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Data Science Training Institutes in Bangalore

Data Science Training in Bangalore & Best Data Science Training Institutes in Bangalore



About Data Science Training in Bangalore

MyClass Training Bangalore is a Leading Data Science Training institute in Bangalore providing Real-Time and Placement Oriented Data Science Training Courses. Our Training Institutes are mainly focussed on introducing new methods of Learning by making it Interesting and Motivating. Our Data Science Training Centres spans across all major locations in Bangalore. We provide range of Career oriented courses for different segments like students, job seekers and corporate users. 

MyClass Training Institute has distinguished itself as the leading Data Science Training Institutes in Bangalore. Our Data Science Consultants or Trainers are highly qualified and experienced working professionals with minimum of 6 Years of hands on real time expertise to deliver high-quality Data Science Training across Bangalore. Our Data Science training course includes basic to advanced level. 

Data Science is an interdisciplinary field about processes and systems to extract knowledge or insights from large volumes of data in various forms, either structured or unstructured. Data science utilizes data preparation, statistics, predictive modeling and machine learning to investigate problems in various domains. In our Data Science Training you will learn Data Exploration, Fundamental Modeling Techniques, Modeling Techniques, Database Technologies and Map-Reduce

Our team of Expert Trainers have designed Data Science training course content and syllabus as per the current Industry Requirements. This enables our Students to be an Industry-Ready Professionals, capable of handling majority of the real-world ScenarData Science after Successful completion of the Course.  

We Provide Exclusive Course Materials, Interview Questions, Real Time Project ScenarData Science on Data Science Training which will give our students an edge over other Training Institutes. You can Experience Real-time training in our well equipped labs to excel in Data Science course. Through our associate Training Institutes we have trained more than 800+ Students in Data Science Training. We Provide day time classes, weekend training classes, evening batch classes and fast track training classes for Data Science Training. Our Data Science Training course fee is very economical and tailor made as per Student Requirement.

To Kick Start your Career, Enroll for a free Demo on Data Science Training @ MyClass Training Today!.

Data Science Training Course Content

Curriculum

 1.  Introduction to Probability and Statistics for Data Science

This module aims at preparing you for the essential skill of thinking like a statistician. This module will enable you to change your analytical thinking process, and you will begin to start looking at data and numbers from a different perspective. This is a fundamental module and strong concepts in this area will enable you to differentiate yourself as a Data Scientist. This module covers •     Probability theory and related algorithms •          Descriptive statistical methods •          Inferential statistical methods From a tools perspective, you will gain confidence with tools like R and Excel Fundamentals of Probability •         Introduction to random variables •          Probability theory •        Conditional probability •       Bayes Theorem The Concept of a data set •          Understanding the properties of an attribute: Central tendencies (Mean, Median, Mode); •     Measures of spread (Range, Variance, Standard Deviation) •        Basics of Probability Distributions; Expectation and Variance of a variable Probability distribution and differences between discrete and continuous distributions •  Discrete probability distributions: Binomial, Poisson • Continuous probability distributions: Normal distribution; t-distribution. Procedure for gaining inference about populations from samples. Understand the data attributes, distributions, sample vs population Procedure for statistical testing •       Extend the understanding to analyze relationships between variables •         How to conduct statistical hypothesis testing and introduction to various methods such as chi-square test, t-test, z-test, F-test and ANOVA •            Covariance and Correlation and a Precursor to Regression • Hands-on Implementation in R

 

  2.Essential mathematical concepts:

•Vectors, Matrices, Eigen values, Eigen vectors, Orthogonality, etc. •       Kernel tricks, kernel functions, PCA, SVD, LSA •             Hands-on implementation in R

 

  3.Essential Engineering skills for Data Science

Data preprocessing techniques • Python and R basics • Database Concepts • String and list objects • Exception handling • Understanding of data structures, functions, control structures, data manipulations, date and string manipulations • Pre-processing techniques: Binning, Filling missing values, Standardization and Normalization, Type conversions, train-test Data split, ROCR1

 

4.Data Exploration, Data Visualizations and Data Story

• Need for Visualizations • How to tell a Data Story • Communicating with data: Issues and guiding principles; Primary ingredients of data visualizaon; How to pick visual encodings such as color, shape, size; Which chart to use when; How to accommodate more than 2 dimensions • A case highlighting the transition from a simple chart to a powerful visualization, complete with storytelling • Using R-ggplots and Qliksens for visualizations

 

 

5.Introduction to Planning and Architecting Data Science Solutions

Introduction to Planning and Architecting Data Science Solutions • Frameworks to analyze a data science problem • How to choose an error metrics • What are the efficient ways to present results of data Science and data Analytics • What are different forms in which data is available

 

 

