Data visualization provides a powerful way to communicate data-driven findings, motivate analyses, and detect flaws.How to communicate data-driven findingsLearn Matplotlib, Seaborn, Ggplot
Learn about foundation in data science, using a structured methodology. including working on exploratory data analysis, data wrangling and preprocessingThe learners will be able to build Supervised and Unsupervised Learning models using PythonParticipants are required to work on minimum one project
This course is intended to expose learners to the latest tools used in Data Science, Exploratory Data Analysis, building models using Machine Learning Algorithms using Python, and effectively building predictive models for helping business meet its objectives.Understand the importance of Data Science, Machine Learning and its applicationsUnderstand data visualization for exploratory analysis and effective communication to a diverse audienceUnderstand and build supervised and unsupervised algorithms based on the problem statements and the data (labelled and unlabeled data)Evaluate the performance of the modelsBuild analytics solutions and assess their effectiveness;To apply the relevant concepts of Data Science to a given context/business scenarioTo analyze a problem/ business data and conduct EDA, build models to predict the patterns from the dataTo building predictive models using Machine Learning Algorithms and other tools and techniques
Data Science, Machine Learning and its applicationsData VisualizationSupervised and unsupervised algorithmsAnalytics solutions and assessment of their effectiveness