Data Science Training in Bangalore

Data Science Training in Bangalore

Data Science Training in Bangalore – Data science is a new interdisciplinary field of algorithms for data, systems, and processes for data, scientific methodologies for data and to extract out knowledge or insight from data in diverse forms – both structured and unstructured. This field is also known as data-driven science. In layman’s word, Data science can be considered as the study of processed data and its flow, i.e., from where this information comes to how it is represented and used and later gets converted to value resource in business or IT strategy. In this tutorial, you will study the data science and its uses and importance.

MORE ABOUT DATA SCIENCE

A huge quantity of ordered and unordered data can be identified, and their patterns can aid in rein the costs of the organization, maximizes the efficiencies, helps in identifying new opportunities in the market as well as increase the competitive advantage of any organization. In the field of data science, the data remains at heart as the main entity. The applications of data science deal with using the data in creative forms in generating business and market value.

REAL-TIME EXAMPLE OF DATA SCIENCE

A good example of a data for the product is the recommendation engine that ingests data generated by users and composes tailored implication depending on the data that has been extracted. Some common examples of products generating data are:

  • The recommendation engine of Amazon put forward items for users to buy or show associated items and products as well as decide which algorithm to use.
  • Another data product is the Gmail’s spam filterer – which runs an algorithm in the background and checks for incoming emails and decides whether that message is a spam or a genuine one.
  • Self-driving cars are another data product – which uses machine learning techniques and algorithms that can detect traffic lights, other colliding objects in traffic, cars, and pedestrian on the road, trees, and pillars, etc.

WHO ARE THE DATA SCIENTISTS

As the data quantity of modern business keeps on increasing, data scientists get hired by organizations or companies to handle and manage a bulk amount of data and convert this raw data to valuable information for corporate business. Data scientists and data analysts do this entire thing. Hence they must have a good hold over analytics, machine learning, algorithms and data management functions as well as statistical skills.

USES OF DATA SCIENCE AND ITS APPLICATIONS

  • Disaster Risk: Various applications deal with the precise weather forecast and related catastrophes such as flooding, wind damage.
  • Mental Health Care: Applications like Ginger.io takes the mobile data of users’ for forming a view on the feelings of users on various things.
  • Bail Bonds: Other companies use the algorithms and concepts of data science for making estimation and evaluations regarding the risks allied with lending capital for bail.
  • Risk Management: Almost every business bears the risk of funding and getting profit. All these forecasting’s of profit and loss along with project completion and risk factor can be estimated using the concept of data science and its algorithms.

COURSE COVERS

  • Applied Predictive Analytics
  • Statistical Modeling
  • Effective Decision Making
  • Advanced Predictive Analytics

Applied Predictive Analytics

 

Pre-processing Techniques, Graphical Visualization; Handling missing values; Data Standardization; Principal Component Analysis (PCA) Predictive Models: Naïve Bayes classifier; Decision Trees; Random Forests; Bagging; AdaBoost Model Selection Techniques: Cross Validation; Analysis metrics like Accuracy, Precision, Recall, Over fitting, Bias and Variance a thorough introduction to solving analytics problems using R.

Statistical Modeling

 

Studying the data systematically and gaining intuition about variables and their inter-relationships Probability Distribution Analysis, Chi-square testing, Hypothesis testing Maximum Likelihood Estimation (MLE), Maximum a Posteriori Estimation (MAP), ANOVA Linear regression and Multilinear regression, Logistic regression, Naïve Bayes analysis ARIMA, Time series analysis.

Effective Decision Making

 

Linear Programming and Sensitivity Analysis Quadratic Programming Survival Analysis Non-parametric statistics Statistical Process Control (SPC) Genetic Algorithms as applied to single and multi-objective optimization Monte Carlo simulations.

Advanced Predictive Analytics

 

K-Nearest Neighbours, Neural Networks, Perceptron and Single Layer Neural Network, and hand calculations, Back Propagation algorithm and a typical Feed Forward Neural Net, Support Vector Machine (SVM).