Applied Data Science with SQL, R, and Python

via Coursera

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

15 weeks, 1 hour a week
Sessions

Self-Paced
Level

Intermediate
Subtitles

English

Found in

Overview

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This course equips learners with practical skills in data science using SQL, R, and Python, emphasizing their role in analyzing and interpreting complex datasets. Participants develop the ability to manage, manipulate, and visualize data to support data-driven decisions. Learners progress from foundational SQL concepts to advanced query design, joining, and optimization techniques. They then explore R for statistical analysis and visualization, followed by Python for data processing and creating advanced visualizations with Matplotlib. The course combines theory with hands-on, example-driven lessons, making programming and data analysis accessible. Learners gain applied experience that strengthens their confidence and competence in real-world data science tasks. Data analysts, aspiring data scientists, and professionals seeking practical skills in SQL, R, and Python will benefit from this course. Basic familiarity with computers is helpful but not required. This course is part three of a three-course Specialization designed to provide a comprehensive learning pathway in this subject area. While it delivers standalone value and practical skills, learners seeking a more integrated and in-depth progression may benefit from completing the full Specialization. From Data Analytics & Visualization All-in-One For Dummies Copyright © 2024 by John Wiley & Sons, Inc., Hoboken, New Jersey Used by arrangement with John Wiley & Sons, Inc.

Syllabus

SQL Foundations

This module introduces the foundational concepts of relational databases, highlighting the distinctions between theoretical models and practical SQL implementations. Learners will explore key topics such as sets, relations, tables, functional dependencies, keys, views, and database catalogs to build a solid understanding of database structure and design.

Drilling Down to the SQL Nitty-Gritty

This module introduces essential SQL concepts, including syntax, data types, constraints, and the handling of special values like NULL. Learners will explore how SQL integrates with other programming languages, manage different data types such as numbers, strings, dates, and XML, and understand the importance of reserved words and table constraints. By the end, you'll be equipped to design robust database structures and avoid common pitfalls in SQL programming.

Values, Variables, Functions, and Expressions

This module introduces essential SQL tools for data preprocessing, including the use of set and value functions, expressions, and conditional logic to manipulate and summarize data. Learners will gain practical skills in transforming, analyzing, and converting data types to enhance the accuracy and flexibility of their data analysis and reporting.

SELECT Statements and Modifying Clauses

This module introduces the fundamentals of retrieving and manipulating data in SQL using SELECT statements, WHERE clauses, and aggregation with GROUP BY and HAVING. Learners will gain practical skills in filtering, grouping, and summarizing data efficiently. Common pitfalls and ambiguities in SQL syntax are also discussed to help avoid errors.

Tuning Queries

This module explores practical strategies for optimizing SQL queries to enhance database performance. Learners will investigate how query plans, temporary tables, and specific SQL clauses like ORDER BY and HAVING impact efficiency. By the end, you'll be equipped to analyze and improve query execution in real-world scenarios.

Complex Query Design

This module delves into advanced SQL techniques for combining and manipulating data across multiple tables. Learners will explore various types of subqueries, JOIN and UNION operators, and strategies for optimizing complex queries. Practical applications in SELECT, INSERT, UPDATE, and DELETE statements are also covered, with a focus on performance tuning.

Joining Data Together in SQL

This module introduces the use of SQL JOIN operators to combine data from multiple tables, highlighting the differences between join types and their effects on data retrieval. Learners will explore equi-joins, column-name joins, and the roles of ON and WHERE clauses in constructing efficient queries.

Using Open Source R for Data Science

This module introduces the essentials of R programming for data science, including function creation, iteration techniques, and object-oriented concepts. Learners will explore popular statistical analysis packages and practical applications relevant to AI SaaS development. By the end, participants will be equipped to leverage R's tools for efficient data analysis and visualization.

R: What It Does and How It Does It

This module introduces the R programming language, covering its origins, core concepts in probability and hypothesis testing, and essential data structures such as vectors and data frames. Learners will gain hands-on experience installing R, navigating RStudio, and using basic functions to manipulate and analyze data. By the end, you'll understand how R supports statistical analysis and data organization.

Getting Graphical

This module introduces key data visualization techniques in R, including box plots, bar plots, and scatter plots, to help you uncover patterns and effectively communicate insights from data. Learners will gain practical skills in creating and interpreting various types of graphs for statistical analysis and presentation.

Kicking It Up a Notch to ggplot2

This module introduces the foundational concepts of ggplot2, focusing on its grammar of graphics approach to data visualization in R. Learners will discover how to create and customize bar and scatter plots, and understand the underlying principles that make ggplot2 a powerful tool for effective data interpretation.

Discovering the Match Between Data Science and Python

This module introduces learners to Python's essential role in the data science workflow, highlighting practical applications, key libraries, and the importance of code structure. Participants will gain hands-on experience with Python as they explore the steps involved in building efficient data science pipelines.

Using Python for Data Science and Visualization

This module introduces the foundational elements of Python programming, including data types, loops, functions, and classes, with a focus on their application in data science. Learners will also explore essential Python libraries and practice creating data visualizations using MatPlotLib. By the end, you'll be equipped to manipulate data and automate tasks for AI SaaS development.

Getting a Crash Course in Matplotlib

This module introduces the essentials of 2D data visualization using Matplotlib in Python. Learners will discover how to customize plots, format axes, and enhance clarity with labels, annotations, and legends to effectively communicate data insights.

Visualizing the Data

This module introduces a variety of data visualization techniques using Matplotlib, including histograms, scatterplots, time series, geographic, and network graphs. Learners will discover how to effectively present and interpret data to reveal patterns, trends, and relationships. By the end, you'll be equipped to choose and create the right visualizations for different data types and analytical goals.