Dell Data Science Optimize D-AA-OP-23

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Master advanced Data Science methods — Hadoop ecosystem, NLP, Social Network Analysis, advanced ML algorithms, and data visualization — to solve real-world business challenges at enterprise scale.

Apache Hadoop Apache Spark Pig & Hive NLP HBase Social Network Analysis Random Forests Data Visualization MapReduce YARN

What You Will Learn

Six Advanced Data Science Modules

Go beyond the fundamentals — master the tools and techniques that professional data scientists use to solve complex real-world problems.

Module 01

MapReduce & Hadoop Core

›MapReduce framework and implementation in Hadoop
›Hadoop Distributed File System (HDFS) — blocks, replication, NameNode/DataNode
›Yet Another Resource Negotiator (YARN) — resource scheduling and cluster management

Module 02

Hadoop Ecosystem & NoSQL

›Apache Pig — data flow scripting for MapReduce
›Apache Hive — SQL-like queries on Hadoop data
›HBase — distributed NoSQL column-family database
›Apache Spark — in-memory distributed processing for real-time analytics

Module 03

Natural Language Processing

›NLP core concepts and four main ambiguity categories
›Text preprocessing — tokenization, stemming, lemmatization, stop-word removal
›Language modeling — n-gram models, probabilistic parsing, and NLP pipelines

Module 04

Social Network Analysis

›Graph theory foundations — nodes, edges, directed and undirected graphs
›Community detection algorithms — modularity, Girvan-Newman, Louvain
›SNA tools — NetworkX, Gephi, and graph database applications

Module 05

Data Science Theory & Methods

›Simulation techniques — Monte Carlo methods and stochastic modelling
›Random Forests — ensemble decision trees, feature importance, and OOB error
›Multinomial Logistic Regression and Maximum Entropy models for multi-class classification

Module 06

Data Visualization

›Perception science and pre-attentive attributes in visualization design
›Visualization of multivariate data — scatter plot matrices, parallel coordinates
›Interactive dashboards and storytelling with data insights

Full Curriculum

6-Module D-AA-OP-23 Programme

Comprehensive advanced data science curriculum from Hadoop architecture through NLP, social network analysis, and visualization methods.

›MapReduce programming model — Map phase, Shuffle and Sort, Reduce phase
›Hadoop Distributed File System (HDFS) — block storage, replication factor, NameNode and DataNode architecture
›Yet Another Resource Negotiator (YARN) — ResourceManager, NodeManager, ApplicationMaster workflow
›Hadoop cluster configuration — core-site.xml, hdfs-site.xml, mapred-site.xml, yarn-site.xml
›Running MapReduce jobs — WordCount example, custom mapper and reducer implementation in Java

›Apache Pig — Pig Latin scripting for ETL workflows, loading and transforming large datasets
›Apache Hive — HiveQL, partitioning, bucketing, and external/managed tables on HDFS
›HBase — row keys, column families, coprocessors, and Bloom filters for NoSQL operations
›Apache Spark — RDDs, DataFrames, Spark SQL, Spark Streaming, and MLlib integration
›NoSQL database selection — HBase vs Cassandra vs MongoDB for different data access patterns

›NLP pipeline — tokenization, POS tagging, named entity recognition, and dependency parsing
›Four main categories of ambiguity — lexical, structural, semantic, and pragmatic ambiguity handling
›Text preprocessing — stop-word removal, stemming (Porter/Snowball), lemmatization, TF-IDF vectors
›Language modeling — n-gram models, smoothing techniques, and perplexity evaluation
›Sentiment analysis, text classification, and topic modeling with LDA on real-world text corpora

›Graph theory foundations — degree, betweenness, closeness, and eigenvector centrality metrics
›Community detection — modularity optimization, Girvan-Newman algorithm, and Louvain method
›Network problems — link prediction, influence maximization, and community evolution tracking
›SNA tools — NetworkX for Python-based graph analysis, Gephi for visual network exploration
›Real-world SNA applications — fraud detection, recommendation systems, and knowledge graphs

›Simulation and stochastic modeling — Monte Carlo methods for risk analysis and uncertainty quantification
›Random Forests — bagging, feature importance via Gini impurity, OOB error estimation, and tuning
›Multinomial Logistic Regression — softmax function, cross-entropy loss, and multi-class classification
›Maximum Entropy (MaxEnt) models — feature templates, regularization, and applications in NLP
›Model evaluation — cross-validation, ROC-AUC, precision-recall curves, and F1 score interpretation

›Pre-attentive processing — colour, shape, motion, and spatial position for effective chart design
›Visualization of multivariate data — scatter plot matrices, parallel coordinate plots, and radar charts
›Time-series and temporal visualization — line charts, area charts, and horizon plots
›Network and hierarchical visualization — force-directed graphs, treemaps, and sunburst diagrams
›Data storytelling — dashboard design principles, annotation, and communicating insights to stakeholders

Live Training

22,500 INR

/ 270 USD

Live Instructor

Mon–Fri · Weekend batches

Recording

Live class recording included

45 Days

1.5 hrs/day weekday · 3 hrs weekend

Course Materials

Study notes and resources

Tech Support

Call / WhatsApp support

Working Hours

Mon–Fri · 9 AM – 6 PM

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Dell Data Science

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Six Advanced Data Science Modules

6-Module D-AA-OP-23 Programme

Ready to Advance Your Data Science Career?