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⇱ Applied Data Analytics with Python and SQL | Coursera

Applied Data Analytics with Python and SQL

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Applied Data Analytics with Python and SQL

Instructor: Edureka

1,985 already enrolled

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Write Python programs using core concepts like variables, data types, and control flow.

  • Implement NumPy and Pandas to analyze and process large datasets efficiently and accurately.

  • Create insightful data visualizations with Matplotlib, Seaborn, and Plotly for effective reporting.

  • Execute SQL queries to retrieve, manage, and analyze data from relational databases accurately.

Details to know

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Assessments

15 assignments

Taught in English

Build your subject-matter expertise

This course is part of the Applied Data Analytics Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 4 modules in this course

Learn how to work with data using Python and SQL in a practical, project-based course designed to give you relevant, industry-ready skills. You’ll gain hands-on experience manipulating, querying, and analyzing large datasets preparing you to solve real-world data challenges in business, science, or technology.

By the end of this course, you’ll know how to: - Process and analyze data efficiently using Python, with a focus on Pandas and NumPy for manipulation and cleaning. - Write SQL queries to retrieve, filter, and aggregate data in relational databases. - Handle data files (CSV, Excel, JSON) and automate common data tasks for faster, repeatable analysis. - Visualize and report insights from your data to support decision-making. This course is designed for beginners, data enthusiasts, and aspiring data analysts and scientists. No prior programming experience is required, but some familiarity with databases or basic computing concepts will help you progress. You’ll finish with solid skills in Python scripting, SQL, and data analysis, giving you a strong foundation for further study or entry-level roles in data-driven industries.

This module is designed to help learners take a significant step towards launching their careers in data analytics. In the first week, we'll explore how Python programming concepts that are essential for creating efficient programs. Additionally, we'll delve into the role of pre-defined Python features in data analysis. Let's get started!

What's included

38 videos8 readings5 assignments3 discussion prompts

38 videos•Total 190 minutes
  • Course Introduction•6 minutes
  • Programming Languages and Myths•7 minutes
  • Python for AI/ML - Code Simplicity•4 minutes
  • Python for AI/ML - Ease of Learning•5 minutes
  • Identifiers in Python•5 minutes
  • Literals in Python•7 minutes
  • Arithmetic, Comparison, Logical, and Assignment Operators•6 minutes
  • Bitwise, and Membership Operators•6 minutes
  • Python Token Types•5 minutes
  • Boolean Operators in Conditional Statement•4 minutes
  • AND, OR, NOT in Python•4 minutes
  • Working with Dictionaries•4 minutes
  • Manipulating Dictionary•5 minutes
  • Tuples in Python•4 minutes
  • Working with Strings in Python•5 minutes
  • Managing Strings•4 minutes
  • Operations on String•5 minutes
  • Creating Lists•4 minutes
  • Immutable and Mutable Data Types•5 minutes
  • Operations Performed on Lists•6 minutes
  • Sets •7 minutes
  • Manipulating Sets •1 minute
  • Exploring Python's Built-in Functions•5 minutes
  • Conditional Statement - if condition•3 minutes
  • Conditional Statement - if else condition•5 minutes
  • Initializing For loop•7 minutes
  • Break, Continue and Pass Statement•4 minutes
  • Validating Prime Numbers•4 minutes
  • Mastering the While Loop in Python•6 minutes
  • Opening Files•5 minutes
  • File Operations•6 minutes
  • Global vs. Local Variables in Python•8 minutes
  • Introduction to Lambda Functions in Python•7 minutes
  • Built-in Modules •6 minutes
  • Standard Modules•6 minutes
  • Creating User Defined Functions•6 minutes
  • Functions with Multiple Parameters•4 minutes
  • Summary of Python Essentials•2 minutes
8 readings•Total 80 minutes
  • Welcome to Applied Data Analytics with Python and SQL•10 minutes
  • Starting with Python•10 minutes
  • An Introduction to Python Data Types•10 minutes
  • Built-in Functions in Python•10 minutes
  • Conditional Statements and For Loop in Python•10 minutes
  • Control Flow with Python: break, continue, and pass Statements•10 minutes
  • Lambda Functions in Python•10 minutes
  • File Handling with Python•10 minutes
5 assignments•Total 42 minutes
  • Practice Quiz : Python Programming Basics•3 minutes
  • Practice Quiz : Working with Data Types in Python•3 minutes
  • Practice Quiz : Conditional and Control Flow Statements•3 minutes
  • Practice Quiz : Handling Files and File Operations•3 minutes
  • Knowledge Check: Python Essentials for Programming•30 minutes
3 discussion prompts•Total 30 minutes
  • Introduce Yourself•10 minutes
  • Differences in Sets, Tuples and Dictionary•10 minutes
  • Have You Worked on Other Programming Language apart for Python•10 minutes

During week two of the course, Learners will be taught how to handle data with NumPy and Pandas in various formats while developing skills to represent data visually using different types of charts and graphics.

