Introduction to Algorithms for Data Science

Data Science the art of extracting information in a structured or unstructured form from various domains for decision making and performing statistical and computational analysis to interpret data. Algorithm refers to a set of rules or a process to follow in calculations and other problem-solving operations.

Algorithms are the heart of data analysis. Basics in Mathematical and statistical subjects make the algorithms transparent. In the current world of big data, parsing heavy amounts of information lead to the world of innovation. Companies using Data Science have become intelligent enough to push & sell products as per customers purchasing power & interest. The most common application of data Science used in Internet search, Recommender systems, Price comparison websites, airline route planning, delivery risk logistics and in future self-driving cars.

Data Scientists and Business Analysts are experts in using an algorithm for data analytics and applying skills for data-driven business intelligence. Data Scientists write an algorithm to understand the business data for a solution, and future business performance. Business Analysts spend more time in applying existing algorithms.

In data analysis, there is a need for programming fluency and experience with real and challenging indispensable data. This paves the necessity of knowledge using Python, R, Knime, SAS, SQL, SPSS and real data analysis.  By the end of this course, you gain the ability to adapt algorithms to solve new problems and carry out innovative data analytics for decision making, applied problem-solving, data analysis and database management.

Why algorithm for data science?

Algorithms are designed and developed using Mathematical programming as they support business to manage large amounts of data,  provide decision-making tools, allows the creation of visuals to aid in understanding. Machine learning algorithms create the opportunity in datasets through data mining and perform statistical data analysis.

Who needs this training?

This tutorial is ideal for Students those who aspire to begin their career as Data Scientist. This course is perfect for the Business Analyst, Mathematicians, Statisticians, Software professionals who want to learn about the application of algorithms in data science.

Why Should I know Algorithm for Data Science?

Data Scientists need to know data structures and different algorithms:

• To work with raw and extensive data to do data crunching much faster and get the results.
• To solve a complex programming task such as crafting a new modeling technique from scratch or developing a real-time system to deploy a model.
• To manipulate, evaluate, or sort data, the quality, and efficiency of the algorithms used to determine the soundness and the time requirements of the analysis.
• Algorithms are like road maps for accomplishing a given, well-defined task.

 What will I learn in this training?

•  Learn to use machine learning algorithms such as regression, Bayesian, regularization, decision tree, instance based, clustering, neural networks, and others.

• Manage and organize large sets of multivariate data using methods like linear discriminant analysis and multilinear regression selection. 
• Create design and structure databases using tools like Teradata, Oracle, and Hadoop.
• Understand and build the foundation of analysis for problem-solving
• Develop skills to perform all the steps in a complex data science project
• Good understanding of an extensive range of algorithms, helps you to choose the right one for a problem and apply it correctly.

 Prerequisites for this course:

• Basic knowledge and experience in Statistics, Matrices, Probability
• Exposure to Vectors   
• Basic programming knowledge using Python and R

 What is covered in Algorithm for Data Science training?

 Lesson 1: Introduction to Data Science

Data Science is the practice of exploring, obtaining, modeling and interpretation of data. It explores topics such as statistics, mathematics, visualization, machine learning, data analysis, and programming.  

Class 1:

• Demand for Data Science
• About Sourcing Data
• Introduction to Machine Learning

 Class 2:

• Different techniques and algorithms used for Analysis
• Data Science in Maths and Statistics

 Lesson 2: Mathematics and Statistics

Data Analytics requires the use of various quantitative tools, algebra, calculus, statistics, econometrics, with different software and programming languages.

 Class 1:

• Introduction to Mathematics, Linear algebra, and calculus
• Using Exponentials, compounding, and Logarithms

 Class 2:

• Overview of Vector Algebra
• About Statistical Regression
• Using Matrix calculus and equations

 Lesson 3: Machine Learning Algorithms

Machine learning (ML) is a mandatory skill for data scientists, data analysts, a business analyst as it helps to refine raw data into useful predictions. This section helps you identify and extract useful features to represent your data and to evaluate the performance of your machine learning algorithms. Basics of statistics and probability theory are utilized in programming through which machine learning problems find their solution. Each type of AL

Class 1:

• Introduction to different types of Machine Learning
• Algorithms under Supervised ML
• Algorithms under Unsupervised ML

 Lesson 4: Regression

 A regression method is useful for modeling the relationship between the variables which are iteratively refined using a measure of error predictions made by the model.  There are innumerable ways regressions. The most widely used regression algorithms are the Linear Regression and Logistic regression. You can apply these regression algorithms considering the conditions of data. 

 Class 1:

• Linear Regression
• Logistic Regression
• Ordinary Least Squares Regression (OLSR)

Class 2:

• Stepwise Regression
• Multivariate Adaptive Regression Splines (MARS)
• Locally Estimated Scatterplot Smoothing (LOESS)

 Lesson 5: Regularization Algorithms

Regularization algorithms are powerful and are simple modifications performed to other methods.  They provide an extension to another method thereby making the model for simple for use. The list of most popular regularization algorithms serves as the agenda of this lesson.

