# Topics to be covered in data science : A guide for self learning

### Topics to learn in Python for data science

#### Part 1:

Basic arithmetic operationsControl and conditional structures

Looping – For, while loops

User input

Strings

Integers

Floating values

Typecasting

#### Part 2: In built Data Structures

Python Strings and Inbuilt functionsPython List and Boolean Variables Inbuilt functions

Python Sets-Inbuilt functions

Python Dictionaries

Python tuples

Functions in Python

Lambda Functions

Iterators and Generators

Exception Handling and import libraries

#### Part 3- OOPS:

Object

Method

Inheritance

Polymorphism

Data Abstraction

Encapsulation

#### Part 4- Libraries:

Pandas

Matplotlib

Seaborn

Scipy

Sklearn

Focus on performing EDA

#### Part 5 Framework for web development

Django

### Topics to learn in Maths for data science:

#### Part 1 Statistics and Probability

Basic Stats

Introduction to basic termsVariables

Random variables

Population,sample,Population mean, sample mean

Population distribution, Sample distribution and sampling distribution

Mean, Median, Mode

Range

Measure of Dispersion

Variance

Standard Deviation

Gaussian/Normal Distribution

#### Advance Stats

Q-Q plotChebyshev’s inequality

Discrete and continuous Distribution

Bernouli and Binomial distribution

Log normal distribution

Box cox Transform

Poisson distribution

Application of non Gaussian distribution

Z test, T test, Chi square, Anova Test

#### Part 2 Linear Algebra

Matrices

Transpose of matrix

Inverse of a matrix

Determinant of a matrix

Trace of matrix

Dot product

Eigen values

Eigen vectors

Single value Decomposition

#### Part 3 Calculus

Partial Derivatives

Integrations

Beta and gamma functions

Functions of Multiple variable, Limit, continuity, partial derivatives

Variants of Optimizers

Loss Functions

Back Propagation

Minima and Maxima

### Topics to learn in Machine learning for data science:

#### Machine Learning

• Supervised learning

a. Regression

Linear RegressionLogistic Regression

Decision Tree Regression

Random Forest Regression

Polynomial Regression

Support vector Machine

Gaussian Regression

Lasso Regression

KNN Model

Ridge Regression

Bayesian Linear Regression

#### b. Classification

Naive Bayes ClassifierKNN

Decision Tree( Categorical and continuos variable)

Random Forest

Support Vector Machine

Logistic Regression

#### • Unsupervised learning

Clustering Algorithms

K MeansFuzzy K Means

Hierarchical clustering

Agglomerative Hierarchical clustering

Divisive Hierarchical clustering

Anomaly Detection

Principle component analysis

Apriori algorithm

Singular value decomposition

Density-based spatial (DBSCAN) clustering

Gaussian Mixture Model algorithm

Mean-Shift clustering algorithm

#### • Reinforcement learning

Q LearningR Learning

TD Learning

### Topics to learn in Deep learning for data science :

Convolutional Neural Networks (CNNs)Long Short Term Memory Networks (LSTMs)

Recurrent Neural Networks (RNNs)

Generative Adversarial Networks (GANs)

Radial Basis Function Networks (RBFNs)

Multilayer Perceptrons (MLPs)

Self Organizing Maps (SOMs)

Deep Belief Networks (DBNs)

Restricted Boltzmann Machines( RBMs)

Autoencoders

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