Data Science Courses in Bangalore|Data Science Training in Bangalore

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Data Science

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myTectra the Market Leader in Data Science Training in Bangalore

myTectra offers Data Science training in bangalore using Class Room. myTectra offers Live Online Data Science Training Globally.

Getting Started With Data Science And Recommender Systems Data Science Overview Reasons to use Data Science Project Lifecycle Data Acquirement Evaluation of Input Data Transforming Data Statistical and analytical methods to work with data Machine Learning basics Introduction to Recommender systems Apache Mahout Overview Reasons To Use, Project Lifecycle What is Data Science? What Kind of Problems can you solve? Data Science Project Life Cycle Data Science-Basic Principles Data Acquisition Data Collection Understanding Data- Attributes in a Data, Different types of Variables Build the Variable type Hierarchy Two Dimensional Problem Co-relation b/w the Variables- explain using Paint Tool Outliers, Outlier Treatment Boxplot, How to Draw a Boxplot Acquiring Data Discussion on Boxplot- also Explain Example to understand variable Distributions What is Percentile? – Example using Rstudio tool How do we identify outliers? How do we handle outliers? Outlier Treatment: Using Capping/Flooring General Method Distribution- What is Normal Distribution Why Normal Distribution is so popular Uniform Distribution Skewed Distribution Transformation Machine Learning In Data Science Discussion about Box plot and Outlier Goal: Increase Profits of a Store Areas of increasing the efficiency Data Request Business Problem: To maximize shop Profits What are Interlinked variables What is Strategy Interaction b/w the Variables Univariate analysis Multivariate analysis Bivariate analysis Relation b/w Variables Standardize Variables What is Hypothesis? Interpret the Correlation Negative Correlation Machine Learning Statistical And Analytical Methods Dealing With Data, Implementation Of Recommenders Using Apache Mahout And Transforming Data Correlation b/w Nominal Variables Contingency Table What is Expected Value? What is Mean? How Expected Value is differ from Mean Experiment – Controlled Experiment, Uncontrolled Experiment Degree of Freedom Dependency b/w Nominal Variable & Continuous Variable Linear Regression Extrapolation and Interpolation Univariate Analysis for Linear Regression Building Model for Linear Regression Pattern of Data means? Data Processing Operation What is sampling? Sampling Distribution Stratified Sampling Technique Disproportionate Sampling Technique Balanced Allocation-part of Disproportionate Sampling Systematic Sampling Cluster Sampling 2 angels of Data Science-Statistical Learning, Machine Learning Testing And Assessment, Production Deployment And More Multi variable analysis linear regration Simple linear regration Hypothesis testing Speculation vs. claim(Query) Sample Step to test your hypothesis performance measure Generate null hypothesis alternative hypothesis Testing the hypothesis Threshold value Hypothesis testing explanation by example Null Hypothesis Alternative Hypothesis Probability Histogram of mean value Revisit CHI-SQUARE independence test Correlation between Nominal Variable Business Algorithms, Simple Approaches To Prediction, Building Model, Model Deployment Machine Learning Importance of Algorithms Supervised and Unsupervised Learning Various Algorithms on Business Simple approaches to Prediction Predict Algorithms Population data sampling Disproportionate Sampling Steps in Model Building Sample the data What is K? Training Data Test Data Validation data Model Building Find the accuracy Rules Iteration Deploy the model Linear regression Getting Started With Segmentation Of Prediction And Analysis Clustering Cluster and Clustering with Example Data Points, Grouping Data Points Manual Profiling Horizontal & Vertical Slicing Clustering Algorithm Criteria for take into Consideration before doing Clustering Graphical Example Clustering & Classification: Exclusive Clustering, Overlapping Clustering, Hierarchy Clustering Simple Approaches to Prediction Different types of Distances: 1.Manhattan, 2.Euclidean, 3.Consine Similarity Clustering Algorithm in Mahout Probabilistic Clustering Pattern Learning Nearest Neighbor Prediction Nearest Neighbor Analysis Integration Of R And Hadoop R introduction How R is typically used Features of R Introduction to Big data R+Hadoop Ways to connect with R and Hadoop Products Case Study Architecture Steps for Installing RIMPALA How to create IMPALA packages

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Customer Reviews

Well Explained Review by R channa

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We attended Python with Data Science course in July. My daughter is new to programming and I was new to Data Science. Ankit explained the concepts well and was very thorough. He follows up on mail questions and helped with assignments. Please pass on our sincere thanks to Ankit

I have completed Data Science course from myTectra Review by Praveen Shetty

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I am overall satisfied with the course content and the way it progressed.
The facility possess good knowledge on the subject, explained the real time scenarios and the material provided by trainer very useful for our preparation.

I have completed Data Science course from myTectra Review by Kiran Sonar

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I have completed Data Science.Need to bring to your attention of my status of program to complete my level of expectation. Trainer has put expertise and commitment towards course & he is very passionate. Thanks for your support from Support Team....

I have completed Data Science course from myTectra Review by B S Natesh

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I have completed Data Science course, All the classes went very well. Trainer showed real time example that helps to us & achieved, I would like to say special thanks for Support Team, myTectra. Awesome...

I have completed Data Science course from myTectra Review by Kalyan

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Awesome place to learn and enhance the technical skills, skilled trainers and best staff. Friendly environment. Recommended.

Job oriented software training provided Review by Nickson

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Hi, I thank mytectra team for providing me good faculty and practical sessions . Good placements are also provided by them.

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myTectra flexible batch schedules enables you to start your class at your convenient date and time. 1. All the enrolled candidates must start the class by any date within 30 days from the date of enrollment. 2. Maximum of 3 Days will be taken by myTectra to schedule the batches from the date of schedule request from the candidate. 3. Individual Focus - Maximum 5 candidates allowed per batches 4. The candidate can request any one of the batch time slot from the below list

Batch Type	Time Slab	Hours/Day	Time Zone	Days
Regular Morning	6.30 AM - 9.30 AM	Anytime Maximum 2 Hrs	IST	Monday-to-Friday
Regular Evening	6.30 PM - 9.30 PM	Anytime Maximum 2 Hrs	IST	Monday-to-Friday
Week End	6.30 AM - 9.30 PM	Anytime Maximum 3 Hrs	IST	Saturday,Sunday
Fast Track-Morning	6.30 AM - 9.30 AM	Anytime Maximum 2 Hrs	IST	Monday-to-Saturday
Fast Track-Evening	6.30 PM - 9.30 PM	Anytime Maximum 2 Hrs	IST	Monday-to-Saturday
Customized Batch	Customized Timing	Customized Hrs	IST	Customized Days

Note : We can schedule your batch anytime on Saturday and Sunday between 6.30 AM 9.30 PM at your convenient time

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