Class 1: 

• Ridge Regression
• Least Absolute Shrinkage and Selection Operator (LASSO)
• Elastic Net
• Least-Angle Regression (LARS)

Lesson 6: Bayesian Algorithms

Bayesian methods involve the process of making optimal decisions for the problems to minimize our actions and reduce loss.  Bayesian methods are those that apply Bayes’ Theorem to problems such as classification and regression.

Class 1:

• Naive Bayes
• Gaussian Naive Bayes
• Multinomial Naive Bayes
• Averaged One-Dependence Estimators (AODE)
• Bayesian Belief Network (BBN)
• Bayesian Network (BN)

Lesson 7: Instance-Based Algorithms

Instance-based algorithms are the model that provide a decision problem with instances or examples of training data that are supposed to be important or required to the model. Predictions are derived using a database consisting of sample data and comparing them with new data using similarity measure.

Class 1:

• k-Nearest Neighbor (kNN)
• Learning Vector Quantization (LVQ)
• Self-Organizing Map (SOM)
• Locally Weighted Learning (LWL)

Lesson 8: Decision Tree Algorithms

Decision tree algorithms help you to construct a model of decisions that are made based on actual values of attributes in the data. Decision trees are designed for data to perform classification and regression problems. They are fast and accurate and a big favorite in machine learning. This section teaches you the most popular decision tree algorithms.

Class 1:

• Classification and Regression Tree (CART)
• C4.5 and C5.0  
• Chi-squared Automatic Interaction Detection (CHAID)

Class 2:

• M5
• Decision Stump
• Conditional Decision Trees
• Iterative Dichotomiser 3 (ID3)

Lesson 9: Dimensionality Reduction Algorithms

The DR (Dimensionality Reduction) algorithm reduces the number of variables randomly to obtain a set of key variables. These algorithms are useful to visualize dimensional data or simplify data which can then be utilized in a supervised learning method. This chapter covers the different types of algorithm methods using DR algorithms.

Class 1:

• Principal Component Analysis (PCA)
• Principal Component Regression (PCR)
• Partial Least Squares Regression (PLSR)

Class 2:

• Sammon Mapping
• Multidimensional Scaling (MDS)
• Projection Pursuit

Class 3:

• Linear Discriminant Analysis (LDA)
• Mixture Discriminant Analysis (MDA)
• Quadratic Discriminant Analysis (QDA)
• Flexible Discriminant Analysis (FDA)

 Lesson 10: Clustering Algorithms

Clustering is the problem of grouping the individuals in a population together by their similarity of attributes. Clustering defines the class of problem and the class of methods in a similar way like regression.

Class 1:

• k-Means
• k-Medians
• Expectation Maximisation (EM)
• Hierarchical Clustering

Lesson 11: Deep Learning Algorithms

Deep Learning Algorithms serve as an update to Artificial Neural Networks and are concerned with building larger and more complex neural networks.

Class 1:

• Deep Boltzmann Machine (DBM)
• Deep Belief Networks (DBN)
• Convolutional Neural Network (CNN)
• Stacked Auto-Encoders

Lesson 12: Neural Networks Algorithms

Neural Networks algorithms are models that are motivated by the structure and function of biological neural networks. They are a class of pattern matching commonly used for regression and classification problems, but they comprise of hundreds of algorithms and variations for all manner of problem types. These algorithms provide best solutions to many problems in image and speech recognition and natural language processing.

Class 1:

• Perceptron
• Back-Propagation
• Hopfield Network
• Radial Basis Function Network (RBFN)

Lesson 13: Ensemble Algorithms

Ensemble algorithms include models which are composed of multiple weaker models that are independently trained and whose predictions are added in some way to make the overall prediction.

Class 1:

• Boosting
• Bootstrapped Aggregation (Bagging)
• AdaBoost
• Stacked Generalization (blending)
• Gradient Boosting Machines (GBM)
• Gradient Boosted Regression Trees (GBRT)
• Random Forest

Lesson 14: Associated Rule Algorithms

Association rule algorithm methods extract rules which best explain observed relationships between variables in data. These rules can discover significant and commercially useful associations in the major multidimensional datasets that can be deployed by an organization.

Class 1:

• Apriori algorithm
• Eclat algorithm

Lesson 15: Other Algorithms

This section provides an overview of the other Algorithms used in data science to process and understand data.

Class 1:

• Support Vector Machines (SVM)           
• Evolutionary Algorithms
• Inductive Logic Programming (ILP)
• Reinforcement Learning

Class 2:

• ANOVA
• Page Rank
• Information fuzzy network (IFN)          
• Conditional Random Fields (CRF)