What's included

19 videos5 readings4 assignments

19 videos•Total 91 minutes
  • Introduction to NumPy in Python•6 minutes
  • Hands-On with NumPy•5 minutes
  • Introduction to Pandas in Python•4 minutes
  • Working with Pandas Series•5 minutes
  • Understanding Pandas DataFrames•5 minutes
  • Data Manipulation with Pandas•4 minutes
  • Combining Datasets with Pandas•6 minutes
  • Matplotlib Library•5 minutes
  • Plotting Charts•5 minutes
  • Plotting Histogram and Box Plot•3 minutes
  • Plotting Multiple Charts•6 minutes
  • Introduction to Seaborn - Scatter Plot•6 minutes
  • Seaborn Basic Charts•6 minutes
  • Seaborn - HeatMap Manipulation•2 minutes
  • Seaborn : Flights Dataset•5 minutes
  • Seaborn: Gaining Insights in Flight Data•5 minutes
  • Visualizing Charts with Plotly•5 minutes
  • Customizing different charts in Plotly•6 minutes
  • Summary of Exploring with NumPy and Pandas •2 minutes
5 readings•Total 60 minutes
  • NumPy in Python•10 minutes
  • Getting Started with Pandas•10 minutes
  • Data Visualization Fundamentals•20 minutes
  • Data Visualization with Matplotlib•10 minutes
  • Visualization with Seaborn•10 minutes
4 assignments•Total 39 minutes
  • Practice Quiz : Working with NumPy•3 minutes
  • Practice Quiz : Handling Data using Pandas•3 minutes
  • Practice Quiz : Visualizing Data in Python•3 minutes
  • Knowledge Check: NumPy, Pandas and Data Visualization Libraries•30 minutes

In this module, learners will explore methods for gathering information from various sources on the internet. They will utilize different Python libraries to process and organize the data for thorough evaluation, as well as connect MS SQL Server with Python.

What's included

33 videos4 readings5 assignments

33 videos•Total 155 minutes
  • What is Web Scraping?•6 minutes
  • Web Scraping Process Flow•5 minutes
  • Managing Data•6 minutes
  • Beautiful Soup•5 minutes
  • Demonstration of Beautiful Soup•5 minutes
  • Demonstration of Scrapy•3 minutes
  • What is SQL?•5 minutes
  • Operation on Keys•5 minutes
  • Normalization in Databases•4 minutes
  • Types of Normalization 1 NF•3 minutes
  • Types of Normalization 2 NF•2 minutes
  • Types of Normalization 3 NF•5 minutes
  • Types of Normalization 4 NF•2 minutes
  • Types of Normalization 5 NF•4 minutes
  • Installation of MS SQL Server•6 minutes
  • DDL Commands•6 minutes
  • DQL Commands•6 minutes
  • DML Commands•7 minutes
  • DCL Commands•5 minutes
  • TCL Commands•7 minutes
  • Setting Up SQL Server•4 minutes
  • Managing Foreign Key •3 minutes
  • Inserting Data•7 minutes
  • Deleting Data•3 minutes
  • Alter Command•5 minutes
  • Setting up Transaction•4 minutes
  • Connecting with Database•7 minutes
  • Creating Table•7 minutes
  • Inserting Data •7 minutes
  • Selecting Data•1 minute
  • Updating Records•4 minutes
  • Deleting Records•4 minutes
  • Summary of Web Scraping and SQL•1 minute
4 readings•Total 40 minutes
  • Web Scraping Best Practices•10 minutes
  • Difference Between MS SQL Server and MySQL •10 minutes
  • SQL Commands•10 minutes
  • Python with Microsoft SQL Server•10 minutes
5 assignments•Total 42 minutes
  • Practice Quiz : Web Scraping with Python•3 minutes
  • Practice Quiz : SQL and Normalization•3 minutes
  • Practice Quiz : Operations on SQL•3 minutes
  • Practice Quiz : Integrating SQL with Python•3 minutes
  • Knowledge Check : SQL, Normalizations and Web Scraping with Python•30 minutes

This module is designed to assess an individual on the various concepts and teachings covered in this course. Evaluate your knowledge with a comprehensive graded quiz on Python programming concepts, Data manipulation with NumPy and Pandas with Web-Scraping with Python

What's included

1 video1 reading1 assignment1 discussion prompt

1 video•Total 2 minutes
  • Course Summary: Applied Data Analytics with Python and SQL•2 minutes
1 reading•Total 30 minutes
  • Practice Project: Data Analysis of Police Records•30 minutes
1 assignment•Total 30 minutes
  • End Course Knowledge Check: Python and Statistics•30 minutes
1 discussion prompt•Total 10 minutes
  • Describe Your Learning Journey•10 minutes

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Instructor

Edureka
203 Courses•185,724 learners

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Frequently asked questions

This course offers a thorough practical application of Python and SQL. Created for Python enthusiasts, it focuses on hands-on skills for manipulating and analyzing data, allowing students to address actual data problems.

Learners will delve into the basics of Python and utilize tools such as NumPy and Pandas for effective data management. They will additionally make use of visualization tools like Matplotlib, Seaborn, and Plotly to present their findings in an efficient manner. Moreover, the course includes instruction on utilizing SQL for data manipulation, empowering students with the skills needed to make informed decisions based on data.

At the conclusion of the course, you will be fully equipped to utilize these skills in different data analysis situations, establishing a solid groundwork for future studies in data analytics.

This course is designed for:

- Freshers looking to enter the fields of data analysis, data science, or artificial intelligence.

- Individuals with a keen interest in programming who want to enhance their technical skills.

- Professionals seeking to upskill in Python and data manipulation with SQL for practical applications in their careers.

- Anyone curious about data and eager to learn how to analyze and visualize it effectively.

Whether you're starting your tech journey or looking to build a strong foundation, this course will guide you through the essentials of Python and SQL.

You’ll primarily use Python for programming and SQL for data handling, along with visualization libraries and database tools.

The course utilizes Google Colab and MS SQL Server as the primary platform for coding operations. Learners may also use integrated development environments (IDEs) like Jupyter Notebook, PyCharm, Spyder, or VS Code for more extensive coding projects if desired.

Install MS SQL Server and required Python libraries with corresponding IDE's such as PyCharm, Spyder, VS Code etc.

No prior experience is required; the course starts with fundamentals and builds up step by step.

Yes, the skills are practical for entry-level analytics roles and form a foundation for advanced study in data science.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Financial aid available,