Data Analytics Projects

1. Implementation of Star Schema-Based Sales Analysis
2. E-Commerce Sales Analysis Using Power BI 
3. Amazon Marketplace Inside Console using Power BI
4. Real-Time Business Monthly Data Analysis
5. HealthCare Prediction on Diabetic Patients Using Python

Full Stack Web Development Projects

1. E-Commerce Platform with Python Django
2. Real-Time Chat Application with Django Channels
3. Quiz Master: MCQ Quiz Platform with Python Django
4. Healthcare Administration System (HAM): Develop a Hospital Website using the Django Framework
5. Unisys Portal: College Management System with Python Django

AICTE EY-GDS Internship December’23

Self-Paced Study Material for Internship 

Introduction to Python: Python, created by Guido van Rossum, is a versatile programming language widely used for web development, data analysis, artificial intelligence, and more.

Setting up your Python environment: Choose an Integrated Development Environment (IDE) like Jupyter or VSCode and install libraries using package managers like pip to set up your Python environment efficiently. 

Data types and variables: Python supports various data types such as numbers, strings, lists, and dictionaries, providing flexibility for diverse programming needs. 

Operators and expressions: Python offers a range of operators, including arithmetic, comparison, and logical operators, allowing concise expression of complex operations. 

Conditional statements: Employ conditional statements like if, elif, and else to execute specific code blocks based on different conditions in your Python programs. 

Looping constructs: Utilize looping constructs, such as for and while loops, to iterate through data structures or execute a set of instructions repeatedly. 

Functions: Define functions to encapsulate reusable code, pass arguments, and return values, promoting code modularity and readability in Python. 

Basic data structures: Python's fundamental data structures, including lists, tuples, and dictionaries, empower efficient storage and manipulation of data in various formats. 

Data manipulation: Master data manipulation techniques like indexing, slicing, and iterating to extract and transform data effectively in Python. 

Working with files: Learn file handling in Python for tasks like reading, writing, and processing data from external files. 

Introduction to modules and libraries: Leverage powerful Python libraries like NumPy for numerical computing and Pandas for data manipulation and analysis to enhance your coding capabilities. 

Resources: 

1. Interactive Python Tutorial: https://www.learnpython.org/ 
2. Official Python Documentation: https://docs.python.org/ 
3. Crash Course Python for Data Science: https://www.dataquest.io/course/introduction-to-python/ 
4. Python for Beginners Tutorial: https://www.w3schools.com/python/ 
5. Python Programming Projects: https://codedamn.com/news/python/100-python-projects-for-practice 

What is Power BI (Business Intelligence): 

Imagine a toolbox that helps you turn a jumble of raw data, from spreadsheets to cloud databases, into clear, visually stunning insights. That's Microsoft Power BI in a nutshell! It's a suite of software and services that lets you connect to various data sources, clean and organize the information, and then bring it to life with interactive charts, graphs, and maps. Think of it as a powerful storyteller for your data, helping you uncover hidden trends, track progress towards goals, and make informed decisions.  

Useful Links for Self-Study: 

Power Query Editor:   

https://learn.microsoft.com/en-us/power-query/power-query-ui 

https://learn.microsoft.com/en-us/power-query/ 

Power BI Desktop:

https://learn.microsoft.com/en-us/power-bi/fundamentals/desktop-what-is-desktop 

https://learn.microsoft.com/en-us/power-bi/fundamentals/ 

https://www.youtube.com/watch?v=-_DJPRrFQXI 

Data Pre-Processing:

https://www.youtube.com/watch?v=zv6RWIP9rpg 

https://www.youtube.com/watch?v=9MMj7NsBM_U 

Data Visualization: 

https://www.youtube.com/watch?v=_1w9w7tjSys 

DAX: 

https://www.youtube.com/watch?v=Ar6hSITP-w4 

Formatting:

https://www.youtube.com/watch?v=giWb_rpTGT0&t=171s 

Project Preparation:

https://www.youtube.com/watch?v=9tF1IrfLflg 

Saving the Project: 

https://www.youtube.com/watch?v=6IfrAyTBzYk

JAVASCRIPT: 

• JavaScript is the world's most popular programming language. 
• JavaScript is the programming language of the Web. 
• JavaScript is easy to learn. 
• This tutorial will teach you JavaScript from basic to advanced. 
• JavaScript (JS) is a cross-platform, object-oriented programming language used by developers to make web pages interactive.  
• It allows developers to create dynamically updating content, use animations, pop-up menus, clickable buttons, control multimedia, etc.  
• The use of JavaScript can be both on the client side and server-side. While HTML and CSS languages are used to give structure and style to web pages, JavaScript is used to add interactive elements that engage users.  
• Without JavaScript, 90% of Internet webpages would be static.   

Introduction to EDA: Exploratory Data Analysis (EDA) involves systematically analyzing and visualizing data to discover patterns, anomalies, and insights, playing a crucial role in understanding the underlying structure of the data. 

Importing and loading data: Data can be imported into Python using various formats such as CSV, Excel, or SQL, providing a foundation for EDA and subsequent analysis. 

Data cleaning and preprocessing: Cleaning and preprocessing steps, including handling missing values, outliers, and inconsistencies, are essential for ensuring the accuracy and reliability of the data. 

Descriptive statistics: Descriptive statistics, encompassing measures of central tendency and dispersion, offer a summary of the main characteristics of the dataset. 

Data visualization: Visualizations like histograms, boxplots, and scatter plots provide a powerful means to explore data distributions, relationships, and outliers, enhancing the interpretability of the dataset. 

