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Course Overview and Foundations

Why Data Science & Analytics Matter

Core Tools Setup: Python, R, and SQL

Data Acquisition, SQL, and Data Management

Relational Data and SQL for Analytics

From Raw Data to Clean Tables

Statistical Analysis and Feature Engineering

Statistics for Insight and Inference

Feature Engineering and Model Evaluation

Programming for Analysis: Python and R

Analytical Workflows in Python

Analytical Workflows in R

Big Data and Scalable Analytics

Working with Spark and Hadoop

Data Visualization and Communication

Applied Project and Assessments

Capstone: End-to-End Data Workflow

Quiz: SQL and Data Acquisition

Quiz: Statistics and Inference

Quiz: Feature Engineering and Evaluation

Quiz: Python and R Workflows

Quiz: Big Data Tools and Visualization

Course Feedback Survey

End-to-End Data Science: Python, R, SQL, and Big Data is a practical, portfolio-ready program that takes you from raw data to business impact. You’ll master the full analytics workflow—acquiring data with SQL, cleaning and transforming it in Python and R, applying core statistics, building and evaluating models, and communicating insights with compelling visuals and reproducible reports. Designed for real-world roles, the course maps your learning to day-to-day responsibilities of Data Analysts, Data Scientists, and BI Analysts, so every module translates directly to workplace value. You’ll set up professional environments in Python, R, and SQL, learn how to manage data at scale with Spark, and understand when and how to move beyond spreadsheets and single-machine workflows. Throughout, you’ll apply best practices: version control, documentation, and reproducible pipelines. By the end, you’ll complete an end-to-end capstone that showcases your ability to extract data, engineer features, run statistical analyses, build simple predictive models, and present clear, decision-ready findings. Whether you’re upskilling or pivoting into analytics, this course equips you with the tools, patterns, and confidence to deliver outcomes that matter.

End-to-End Data Science: Python, R, SQL, and Big Data

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            <p>Data increasingly defines how organizations operate, compete, and grow. End-to-End Data Science: Python, R, SQL, and Big Data equips you to turn raw information into reliable, decision-ready insights using a workflow that mirrors professional practice. You will start by understanding where analytics delivers value and how responsibilities differ across roles like Data Analyst, Data Scientist, and Business Intelligence Analyst. From there, you will set up a reproducible toolkit—Python, R, SQL, and Git—so your analyses are consistent, shareable, and ready for real-world collaboration.</p>

<p>The course then moves through the complete lifecycle of data work. You will model business data in relational systems and write production-grade SQL using joins, window functions, and performance-aware techniques to create clean, reusable extracts. You will acquire data from files, APIs, and databases; clean, validate, and document transformations; and store curated datasets for downstream analysis. With a strong foundation, you will conduct exploratory analysis and core statistics—probability, sampling, inference, and regression—before engineering features and evaluating models using disciplined splits, cross-validation, and meaningful metrics.</p>

<p>Hands-on programming modules in both Python and R ensure fluency where it counts: pandas and tidyverse for wrangling, Matplotlib/Seaborn and ggplot2 for visualization, and aligned modeling workflows via scikit-learn, statsmodels, base R, and tidymodels. You will learn to build clear, narrative notebooks and reproducible reports that stakeholders can trust, while integrating SQL and bridging Python–R where advantageous. When data scales, you will transition to the big data ecosystem—HDFS, Hive, and Spark—working with PySpark and sparklyr to read, transform, and optimize large datasets while making cost-aware decisions about when to move beyond pandas/tidyverse.</p>

<p>The program culminates in a capstone where you define a business question mapped to your target role, acquire and extract data via SQL, engineer features in Python or R, perform statistical analysis, and build a well-evaluated model. You will communicate results through effective visualizations, storytelling, and a reproducible report or notebook that ties insights to decisions. Checkpoint quizzes throughout strengthen mastery of SQL and acquisition, statistics and inference, feature engineering and evaluation, Python and R workflows, and big data tools and visualization—ensuring you graduate with an end-to-end skill set ready for the demands of modern data teams.</p>
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