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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