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What are the Best Data Science Courses on Udemy?

Below you’ll find the most highly rated and recommended data science courses on Udemy

The Data Science Course 2021: Complete Data Science Bootcamp

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What you’ll learn

  • The course provides the entire toolbox you need to become a data scientist
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance

Statistics for Data Science and Business Analysis

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What you’ll learn

  • Understand the fundamentals of statistics
  • How to plot different types of data
  • Make data driven decisions
  • Understand the concepts needed for data science even with Python and R!
  • Calculate the measures of central tendency, asymmetry, and variability
  • Use and understand dummy variables
  • Distinguish and work with different types of distributions

R Programming A-Z™: R For Data Science With Real Exercises!

What you’ll learn

YOUTUBE ABONE
  • Learn to program in R at a good level
  • Learn how to create a while() loop and a for() loop in R
  • Learn how to create variables
  • Understand the Normal distribution
  • Practice working with financial data in R

Machine Learning, Data Science and Deep Learning with Python

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What you’ll learn

  • Build artificial neural networks with Tensorflow and Keras
  • Make predictions using linear regression, polynomial regression, and multivariate regression
  • Implement machine learning at massive scale with Apache Spark’s MLLib
  • Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA
  • Build a movie recommender system using item-based and user-based collaborative filtering
  • Design and evaluate A/B tests using T-Tests and P-Values
  • Classify images, data, and sentiments using deep learning
  • Understand reinforcement learning – and how to build a Pac-Man bot
  • Use train/test and K-Fold cross validation to choose and tune your models

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