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Traditional Machine Learning

Data Analysis for ML/AI

Exploratory analysis, feature thinking, and data quality for downstream modeling.

Beginner 16 hours · Self-paced 99.0 USD · 100 lessons · ~932 min read

20 topics 100 lessons Start anywhere
Grounded in sources, not a frozen script Ideas in this path map to readings and the Research Lab. See how we refresh lessons as the field moves.

What you’ll get out of this course

  • Build practical skill in “Data Analysis for ML/AI” with text-first lessons and clear checkpoints.
  • Level: Beginner—follow the syllabus in order or jump to the modules you need.
  • Reinforce ideas with end-of-topic checks and (where available) hands-on coding tasks.

Trust & quality

Content is designed and maintained by the Deep AI Minds team—structured for working adults, with frequent updates as tooling and best practices evolve.

Content currency: ~100% of lessons on the current curriculum revision

Instructor & outcomes

Deep AI Minds

Curriculum & instruction

Structured, industry-relevant paths with clear checkpoints and refresh cadence.

Satisfaction & billing

30-day satisfaction: if the syllabus or access is not as described, contact support and we will help (refunds for eligible purchases, case by case for integrations).

Common questions

You keep access for the lifetime of the catalog item you purchased, subject to fair use and our terms.

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Review the full syllabus before buying. If something is wrong on our side, reach out and we will make it right.
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Syllabus

We structured this course to build your skills step by step

Scroll through each module below—open lessons in place or jump into a topic. Everything runs in order, but you’re free to explore.

Topic 1
Learning module

The data analysis mindset

How analysts think — questions, assumptions, evidence — before any chart is drawn.

What is data analysis for ML/AI? 10 min
The data analysis pipeline 10 min
Asking the right questions 9 min
Data, information, knowledge 8 min
Analyst vs ML engineer: the handoff 9 min
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Topic 2
Learning module

Data sources and formats

Where data comes from and the formats you'll meet: CSV, JSON, Parquet, SQL, APIs.

CSV and TSV — the universal table format 10 min
JSON and JSON-lines 10 min
Parquet and columnar formats 11 min
Databases and SQL 10 min
APIs and streaming sources 10 min
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Topic 3
Learning module

Loading and saving data

Reading and writing data robustly: encodings, chunks, compression, schemas.

Reading CSV robustly 11 min
Writing data back 9 min
Compression and binary formats 9 min
File paths and storage backends 9 min
Loading data larger than memory 10 min
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Topic 4
Learning module

Data types and schemas

Numeric, string, datetime, categorical — pick the right type and prevent silent bugs.

Numeric types: int, float, decimal 10 min
Strings and categoricals 10 min
Dates and timestamps 11 min
Type coercion pitfalls 10 min
Data schemas and contracts 10 min
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Topic 5
Learning module

Data quality and cleaning

Duplicates, whitespace, casing, type coercion errors, validation rules.

Duplicates: detection and resolution 10 min
Whitespace, casing, and string cleanup 9 min
Validation rules at ingestion 10 min
Standardizing units and formats 10 min
Repeatable, version-controlled cleaning 10 min
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Topic 6
Learning module

Missing values

Patterns of missingness, MAR/MCAR/MNAR, when to drop, when to impute, indicator variables.

MCAR, MAR, and MNAR — kinds of missingness 10 min
Imputation strategies 11 min
When to drop missing values 9 min
Missing-value indicator features 8 min
Common pitfalls with missing data 9 min
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Topic 7
Learning module

Outliers and anomalies

Detection (z-score, IQR, isolation forest), treatment, and the role of domain context.

What is an outlier? 9 min
Z-score and IQR detection 10 min
Multivariate outlier detection 10 min
Treating outliers 9 min
Anomaly detection systems 10 min
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Topic 8
Learning module

Exploratory data analysis

Build intuition before modeling: distributions, outliers, correlations, hypotheses.

