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Overview of R, R data types and object
Reading and writing data
control structures, functions, scoping rules
Use of Python in Data Science
Where to use python in real time
Python Environment setup, operators
Simple if Statement, if-else Statement
Introduction To while Loops.
Introduction To for Loops, Using continue and break
List, Tuples, Dictionaries
Python Lists, Tuples, Dictionaries
Built-in Functions & Methods
Anonymous Functions – Lambda
Visualizing Data: Box, Scatter, Bar & Histogram
What is Machine Learning?
What is the Challenge?
Introduction to Supervised Learning,Unsupervised Learning.
Introduction to Linear Regression
Linear Regression with Multiple Variables
Disadvantage of Linear Models
Interpretation of Model Outputs
Understanding assumptions of linear regression
Describe or sumarise a set of data
The mean,median,mode, Kurtosis and skewness
Computing Standard deviation and Variance
Covariance, Correlation and Causation
Introduction to Logistic Regression.– Why Logistic Regression
Introduce the notion of classification.
Cost function for logistic regression
Confusion Matrix, Odd’s Ratio And ROC Curve
Advantages And Disadvantages of Logistic Regression.
How to build decision tree?
Understanding Kart Model
Classification Rules- Overfitting Problem
Stopping Criteria And Pruning
Model A decision Tree, Bayes
k-Means algorithm for clustering – groupings of unlabeled data points.
Principal Component Analysis(PCA)
Non-probabilistic Parsing, Probability
Information Theory, Language modeling and Naive Bayes
Viterbi Algorithm for Finding Most Likely HMM Path
Lexical Semantics, Sentiment Analysis
Production system, Ontology
Propositional logic,Pattern Recognition
Distance-Based Neural Networks
Introduction to deep learning
Deep L-layer neural network
Forward Propagation in a Deep Network.
Why deep representations, Forward and Backward Propagation
Parameters vs Hyperparameters
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The "data science" has become one of the disciplines with the highest growth in enterprises and research institutions due to the versatility of solutions that can be offered in different industries. Many times without the user being aware, large amounts of data are processed to recommend a movie, predict the price of a flight or classify a purchase as fraudulent or legitimate.
However, the process of analyzing data is expensive, complicated and imperfect, since data science involves many different disciplines , in addition, there is no recipe for the "perfect model". Unlike software development, which is clear about the final result, an analytical process can yield unexpected results and sometimes a viable solution is not reached. Data science is an inaccurate science.
The field of data science employs mathematics, statistics and computer disciplines, and incorporates techniques such as machine learning, cluster analysis, data extraction and visualization.
We can define data scientists as professionals who, using large volumes of information and of different types, solve business problems and obtain answers from data.
Although there are no "typical" data science projects, since each project is a world, generally companies start with a structure similar to this one: