Our Data Analytics Using Python course is designed to equip participants with the knowledge and skills needed to perform data analysis and derive insights from data using Python programming language and popular libraries like Pandas, Matplotlib, and scikit-learn. Whether you’re new to data analytics or looking to enhance your skills, this course covers essential concepts, tools, and techniques to help you effectively work with data and make informed decisions. Through a combination of lectures, hands-on exercises, and real-world projects, participants will learn how to load, clean, explore, analyze, and visualize data, as well as build and evaluate machine learning models for predictive analytics. Join us and unlock the power of data analytics with Python!
Information Technology
Data Analytics Using Python
₹6,200.00
Course Overview:
- Introduction to Data Analytics: Understanding the role of data analytics in deriving insights from data and making data-driven decisions. Exploring various domains and applications of data analytics.
- Python Basics for Data Analytics: Learning the basics of Python programming language, including data types, variables, operators, control flow, functions, and data structures like lists, dictionaries, and tuples.
- Data Manipulation with Pandas: Introduction to the Pandas library for data manipulation and analysis. Learning how to load, clean, filter, transform, and summarize data using Pandas DataFrames.
- Data Visualization with Matplotlib and Seaborn: Exploring data visualization techniques using Matplotlib and Seaborn libraries. Learning how to create plots, charts, histograms, scatter plots, and other visualizations to explore and communicate insights from data.
- Exploratory Data Analysis (EDA): Understanding the process of exploratory data analysis (EDA) to gain insights into data distributions, relationships, patterns, and anomalies. Learning how to use statistical measures and visualization tools for EDA.
- Data Preprocessing and Feature Engineering: Preprocessing raw data for analysis by handling missing values, encoding categorical variables, scaling numerical features, and performing feature engineering to create new informative features.
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