English | Size: 20.09 GB
Genre: eLearning
What you’ll learn
End-to-end knowledge of Data Science
Prepare for a career path as Data Scientist / Consultant
Overview of Python programming and its application in Data Science
Detailed level programming in Python – Loops, Tuples, Dictionary, List, Functions & Modules, etc.
Decision-making and Regular Expressions
Introduction to Data Science Libraries
Components of Python Ecosystem
Analysing Data using Numpy and Pandas
Data Visualisation with Matplotlib
Three-Dimensional Plotting with Matplotlib
Data Visualisation with Seaborn
Introduction to Statistical Analysis – Math and Statistics
Terminologies & Categories of Statistics, Correlation, Mean, Median, Mode, Quartile
Data Science Methodology – From Problem to Approach, From Requirements to Collection, From Understanding to Preparation
Data Science Methodology – From Modeling to Evaluation, From Deployment to Feedback
Introduction to Machine Learning
Types of Machine Learning – Supervised, Unsupervised, Reinforcement
Regression Analysis – Linear Regression, Multiple Linear Regression, Polynomial Regression
Implementing Linear Regression, Multiple Linear Regression, Polynomial Regression
Classification, Classification algorithms, Logistic Regression
Decision Tree, Implementing Decision Tree, Support Vector Machine (SVM), Implementing SVM
Clustering, Clustering Algorithms, K-Means Clustering, Hierarchical Clustering
Agglomerative & Divisive Hierarchical clustering
Implementation of Agglomerative Hierarchical Clustering
Association Rule Learning
Apriori algorithm – working and implementation
Who this course is for:
Data Scientists
Data Analysts / Data Consultants
Senior Data Scientists / Data Analytics Consultants
Newbies and beginners aspiring for a career in Data Science
Data Engineers
Machine Learning Engineers
Software Engineers and Programmers
Python Developers
Data Science Managers
Machine Learning / Data Science SMEs
Digital Data Analysts
Anyone interested in Data Science, Data Analytics, Data Engineering
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