Topics to be covered in data science – A guide for self-learning

 Topics to be covered in data science :

 

A guide for self learning

Topics to learn in Python for data science

Part 1:

Basic arithmetic operations
Control and conditional structures
Looping – For, while loops
User input
Strings
Integers
Floating values
Typecasting

Part 2: In built Data Structures

Python Strings and Inbuilt functions
Python 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:

Classes
Object
Method
Inheritance
Polymorphism
Data Abstraction
Encapsulation

Part 4- Libraries:

Numpy
Pandas
Matplotlib
Seaborn
Scipy
Sklearn
Focus on performing EDA

Part 5 Framework for web development

Flask
Django

Topics to learn in Maths for data science:

Part 1 Statistics and Probability

Basic Stats

Introduction to basic terms
Variables
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 plot
Chebyshev’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

Vectors
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

Chain Rule of Differentiation
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 Regression
Logistic 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 Classifier
KNN
Decision Tree( Categorical and continuos variable)
Random Forest
Support Vector Machine
Logistic Regression

• Unsupervised learning
Clustering Algorithms

K Means
Fuzzy 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 Learning
R 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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