Distributions and outliers 7 min
Correlation caveats 7 min
EDA checklist 6 min
The EDA workflow 10 min
Automated EDA tools 8 min
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Topic 9
Learning module

Univariate analysis

One variable at a time: distributions, summaries, transformations.

Describing distributions 10 min
Histograms and density estimation 10 min
Transforms for skewed data 10 min
Summary statistic pitfalls 9 min
A univariate analysis checklist 8 min
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Topic 10
Learning module

Bivariate analysis

Pairs of variables: scatter, correlation, contingency, mutual information.

Scatter plots and what they tell you 9 min
Correlation: Pearson vs Spearman 9 min
Contingency tables and chi-square 9 min
Mutual information 9 min
Practical bivariate analysis recipes 9 min
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Topic 11
Learning module

Visualizing distributions

Histograms, KDEs, violin and box plots — and when each lies to you.

Histograms in depth 10 min
Box plots and violin plots 10 min
ECDFs and percentile plots 9 min
When distribution charts mislead 9 min
Choosing the right chart 8 min
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Topic 12
Learning module

Visualizing relationships

Scatter, regression overlays, hex bins, pair plots, faceting.

Scatter plots and overplotting 9 min
Pair plots and correlation matrices 9 min
Relationships by group (faceting) 9 min
Annotations and storytelling charts 8 min
Interactive and dashboard tools 8 min
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Topic 13
Learning module

Feature distributions for ML

Scaling, transformations, target encoding, and how EDA leaks into models.

Scaling features 10 min
Encoding categorical features 10 min
Transformations that help (or hurt) models 10 min
EDA-induced leakage 9 min
Feature stability over time 9 min
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Topic 14
Learning module

Categorical data analysis

Frequency tables, chi-square, cardinality, rare-category handling.

Frequency tables and visualization 9 min
Handling rare categories 9 min
Categorical association tests 9 min
Encoding strategies for categoricals 9 min
Ordinal and cyclical features 9 min
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Topic 15
Learning module

Time series analysis

Date indexing, resampling, rolling windows, seasonality, autocorrelation.

Time as an index 10 min
Resampling and rolling windows 10 min
Seasonality and trend 10 min
Autocorrelation and stationarity 9 min
A time series analysis checklist 8 min
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Topic 16
Learning module

Text data analysis

Tokenization, vocabularies, n-grams, EDA on text features.

Tokenization fundamentals 10 min
Text normalization: case, accents, unicode 10 min
Stopwords, stemming, and lemmatization 9 min
N-grams, bag-of-words, and TF-IDF 10 min
Text data pitfalls: encodings and dirty inputs 9 min
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Topic 17
Learning module

GroupBy, aggregation, and pivots

Split-apply-combine: aggregations, transforms, pivot tables.

Split-apply-combine: groupby fundamentals 10 min
Named aggregations and multi-metric reports 9 min
Pivot and unpivot: reshaping for analysis 9 min
Window functions: rolling, lag, rank 10 min
Groupby pitfalls and performance 9 min
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Topic 18
Learning module

Joining and merging data

Inner/outer/left/right joins, merge keys, fuzzy matches, time-based joins.

Join fundamentals: keys and cardinality 10 min
Inner, outer, and anti-joins in practice 10 min
Fuzzy matching: dirty keys and entity resolution 10 min
Set operations: union, intersect, except 8 min
Join performance and pitfalls 9 min
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Topic 19
Learning module

Features and leakage

Engineering features that generalize — and avoiding the leakage that wrecks models.

Feature construction: from raw to model-ready 10 min
Target leakage: when the future sneaks into training 10 min
Train/test contamination patterns 9 min
Feature importance and feature redundancy 9 min
A leakage audit checklist before shipping 8 min
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Topic 20
Learning module

Labels and data quality

Label noise, class imbalance, dataset versioning, and audit-grade quality.

Label noise: where dirty labels come from 9 min
Gold sets and inter-annotator agreement 10 min
Weak supervision and programmatic labels 10 min
Label drift and concept change 8 min
Label quality audits before training 8 min
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