AICTE IDEA Lab-GGSIPU
AICTE IDEA Lab-GGSIPU
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    • Home
    • Workshop Calander
    • Coordinating Team
    • Summer Internship
      • Summer Internship 2024
      • Internship Projects
      • Surveillance Vehicle
      • Drone for Air Pollution
      • LPG DETECTION SYSTEM
      • Smart Dust Collector
      • AutoCart
      • Smart Irrigation System
      • Line Follower Robot
      • Smart Helmet
      • Saline Level
      • MINE SAFETY
      • Patient Health Monitoring
      • Mental Health Management
    • Call for Project
    • Syllabus
    • Past Workshop/Seminar
    • Workshop on IoT
    • Curriculum Development
    • Thinking and Innovation
    • 3D Printing & Prototyping
    • Hands-on Training on IoT
    • IoT based Applications
    • 3D Printing & Prototyping
    • Skill Programme on Python
    • Drone Design Workshop
    • Embedded System & IoT
    • Internship Project
    • IDEA Club
  • Home
  • Workshop Calander
  • Coordinating Team
  • Summer Internship
    • Summer Internship 2024
    • Internship Projects
    • Surveillance Vehicle
    • Drone for Air Pollution
    • LPG DETECTION SYSTEM
    • Smart Dust Collector
    • AutoCart
    • Smart Irrigation System
    • Line Follower Robot
    • Smart Helmet
    • Saline Level
    • MINE SAFETY
    • Patient Health Monitoring
    • Mental Health Management
  • Call for Project
  • Syllabus
  • Past Workshop/Seminar
  • Workshop on IoT
  • Curriculum Development
  • Thinking and Innovation
  • 3D Printing & Prototyping
  • Hands-on Training on IoT
  • IoT based Applications
  • 3D Printing & Prototyping
  • Skill Programme on Python
  • Drone Design Workshop
  • Embedded System & IoT
  • Internship Project
  • IDEA Club

Skill Development Programme on Python Programming for Data Science

Workshop Organized for Saturday and Sunday

Objective of Workshop

 Join us for an exciting online Skill Development Programme focused on Python Programming for Data Science! In this workshop, our aim is to equip participants with the essential Python skills crucial for success in the realm of Data Science. Through a series of interactive sessions . you'll delve into Python basics, explore powerful Data Science libraries like Pandas and NumPy, learn to create impactful visualizations using Matplotlib and Seaborn, and apply your skills to real-world projects. Whether you're just starting your journey in Data Science or looking to enhance your expertise, this workshop offers a unique opportunity to master Python for data analysis. Engage with industry experts and fellow learners in a collaborative online environment, expanding your knowledge and skill set. Don't miss this chance to unlock the potential of Python for Data Science and elevate your career! Register now to secure your spot as seats are limited.

 The essence of the workshop, highlighting its focus on Python programming, Data Science libraries, visualization, hands-on projects, and collaborative learning.

Workshop Schedule

Python Programming for Data Science

Python programming for data science is the utilization of the Python programming language tailored specifically for tasks integral to data analysis, manipulation, visualization, and interpretation. Python has risen to prominence as one of the most favored languages in the realm of data science owing to its inherent simplicity, adaptability, and a plethora of libraries meticulously crafted for the management and analysis of data.


In the sphere of data science, Python is instrumental in a multitude of tasks, encompassing:


Data Cleaning and Preprocessing: Python's acclaimed libraries such as Pandas and NumPy are instrumental in the meticulous cleaning, organization, and preprocessing of raw data, laying a solid foundation for further analysis.


Data Analysis: Data scientists harness Python's capabilities to conduct intricate statistical analyses, hypothesis testing, and exploratory data analysis (EDA), thereby extracting valuable insights and patterns from datasets.


Data Visualization: Libraries including Matplotlib, Seaborn, and Plotly offer a canvas for crafting visually compelling plots and charts, enabling the representation of intricate data patterns and trends in an informative manner.


Machine Learning: Python's prowess shines in the domain of machine learning, boasting potent libraries such as Scikit-Learn, TensorFlow, and PyTorch. These frameworks facilitate the implementation of a myriad of machine learning algorithms, spanning from classification and regression to clustering and neural networks.


Python's inherent readability and user-friendliness render it an optimal choice for novices venturing into data science. Simultaneously, its robust libraries and frameworks position it as the preferred tool for seasoned professionals embarking on intricate data projects. Be it unraveling customer behavior, forecasting stock prices, or discerning trends within healthcare data, Python programming for data science empowers analysts with the requisite tools and capabilities to glean invaluable insights from extensive datasets.

Skilled Covered

Python programming concepts/ Basic of python

Python is a robust and easily understandable programming language. The programming language prioritizes clear and organized code structure through the use of indentation. It also provides support for a wide range of data types, including integers, floats, texts, and Booleans. Control flow encompasses conditional expressions (if-else) and iterative structures (for and while loops). Functions are declared with the keyword "def," and the programming language provides data structures such as lists, tuples, and dictionaries. Python is a computer language that enables object-oriented programming and has a diverse library ecosystem that can be accessed using the "import" keyword. Python's file management capabilities are uncomplicated, which makes it a great programming language for both novices and seasoned professionals.

