data analytics with python nptel
Mastering Data Analytics with Python: An NPTEL Course
data analytics with python nptel
The “Data Analytics with Python” course offered by NPTEL (National Programme on Technology Enhanced Learning) is designed to provide learners with a comprehensive understanding of data analysis techniques using Python. It encompasses various fundamental concepts such as data manipulation, data visualization, and statistical analysis, leveraging libraries like Pandas, NumPy, Matplotlib, and Scikit-learn. The course typically covers the entire data analysis process, including data collection, cleaning, exploration, and interpretation of results, allowing students to apply analytical methods to real-world datasets. With a blend of theoretical knowledge and practical hands-on exercises, participants develop the skills necessary to analyze data effectively and derive actionable insights, preparing them for careers in data science and analytics.
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1 - Introduction to Data Analytics: Get an understanding of what data analytics is, its importance in today’s data driven world, and how it is used across various industries.
2) Python for Data Analytics: An overview of why Python is a preferred language for data analytics, including its libraries and community support.
3) Setting up the Environment: Guidance on how to set up a Python development environment using tools such as Anaconda or Jupyter Notebook.
4) Data Structures in Python: Learn about core Python data structures (lists, tuples, dictionaries, and sets) that are fundamental for data manipulation.
5) Data Manipulation with Pandas: Introduction to the Pandas library for handling and analyzing data in Python, covering data frames and series.
6) Data Visualization: Understand the importance of visualizing data. Learn to use libraries like Matplotlib and Seaborn for creating informative graphs and charts.
7) NumPy for Numerical Data: Explore NumPy, a library for numerical computations, particularly its array structure and operations used in data analysis.
8) Data Cleaning Techniques: Learn methods to preprocess and clean datasets, which include handling missing values, data transformation, and filtering.
9) Exploratory Data Analysis (EDA): Conduct EDA to summarize the main characteristics of datasets, and learn visualization techniques to uncover patterns and anomalies.
10) Statistical Foundations: An introduction to basic statistical concepts that are vital in data analytics, such as mean, median, mode, and variance.
11) Introduction to Machine Learning: Get familiar with the basic concepts of machine learning and how they apply to data analytics projects.
12) Working with APIs: Learn how to collect data from external sources via APIs, including RESTful APIs for fetching real time data.
13) Case Studies and Applications: Analyze real world case studies that showcase the application of data analytics in various sectors like finance, healthcare, and retail.
14) Project Work: Engage in hands on projects where students apply their learned skills to analyze datasets, create visualizations, and derive insights.
15) Ethics in Data Analytics: Discuss the ethical considerations in data handling and analytics, including data privacy and the responsible use of data.
16) Career Prospects in Data Analytics: Exploration of career pathways, essential skills for jobs in data analytics, and guidance on building a resume and preparing for interviews.
17) Resources for Continuous Learning: Provide students with additional resources, including books, online courses, and communities for further learning in data analytics.
This structured training program will not only equip students with theoretical knowledge but also provide practical skills for real world data analytics scenarios using Python.
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