Dell Data Science Foundations D-DS-FN-23

Foundations

Build the practical foundation skills required by a data scientist — covering the analytics lifecycle, eight ML methods, Big Data technologies, and data visualisation to immediately contribute to analytics projects.

Big Data Analytics Analytics Lifecycle R Programming K-means Clustering Linear Regression Decision Trees Time Series Apache Hadoop MapReduce Data Visualisation

What You Will Learn

Six Data Science Foundation Modules

From Big Data concepts through eight core ML methods, Hadoop ecosystem, and data visualisation — build the practical skills to immediately contribute to analytics projects.

Module 01

Big Data, Analytics & the Data Scientist Role

›Define and describe the five Vs of Big Data — Volume, Velocity, Variety, Veracity, Value
›Business drivers for Big Data analytics — competitive advantage, cost reduction, and innovation
›Data Scientist role — responsibilities, skills profile, and how they work within analytics teams
›Differences between business analysts, data analysts, data scientists, and data engineers

Module 02

Data Analytics Lifecycle

›Data analytics lifecycle purpose — six-phase iterative process for analytics projects
›Discovery — framing the problem, understanding business requirements, and forming hypotheses
›Data preparation — ETLT, conditioning, transforming, and survey techniques using R and Python
›Model planning — selecting algorithms, variable selection, and exploratory analytics approach
›Model building — training, testing, validation splits, and performance evaluation methodology

Module 03

Initial Analysis of the Data

›Basic R commands — loading data, data frames, summary statistics, and plotting functions
›Important statistical measures — mean, median, variance, standard deviation, correlation, and skewness
›Effective visualisations — choosing the right chart type for the data and audience
›Hypothesis testing theory — null hypothesis, p-values, significance levels, and Type I/II errors

Module 04

Eight Analytics Methods

›K-means clustering — centroid algorithm, choosing k (elbow method), and cluster interpretation
›Association rules — Apriori algorithm, support, confidence, lift, and market basket analysis
›Linear regression — OLS, R-squared, residual analysis, and multicollinearity diagnostics
›Logistic regression — log-odds, sigmoid function, and binary classification evaluation
›Naïve Bayes — conditional probability, feature independence assumption, and text classification
›Decision trees — CART, Gini impurity, entropy, pruning, and feature importance
›Time series analysis — stationarity, ACF/PACF, ARIMA modelling, and forecasting
›Text analytics — bag-of-words, TF-IDF, sentiment analysis, and topic modelling with LDA

Module 05

Advanced Analytics for Big Data — Technology & Tools

›Technological challenges of Big Data — storage, processing, and query performance at scale
›Nature and use of MapReduce — map, shuffle-sort, and reduce phases with Hadoop integration
›Hadoop ecosystem — HDFS, YARN, Hive, HBase, Pig, and Spark use cases
›In-database analytics — SQL for analytics, in-database machine learning with MADlib
›Advanced SQL — window functions (OVER/PARTITION BY), ordered aggregates, and analytical functions

Module 06

Operationalising Analytics & Data Visualisation

›Best practices for communicating findings — executive summaries, technical reports, and dashboards
›Project presentations for specific audiences — structuring narrative for business vs technical stakeholders
›Operationalising analytics — model deployment, scoring pipelines, and model monitoring
›Effective data visualisations — pre-attentive attributes, chart selection guide, and avoiding chartjunk
›Tools overview — Tableau, Power BI, ggplot2, matplotlib, and seaborn for analytics reporting

Full Curriculum

6-Module D-DS-FN-23 Programme

Complete data science foundations from Big Data concepts through eight ML methods, Hadoop ecosystem, and professional data visualisation and communication.

›Five Vs of Big Data — Volume (petabyte scale), Velocity (streaming), Variety (structured/unstructured/semi-structured), Veracity (quality), and Value (ROI)
›Business drivers — personalisation at scale, predictive maintenance, fraud detection, and supply chain optimisation
›Data Scientist profile — programming (R/Python/SQL), statistics, domain knowledge, and communication skills
›Team roles — business analyst, data analyst, data scientist, data engineer, and ML engineer collaboration model

›Discovery phase — problem framing, stakeholder interviews, data availability assessment, and initial hypothesis formation
›Data preparation (ETLT) — extract, transform, load/transform — data conditioning, outlier handling, and feature engineering
›Data preparation tools — R dplyr, Python pandas, SQL CTEs, and data profiling techniques
›Model planning — algorithm selection framework, supervised vs unsupervised learning decision process
›Model building — train/test/validation split strategies, cross-validation, and hyperparameter tuning approaches
›Communicate results — data storytelling structure, executive dashboard design, and technical report writing

›R data structures — vectors, data frames, matrices, lists, and factors — loading CSV, JSON, and database sources
›Descriptive statistics in R — summary(), mean(), sd(), cor(), quantile(), and table() functions
›Statistical visualisations — histograms, box plots, scatter plots, Q-Q plots, and correlation matrices
›Hypothesis testing — formulating H0 and H1, choosing t-test vs chi-square vs ANOVA, interpreting p-values
›Confidence intervals and effect size — practical vs statistical significance, and reporting test results

›K-means clustering — algorithm walkthrough, choosing k (elbow, silhouette), and cluster profiling for business insight
›Association rules — Apriori and FP-Growth algorithms, rule generation, pruning, and product recommendation applications
›Linear regression — assumptions (LINE), OLS parameter estimation, R-squared, RMSE, and prediction intervals
›Logistic regression — binary and multinomial logistic, confusion matrix, ROC/AUC, precision-recall trade-off
›Naïve Bayes — Gaussian NB, Multinomial NB, Laplace smoothing, and spam/text classification use cases
›Decision trees — ID3, C4.5, CART algorithms — splitting criteria, tree depth, pruning, and feature importance
›Time series analysis — ACF/PACF plots, stationarity tests (ADF), ARIMA identification, and forecast evaluation
›Text analytics — tokenisation, TF-IDF vectors, Naive Bayes for sentiment, and LDA for topic modelling

›Big Data challenges — 3-tier storage architectures, in-memory processing requirements, and parallel query optimisation
›MapReduce deep dive — mapper emit (key, value) patterns, combiner optimisation, and reducer aggregation strategies
›Hadoop ecosystem components — HDFS (NameNode/DataNode), YARN (ResourceManager/NodeManager), Hive (HQL), HBase (columns), Pig (Latin), Spark (RDD/DataFrame)
›In-database analytics — pushing computation to data, MADlib functions for regression and clustering inside PostgreSQL/Greenplum
›Advanced SQL for analytics — window functions (ROW_NUMBER, RANK, LAG, LEAD), ordered aggregates (PERCENTILE_CONT), and running totals

›Operationalising analytics — model serialisation (pickle/joblib), REST API scoring endpoints, and batch scoring pipelines
›Model monitoring — data drift detection, performance degradation alerts, and retraining triggers
›Communication best practices — pyramid principle for executive summaries, BLUF (Bottom Line Up Front) for technical reports
›Audience-specific presentations — board-level dashboards vs analyst deep-dives vs engineering specifications
›Data visualisation best practices — pre-attentive attributes (colour, shape, position), Gestalt principles, and chart type selection guide
›Tools — Tableau Public, Power BI, R ggplot2, Python seaborn/matplotlib/plotly, and Observable notebooks

Live Training

Live Instructor

Mon–Fri · Weekend batches

Recording

Live class recording included

30 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

Foundations

Six Data Science Foundation Modules

6-Module D-DS-FN-23 Programme

Ready to Launch Your Data Science Career?