Machine learning and statistics are part of data science. Machine Learning algorithm is trained using a training data set to create a model. When new input data is introduced to the ML algorithm, it makes a prediction on the basis of the model. Basically three types of Machine Learning, Supervised learning where algorithm is fine-tuned using training dataset. In the unsupervised learning, the data instances of a training dataset do not have an expected output associated with them. Example is clustering where similar data instances are grouped to identify clusters. Reinforcement learning is where if the machine chooses a correct option it gains the reward point and vice-versa.

Supervised-Continuous:- Regression, Decision Tree, Random Forest

Unsupervised-Continuous:-Clustering i.e. SVD, PCA, K-means

Supervised- Categorical:-Classification like KNN, Trees, Logistic Regression, Naive-Bayes, SVM

Unsupervised-Categorical:-Association like Apriori, FP Growth, and Hidden Markov rule

 

In Comparison data science covers the whole spectrum of data processing, not just the algorithmic or statistical aspects. In particular, data science also covers

 

Data integration

Data visualization

Dashboards and BI

Data engineering

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