Identifying patterns and relationships: EDA enables the identification of patterns and relationships within the data, helping to uncover hidden insights and guide subsequent analysis. 

Univariate and bivariate analysis: Univariate analysis focuses on individual variables, while bivariate analysis explores relationships between pairs of variables, offering a comprehensive understanding of the dataset's structure. 

Feature engineering: Feature engineering involves creating new features from existing data, enhancing the dataset with additional information to improve the performance of machine learning models. 

Hypothesis generation: EDA findings often lead to hypothesis generation, fostering a deeper understanding of the data and guiding further research questions or analytical approaches. 

Resources: 

1. DataCamp's Exploratory Data Analysis with Python: https://www.datacamp.com/courses/introduction-to-data-science-in-python 
2. Pandas Tutorial for Data Analysis: https://pandas.pydata.org/docs/getting_started/intro_tutorials/ 
3. Seaborn Data Visualization Tutorial: https://seaborn.pydata.org/tutorial.html 
4. Hands-On Exploratory Data Analysis with Python: https://www.oreilly.com/library/view/python-for-data/9781449323592/ 
5. Exploratory Data Analysis for Real-World Projects: https://www.analyticsvidhya.com/blog/2016/01/complete-tutorial-learn-data-science-python-scratch-2 

• Django is a high-level Python web framework that follows the model-view-controller (MVC) architectural pattern. It provides a robust set of tools and features for building web applications quickly and efficiently. Django follows the principle of "Don't Repeat Yourself" (DRY) and promotes the use of reusable components, making it highly scalable and maintainable. 

• One of the key features of Django is its object-relational mapping (ORM) layer, which allows developers to interact with databases using Python objects instead of writing SQL queries. This simplifies database operations and reduces the amount of code needed. Django also includes a built-in administration interface that automatically generates forms and views for managing database records. 

• Django follows a URL routing system, where URLs are mapped to specific views or functions. It provides a powerful template engine that allows developers to separate the presentation logic from the business logic, making it easier to design and maintain the user interface. Django also supports internationalization and localization, making it suitable for building multilingual websites. 

• Django is known for its emphasis on security. It includes built-in protection against common web vulnerabilities, such as cross-site scripting (XSS) and cross-site request forgery (CSRF). It also provides features like user authentication and authorization, password hashing, and secure session management. 

• Django has a vibrant and active community, with a vast ecosystem of third-party packages and extensions. It follows the open-source philosophy and is continuously updated and improved by a dedicated team of developers. Django is widely used by organizations of all sizes, from small startups to large enterprises, to build a wide range of web applications, including content management systems, e-commerce platforms, and social networks. 

• Django Official Documentation: The official documentation is the best place to start learning Django. It provides a comprehensive guide, tutorials, and examples. You can find it at: https://docs.djangoproject.com/ 

• Django for Beginners: It covers the basics of Django, including setting up a project, creating models, views, and templates, handling forms, and deploying a Django app. You can find it at https://www.w3schools.com/django/ 

• Django Girls Tutorial: It includes step-by-step instructions and examples. You can find it at: https://tutorial.djangogirls.org/ 

• Django Crash Course: It covers the fundamentals of Django, including models, views, templates, forms, and authentication. It also includes exercises and projects to practice your skills. You can find it at: https://www.freecodecamp.org/news/python-django-course/ 

• DjangoYouTube Channels: Dennis Ivy's Django series (https://www.youtube.com/playlist?list=PL-51WBLyFTg2vW-6XBoUpE7vpmoR3ztO). 

Principles of data visualization: Effective data visualizations prioritize clarity, ensuring that the intended message is easily understandable, and accuracy, representing data truthfully and without distortion.

Choosing the right chart: Select appropriate chart types, such as bar charts, pie charts, line charts, or maps, based on the nature of your data and the insights you aim to convey. 

Matplotlib and Seaborn libraries: Matplotlib and Seaborn are powerful Python libraries for creating both simple and advanced visualizations, providing flexibility and customization options. 

Customizing visuals: Customize visual elements, including colors, labels, axes, and titles, to enhance the overall aesthetics and effectiveness of your data visualizations. 

Interactive visualizations: Utilize libraries like Plotly and Bokeh to create interactive visualizations, allowing users to engage with and explore data dynamically. 

Data storytelling: Data storytelling involves using visuals as a narrative tool to communicate insights effectively, making data more accessible and compelling for a broader audience. 

Best practices for presenting visualizations: When presenting data visualizations, adhere to best practices such as providing context, focusing on key insights, and ensuring clarity to effectively convey the intended message. 

Resources: 

1. Matplotlib Tutorial: https://matplotlib.org/ 
2. Seaborn Tutorial: https://seaborn.pydata.org/tutorial.html 
3. Plotly Python Tutorial: https://plotly.com/python/ 
4. Bokeh Python Library: https://docs.bokeh.org/en/latest/ 
5. Storytelling with Data: https://www.storytellingwithdata.com/ 
6. Effective Data Visualization with Python: https://www.datacamp.com/tracks/data-visualization-with-python 

• CSS, or Cascading Style Sheets, is a stylesheet language used to describe the presentation of a document written in HTML or XML. CSS defines the visual style and layout of elements on a web page, allowing you to control things like fonts, colors, spacing, and positioning 
• CSS is the language we use to style an HTML document. 
• CSS describes how HTML elements should be displayed. 
• This tutorial will teach you CSS from basic to advanced. 

FAQs

No LOR will be provided, and we are not promising any job opportunities.

No, the internship is completely free of cost

No, the stipend will not be provided

Students will be engaged twice a week and session timings cannot be changed.

No, you cannot submit the project at later stages