data analytics using python nptel
Advanced Data Analytics Techniques with Python: NPTEL Course
data analytics using python nptel
Data Analytics using Python, offered by NPTEL, is a comprehensive course designed to introduce learners to the fundamental concepts and techniques of data analysis utilizing Python, one of the most popular programming languages in the field. The course covers essential libraries such as NumPy, pandas, and Matplotlib for data manipulation, analysis, and visualization. It encompasses various aspects of data analytics, including data cleaning, exploratory data analysis, statistical inference, and the application of machine learning algorithms. Through practical examples and hands-on exercises, learners gain the skills to extract insights from large datasets, enabling informed decision-making across diverse domains. The course is aimed at equipping students and professionals with the tools necessary to leverage data effectively in their respective fields.
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1 - Introduction to Data Analytics: Understand the fundamental concepts of data analytics, its importance in the modern world, and how it aids in decision making across various domains.
2) Python Programming Fundamentals: Acquire a solid foundation in Python programming, covering variables, data types, control structures, functions, and libraries essential for data analytics.
3) Libraries for Data Analytics: Explore key Python libraries such as NumPy for numerical data, pandas for data manipulation, and Matplotlib and Seaborn for data visualization.
4) Data Cleaning Techniques: Learn how to preprocess and clean data, addressing missing values, duplicates, and inconsistencies to prepare datasets for analysis.
5) Data Visualization Principles: Gain insights into effective data visualization techniques to represent data graphically, helping in better interpretation and presentation of results.
6) Statistical Analysis Basics: Understand the basic statistics concepts—mean, median, mode, variances, and standard deviations—to evaluate data distributions and make inferences.
7) Exploratory Data Analysis (EDA): Delve into EDA techniques to uncover patterns, trends, and anomalies in data using visual and quantitative methods.
8) Predictive Analytics: Learn the principles behind predictive modeling, including regression analysis and classification techniques using Python.
9) Machine Learning Basics: Introduce the basic concepts of machine learning, including supervised and unsupervised learning, with practical applications using popular libraries like Scikit learn.
10) Working with Real World Datasets: Engage with practical assignments and projects using real world datasets, applying learned techniques to solve genuine data problems.
11) Data Ethics and Privacy: Discuss the ethical implications of data analytics, focusing on data privacy, security, and responsible usage of data.
12) Time Series Analysis: Study techniques for analyzing time series data, including trend decomposition and forecasting methods, to understand temporal trends.
13) Web Scraping for Data Collection: Learn how to scrape data from websites using Python libraries such as BeautifulSoup and Requests, enhancing your data collection skills.
14) Capstone Project: Apply all acquired skills in a comprehensive capstone project, where students solve a complex real life problem using data analytics.
15) Career Opportunities and Next Steps: Explore career paths in data analytics, including roles such as Data Analyst, Data Scientist, and Machine Learning Engineer, along with guidance on further learning and certifications.
This training program through NPTEL serves to equip students with the necessary skills, practical experience, and theoretical knowledge to excel in the field of data analytics using Python.
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