NumPy

Python's NumPy library is a strong tool for doing numerical computations. It has multidimensional arrays that are capable of performing mathematical operations in an effective manner, as well as powerful indexing and broadcasting. Scientific computing, data analysis, and machine learning are all areas in which it is widely utilized, and it integrates without any problems with other libraries such as SciPy and scikit-learn. For operations that are particularly important, NumPy's performance is improved via extensions written in C and Fortran.

Pandas

The Pandas library is a Python package that allows for the manipulation and analysis of data. In addition to aiding processes such as cleaning, processing, and aggregating structured data, it is centered on the DataFrame system. In addition to being used for activities ranging from data cleansing to sophisticated analytics, Pandas is frequently utilized because of its excellent indexing capabilities, its ability to handle missing data, and its specific tools for time series data. Additionally, it supports a wide range of file formats for data input and output, and it combines with other libraries without any inconvenience.

Matplotlib

Matplotlib is a Python library that offers a large variety of static and dynamic plot formats. It is a useful toolkit for data visualization. Because it offers a versatile interface for the creation of visualizations, it is an essential instrument for academics, scientists, and data analysts.
 

Seaborn

Seaborn is a data visualization library written in Python that is built on top of Matplotlib. In addition to having a high-level interface, it specializes in the creation of visually appealing statistics visuals. It offers built-in themes, color palettes, and functions for statistical estimate, and it is integrated with Pandas, which makes it easier to perform visualization jobs. When it comes to analyzing and showing relationships in categorical data, Seaborn is an especially helpful tool. 

Data visualization

 Data visualization is the process of utilizing charts, graphs, and maps to create a graphical representation of data in order to simplify difficult information and encourage discoveries. Matplotlib and Seaborn are two of the most well-known Python libraries for visualization, and products such as Tableau and Power BI provide interactive dashboards. Effective visualizations are beneficial in a variety of disciplines, including business, healthcare, and research, since they facilitate communication, decision-making, and the study of patterns.

Data Cleaning and processing

Data cleaning encompasses the tasks of managing missing values, eliminating duplicates, and dealing with outliers. Data processing encompasses several tasks such as standardizing data, converting categorical data into numerical form, optimizing features, and managing date, time, and textual information. Validation guarantees the integrity of data and the effectiveness of the model by doing thorough checks and cross-validation. Tasks of this nature are frequently accomplished using popular tools such as Pandas, Scikit-learn, and NumPy. 

Data analysis

  
Data analysis entails the examination and condensation of data using descriptive statistics and visualization techniques. The analysis methods encompass hypothesis testing, regression analysis, machine learning, clustering, and time series analysis. The objective of the process is to extract significant insights and facilitate decision-making, frequently employing technologies such as Python libraries (Pandas, Matplotlib, Scikit-learn) and statistical software. The process is iterative, involving the refinement of models and hypotheses based on acquired insights. 

Machine Learning

Machine learning is a subfield of artificial intelligence that focuses on the development of algorithms that enable computers to learn and improve based on data without being explicitly programmed to do so. Additionally, it employs methods such as deep learning with neural networks, as well as supervised learning (data that has been labeled), unsupervised learning (data that has not been labeled), and reinforcement learning (learning that is based on rewards). Image identification, healthcare, agriculture and finance are just few of the many sectors that can benefit from its applications.

About IDEA Lab

 Embark on a journey of innovation and creativity as AICTE paves the way for a revolution in education with its IDEA (Idea Development, Evaluation & Application) Labs sprouting across the nation. These labs stand as beacons, beckoning students to immerse themselves in the dynamic realms of science, technology, engineering, and mathematics (STEM). AICTE's visionary initiative aims not just at imparting knowledge but fostering a culture of hands-on experience, learning by doing, and the exciting prospect of product visualization. As these IDEA Labs take root, they become more than just physical spaces—they become crucibles of ingenuity, shaping the future by empowering students to apply STEM fundamentals in innovative and tangible ways. Welcome to a world where ideas flourish, and the pursuit of knowledge transcends traditional boundaries. The AICTE-IDEA Labs are not merely laboratories; they are the catalysts propelling students into a realm where curiosity converges with creation, ushering in a new era of limitless possibilities. Read more 

Patron

Chief Mentor, IDEA Lab

Dean, USIC&T

Prof. (Dr.) Padma Shri 

Mahesh Verma

Hon’ble Vice Chancellor, GGSIP University 

Dean, USIC&T

Chief Mentor, IDEA Lab

Dean, USIC&T

 Prof. Anjana Gosain 

Dean, USIC&T, GGSIP University 

Chief Mentor, IDEA Lab

Chief Mentor, IDEA Lab

Chief Mentor, IDEA Lab

 Prof. Amit Prakash Singh

Professor, USIC&T, GGSIP University

Coordinator

Coordinator

Coordinator

  Dr. Ruchi Sehrawat 

USIC&T, GGSIP University 

Coordinator

Coordinator

Coordinator

 Dr. Manoj Kumar Satyarthi

USIC&T, GGSIP University

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