The definitive intro to big data science

Explore big data science by blending hacking skills, math and statistics, and domain expertise to reach the central sweet spot where software engineering meets domain knowledge.

Explore unstructured data and its relationship to relational databases, showing how the absence of a predefined schema complicates storage and analysis, and why analysts impose different schemas for use cases.

Discover how tooling and technology drive big data work, focusing on data collection, exploration, and transformation, and the data engineer's role in coding, ETL, and keeping data pipelines in production.

Explore big data terminology and how scaling out drives design. Learn how MapReduce, Hadoop, Spark, BigQuery, and NoSQL address data processing and storage, with Java VM orientation.

Explore key value stores as the core NoSQL distributed data stores, defined by keys and values, with partitioning, replication, and failover using time-based buckets and hash-based mapping.

Learn how big data processing turns storage into insight through distributed batch and real-time paradigms, MapReduce, and a shift from ETL to LTE with raw storage and query-time transforms.

Compare batch mapreduce with streaming processing and uncover on-the-fly computations using tools like Spark Streaming, Storm, and Flink for real-time data.

Build and run a spark batch job by creating a spark session, reading JSON data, filtering and transforming a data frame, and writing output.

Explore how supportive operational tools such as Zookeeper, YARN, and Kafka coordinate distributed servers, manage resources, and enable data movement across the Hadoop ecosystem and big data landscape.

We define supervised machine learning, explain how training data and labels shape models, and contrast it with unsupervised methods, using examples like spam classification and predictive analytics.

Compare supervised learning models using a confusion matrix and metrics, including accuracy, precision, recall, and the F measure, to evaluate binary spam classification.

Explore decision trees, a family of intuitive supervised learning algorithms, using a weather toy example to show how feature splits yield a readable model while managing impurity and overfitting.

Learn Naive Bayes, a Bayes theorem based algorithm using likelihood tables to estimate probabilities, compare with decision trees, and discuss independence assumptions and fast training.

Explore regression as a supervised learning family, starting with linear regression that predicts continuous values with weights and an offset, and learn how root mean squared error evaluates models.

Explore ensemble learners and random forests, which combine multiple simple models like decision trees and naive Bayes to improve accuracy, reduce overfitting, and scale out on big data.

Review supervised machine learning in the data analytics cycle by training on labeled data, evaluating with accuracy, precision, recall, F measure, and comparing trees, naive Bayes, regression, and random forests.

In the definitive intro to big data science, learn k-means clustering, centroid updates, convergence, and the elbow method to choose k, with distance metrics and outliers.

Explore DBSCAN, a density-based clustering algorithm that discovers arbitrarily shaped clusters in data, handles noise, requires no pre-set cluster count, and uses a single scan to group points.

Discover hierarchical clustering, an unsupervised method for unknown numbers of clusters. Compare bottom-up and top-down approaches, read dendrograms for structure, and note distance metrics and comparisons with k-means.

Explore beyond supervised and unsupervised learning with recommender systems, collaborative filtering, association rule analysis, time series, IoT sensor analytics, natural language processing, and next best action models.

Assess machine learning tools for big data, favor scalable Python and Spark ML over memory-heavy options, and advocate blending data scientist and data engineer roles with microservices for production-ready pipelines.