6.Introduction to Machine Learning - Methods and Algorithms

Fundamentals of Linear regression. • Linear regression Relationship between multiple variables: regression (Linear, Multi variate Linear Regression) in prediction. • Understanding the summary output of Linear Regression • Residual Analysis • Identifying significant features, feature reduction using AIC, multicollinearity check, observing influential points. • Non-normality and Heteroscedasticity • Hypothesis testing of regression Model • Confidence intervals of Slope • R-square and goodness of fit • Influential observations- leverage of Multiple linear Regression • Polynomial Regression • Categorical Variables in Regression • Hands-on Linear Regression Introduction and deep dive into logistic regression and the important concept of ROC curves • Logistic Regression • ROC curves • Logistic regression in classification; output interpretations • Hands-on logistic Regression Time Series Analysis • Decomposition of Time Series • Trend and Seasonality detection and forecasting • Smothering Techniques • Understanding ACF & PCF plots • ARIMA Modeling • Holt-Winter Method Principles and ideas in the field of Data Mining • Rule patterns, construction of rule-based classifier from data, turning trees into rules, rule growing strategy, rule evaluation and stopping criteria, several business metrics such as action ability, explicability and later turns towards association rules and cover them in detail. • Indirect from decision trees • Direct: Sequential covering • Market Basket Analysis, Apriori, Recommendation engines, Association Rules • How to combine clustering and classification • How to measure the quality of clustering – outlier analysis • Association Analysis • FP Trees • Hands-on with R Introduction and deep dive into logistic regression and the important concept of ROC curves • Top Induction of decision trees (TDIDT) • Attribution selection based on information theory approach • Recursive partitioning (binary search) • Id3, C4.5, C5.0 for pattern recognition problems, avoiding over fitting, converting trees to rules • Hands-on with R Distance-based classifiers • K-Nearest Neighbor algorithm • Aspects to consider while designing K-Nearest Neighbor • Hands-on example of K-Nearest Neighbor using R • Collaborative filtering Neural networks • Perceptron and Single Layer Neural Network. • Back Propagation algorithm and a typical Feed Forward Neural Net. • Hands-on with R with a Case. Support vector machines (SVM). • Linear learning machines and kernel space, making kernels and working in feature space. • SVM algorithm and comparison with Neural Nets • Demonstrate the working of SVM classification problems using a business case in R. Ensemble methods • Bagging and boosting and its impact on bias and variance • C 5.0 boosting • Random Forest • AdaBoost • Gradient boosting machines Unsupervised learning algorithm-Clustering • Different clustering methods; review of several distance measures • Iterative distance-based clustering • Dealing with continuous, categorical values in K-Means • Constructing a hierarchical cluster, K-medoids, k-mode and density-based clustering to handle different types in practice • Test for stability check of clusters • Hands on implementation of each of these methods will be conducted in R. Bayesian belief nets, Naïve Bayes, popular techniques to handle Overfitting and Under fitting • Introduction to generative techniques • Bayesian belief nets (BBN) • Naïve Bayes- a special case of BBN • Hands-on Naïve Bayes in R • How to avoid Overfitting and Under fitting • Refresher on all the machine learning algorithms

 

 

 

7. Text Mining and Natural language Processing

Text processing algorithms Basics of search engines • Introduction to the Fundamentals to the information retrieval; Language modeling • N-gram models of language Smoothing and probabilistic language models • Query likelihood model • 2-stage smoothing • Text Indexing and Crawling • Inverted Indexes • Boolean query processing • Handling phrase queries • Proximity queries • Crawling Relevance Ranking • Need for Relevance Ranking • TF and IDF • Thinking about the math behind the text; • Properties of words; Vector Space Model • Evaluation metrics for Ranking Link Analysis Algorithms • PageRank • HITS • Topic-sensitive PageRank • Spam Detection Algorithms Natural Language Processing • Stemming, phrase identification, word sense disambiguation • POS tagging Parsing and semantic structures Conference resolution Named Entity Recognition • What is NER? • Possible applications of NER • Evaluation and testing • NER methods

 

 

 

 8.DEEP LEARNING USING TENSORFLOW

• Basics of neural network • Linear algebra • Implementation of neural network in Vanilla • Basics of TensorFlow • Convolutional neural networks (CNNs) • Recurrent neural networks (RNNs) • Generative models • Semi-supervised learning using GAN • Seq-to-seq model • Encoder and decoder


Data Science Training Duration in Bangalore

Regular Classes( Morning, Day time & Evening)
  • Duration : 30 Days
Weekend Training Classes( Saturday, Sunday & Holidays)
  • Duration : 8 Weeks
Fast Track Training Program( 5+ hours daily)
  • Duration : Within 10 days

Data Science Trainer Profile

Our Data Science Trainers in our MyClass Training Bangalore Center
  • Has more than 8 Years of Experience.
  • Has worked on 3 realtime Data Science projects
  • Is Working in a MNC company in Bangalore
  • Already trained 60+ Students so far.
  • Has strong Theoretical & Practical Knowledge

Data Science Placements in Bangalore

Data Science Placement through MyClass Training Bangalore Center
  • More than 500+ students Trained
  • 87% percent Placement Record
  • 427 Interviews Organized
  • Data Science training in Multiple Locations across Bangalore

MyClass Advantage

  • Real Time Trainers
  • 100% Placement
  • Small Training Batch
  • Flexible Timings
  • Excellent Lab Facility
  • Practical Guidance
  • Hands on Experience
  • Certification Support
  •   Multiple Training Locations

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