python) Boolean Masking in Pandas (Data Analysis Using Python) Branching and Conditional Statements in Python (Data Analysis Using Python) Building a Professional Online Presence (Personal Brand) (Job Search) Building and Organizing a Spreadsheet (Data-Driven Decisions) Career Dreamer (AI Tool for Career Exploration) (Job Search) Career Identity Statement (Job Search) Case Studies in Data Analysis and the Practical Impact of Data-Driven Decision-Making (Foundations) Choosing the Right Data to Collect (Data Preparation) Choosing the Right Job Platforms (Job Search) Choosing the Right Tool in Data Analysis (Analyze Data) Choosing the Right Visualization: Audience-Centered Design and Chart Selection (Data Visualization) Cleaning and Merging Multiple Datasets (Data Cleaning & Preparation) Cleaning Data with SQL: Removing Duplicates and Cleaning String Variables (Data Cleaning & Preparation) Clear Communication with Stakeholders and Teams (Data-Driven Decisions) COALESCE (Data Cleaning & Preparation) Code Reusability, Modularity, and Clean Code in Python (Data Analysis Using Python) Combining Data in Pandas (concat and merge) (Data Analysis Using Python) Combining Data Validation and Conditional Formatting in Spreadsheets (Analyze Data) Comments, Algorithms, and Docstrings in Python (Data Analysis Using Python) Common Issues in Dirty Data (Data Cleaning & Preparation) Common Spreadsheet Errors and How to Fix Them (Data-Driven Decisions) Common Types of Data Bias (Data Preparation) Comparing Calculations in Spreadsheets and SQL (Analyze Data) Conflict Resolution in the Workplace (Data-Driven Decisions) Connecting Data and Images (Data Visualization) Context and Bias in Data Analysis (Data-Driven Decisions) Core SQL Queries for Data Cleaning and Analysis (Data Cleaning & Preparation) Creating a CO₂ Emissions Visualization in Tableau Public (Data Visualization) Creating Powerful Data Visualizations: Focus, Structure, and Analytical Purpose (Data Visualization) Creating Temporary Tables in SQL — Methods, Trade-offs, and Best Practices (Analyze Data) Dashboards (Data-Driven Decisions) Data Cleaning with Spreadsheets (Data Cleaning & Preparation) Data Creates Value Only When It Is Communicated (Data-Driven Decisions) Data Dashboards: Organizing Insight for Real-Time Decision Making (Data Visualization) Data Ethics in Data Analysis (Data Preparation) Data Formatting and Unit Conversion in Spreadsheets (Analyze Data) Data Integrity and Its Risks in Data Analysis (Data Cleaning & Preparation) Data Mapping and the Big Picture of Clean Data (Data Cleaning & Preparation) Data Organization in Analysis (Analyze Data) Data Privacy in Data Ethics (Data Preparation) Data Security in Spreadsheets (Data Preparation) Data Storytelling: Giving Numbers a Clear and Convincing Voice (Data Visualization) Data Tables (Tabular Data) (Data Preparation) Data Types and Type Conversion in Python (Data Analysis Using Python) Data Types in Spreadsheets (Data Preparation) Data Types vs Data Structures & Introduction to Lists (Data Analysis Using Python) Data Validation as an Ongoing Analytical Process (Analyze Data) Data Validation in Spreadsheets (Analyze Data) Data Visualization (Data Visualization) Data-Driven Decision-Making (Foundations) Data-Driven Decision-Making and the Role of Analytical Skills (Foundations) Databases and Relational Database Concepts (Data Preparation) Defining the Problem Domain (Data-Driven Decisions) Design Thinking in Data Visualization: A User-Centered Framework (Data Visualization) Designing Effective Data Presentation Slides: Structure, Visuals, and Professional Impact (Data Visualization) Detectives and Data Analysts (Foundations) Dictionaries in Python (Data Analysis Using Python) Dirty Data vs. Clean Data (Data Cleaning & Preparation) Documenting Data-Cleaning Changes (Data Cleaning & Preparation) Effective Meetings (Data-Driven Decisions) Effective vs. Ineffective Data Visualizations in Tableau (Data Visualization) Elements of Art in Data Visualization: Line, Shape, Color, Space, and Movement (Data Visualization) Embedding Calculations in SQL Queries (Analyze Data) Engaging Your Audience in Data Storytelling: Identifying the Key Message (Data Visualization) Exploring Data Analyst Job Opportunities (Data Cleaning & Preparation) Fairness in Data Analysis (Foundations) For Loops in Python (Data Analysis Using Python) Functions in Python (Data Analysis Using Python) Getting Started with Tableau Public (Data Visualization) Grouping and Aggregation in Pandas (groupby, agg) (Data Analysis Using Python) Handling Insufficient Data in Data Analysis (Data Cleaning & Preparation) Handling Objections in Data Presentations: Responding with Confidence and Clarity (Data Visualization) How Data Analysts Use Spreadsheets (Data-Driven Decisions) How Data Analytics Improves the Workplace (Foundations) How Data Is Generated and Collected (Data Preparation) How to Effectively Search for Solutions Online as a Data Analyst (Analyze Data) Identifying Bad Data Sources (When Data Does Not ROCCC) (Data Preparation) Identifying Good Data Sources (ROCCC Framework) (Data Preparation) Importing Data into Spreadsheets (Data Preparation) Industries Where Data Analysts Work and How Data Is Used (Foundations) Interview Preparation (Job Search) Introduction to NumPy and Vectorization (Data Analysis Using Python) Introduction to Pandas (Data Analysis Library) (Data Analysis Using Python) Introduction to Python and Programming Fundamentals (Data Analysis Using Python) Introduction to SQL (Data Cleaning & Preparation) Introduction to Tableau (Data Visualization) Job Application Tracking (Using AI + Spreadsheets) (Job Search) Job Search Plan (Using AI Tools) (Job Search) Jupyter Notebook and Coding Environments (Data Analysis Using Python) Key Factors to Consider When Choosing a Data Analytics Role (Foundations) Libraries, Packages, and Modules in Python (Data Analysis Using Python)

Linking Multiple Datasets in Tableau Public (Data Visualization) Managing Stakeholder Expectations and Project Constraints (Data-Driven Decisions) Margin of Error (Data Cleaning & Preparation) Mathematical Thinking (Data-Driven Decisions) Metadata in Databases (Data Preparation) Metadata Repositories and Data Governance (Data Preparation) Modifying Lists in Python (Data Analysis Using Python) Naming Conventions and Restrictions in Python (Data Analysis Using Python) Networking for Job Search (Job Search) NumPy Arrays (ndarray) and Core Concepts (Data Analysis Using Python) Object-Oriented Programming (OOP) in Python (Data Analysis Using Python) Open Data and Openness in Data Ethics (Data Preparation) Organizing Data for Personal and Work Projects (Data Preparation) Overview of Core Tools Used by Data Analysts (Foundations) Pandas DataFrame & Series (Data Analysis Using Python) Population, Sample Size, and Random Sampling (Data Cleaning & Preparation) Post-Interview Strategy (Job Search) Practical Application of the Data Analysis Process (Foundations) Practicing Interviews with AI (Gemini Live) (Job Search) Preparing Data for VLOOKUP in Spreadsheets (Analyze Data) Preparing for Q&A: Anticipating and Responding to Stakeholder Questions (Data Visualization) Presentation Skills for Data Analysts: Delivering Insights with Confidence (Data Visualization) Presenting Like a Pro: Best Practices for Data Analysts (Data Visualization) Problem-Solving and Seeking Help in Data Analysis (Analyze Data) Python Fundamentals (Data Analysis Using Python) Q&A Best Practices: Answering Questions with Clarity and Confidence (Data Visualization) Quantitative and Qualitative Data in Decision-Making (Data-Driven Decisions) Querying Data with SQL (Data Preparation) range() Function and Loop Control in Python (Data Analysis Using Python) Refining a Resume for Data Analytics Roles (Data Cleaning & Preparation) Reporting Data-Cleaning Results (Data Cleaning & Preparation) Root Cause Analysis and Business Applications of the Five Whys (Foundations) Sample Size and Data Integrity (Data Cleaning & Preparation) Sampling Bias and Unbiased Data (Data Preparation) Sets in Python (Data Analysis Using Python) Sharing Data to Drive Impact (Data-Driven Decisions) Sorting and Filtering Data in Spreadsheets (Data Preparation) Sorting and Filtering Data in SQL Using ORDER BY and WHERE (Analyze Data) Sorting and Filtering in Data Analysis (Analyze Data) Sorting Data in Spreadsheets (Analyze Data) Spreadsheet Calculations with Formulas (Data-Driven Decisions) Spreadsheet Functions (Data-Driven Decisions) Spreadsheet Tools for Data Cleaning (Data Cleaning & Preparation) Spreadsheets in Data Analysis (Data-Driven Decisions) Spreadsheets vs. SQL (Data Cleaning & Preparation) Stakeholder Expectations in Data Analysis (Data-Driven Decisions) STAR Method (Behavioral Interview) (Job Search) Static vs. Dynamic Data Visualizations: Design Tradeoffs, Control, and Interactivity (Data Visualization) Statistical Power in Data Analysis (Data Cleaning & Preparation) Staying Focused on the Project Objective (Data-Driven Decisions) String Formatting with .format() in Python (Data Analysis Using Python) String Indexing and Slicing in Python (Data Analysis Using Python) Strings in Python (Data Analysis Using Python) Structured Data and Data Models (Data Preparation) Structuring a Persuasive Data Presentation: Turning Insights into Story (Data Visualization) Subqueries in SQL (Analyze Data) Tailoring Your Resume (Job Search) Temporary Tables and the WITH Clause in SQL (Analyze Data) The Concept and Basic Use of SQL (Query Language) (Foundations) The Difference Between Data and Metrics, and the Role of Metrics (Data-Driven Decisions) The Importance of Clean Data (Data Cleaning & Preparation) The Importance of Clean Data (revisited) (Data Cleaning & Preparation) The Origins of Data Analysis and the Many Ways to Structure It (Foundations) The Relationship Between Data and Decision-Making (Data-Driven Decisions) The Role and Importance of Data Visualization (Foundations) The Role of Business Tasks in Data Analysis (Foundations) The Role of Spreadsheets in Data Analysis and Basic Concepts (Foundations) The Six Phases of the Data Analysis Process (Foundations) The Stages of the Data Analysis Process and Their Roles (Foundations) Transferable Skills (Job Search) Troubleshooting VLOOKUP and Building a Problem-Solving Framework (Analyze Data) Tuples in Python (Data Analysis Using Python) Understanding Bias in Data Analysis (Data Preparation) Understanding Common Problem Types in Data Analytics (Data-Driven Decisions) Understanding Data Analysis (Analyze Data) Understanding Data Types and Data Formats (Data Preparation) Understanding the Data Analysis Process and the Data Life Cycle (Foundations) Understanding the Data Ecosystem (Foundations) Understanding the Data Life Cycle (Foundations) Using a Strategic Framework to Structure Data Presentations (Data Visualization) Using AI (NotebookLM) for Interview Preparation (Job Search) Using AI to Improve and Tailor Your Resume (Job Search) Using CAST to Clean and Format Data in SQL (Data Cleaning & Preparation) Using CONCAT in SQL to Combine Text from Multiple Columns (Analyze Data) Using COUNTIF and SUMIF for Conditional Aggregation in Spreadsheets (Analyze Data) Using Creativity in Tableau (Data Visualization) Using Data Analysis to Choose the Right Advertising Strategy (Data-Driven Decisions) Using Feedback from Data Cleaning to Improve Data Quality (Data Cleaning & Preparation) Using Filters to Create Compelling and Focused Visuals (Data Visualization) Using GROUP BY and ORDER BY for Aggregated Calculations in SQL (Analyze Data) Using JOIN in SQL to Combine Tables (Analyze Data) Using Pivot Table Filters and Calculated Fields for Deeper Analysis (Analyze Data) Using Pivot Tables for Calculations and Trend Analysis (Analyze Data) Using Spreadsheet Formulas for Sales Trend Analysis (Analyze Data) Using Spreadsheet Functions for Data Cleaning (Data Cleaning & Preparation) Using SUMPRODUCT for Advanced Spreadsheet Calculations (Analyze Data) Using VLOOKUP to Combine Data Across Spreadsheets (Analyze Data) Variables in Python (Data Analysis Using Python) Verification Techniques: Using Spreadsheets and SQL to Catch Repeated Errors (Data Cleaning & Preparation) Verifying and Reporting Data Integrity (Data Cleaning & Preparation) Verifying Data-Cleaning Efforts (Data Cleaning & Preparation) Viewing Data Differently for More Effective Data Cleaning (Data Cleaning & Preparation) Weaving Data into Presentations: Hypotheses, Context, and the McCandless Method (Data Visualization) While Loops and Iteration in Python (Data Analysis Using Python) Why Asking the Right Questions Matters in Data Analytics (Data-Driven Decisions) Why Data Analytics Matters Today (Foundations) Wide Data vs. Long Data (Data Preparation) Working with Strings in Spreadsheets (LEN, LEFT, RIGHT, FIND) (Analyze Data)

Tags: purpose: reference topic: data analytics