Data Science Training in Bangalore
TecMax is one of the leading Data Science Training Institute in Bangalore. Certified experts at TecMax are real-time consultants at multinational companies and have more than 5+ years of experience in Data Science Training. Our Trainers have conducted more than 200 classes and have extensive experience in teaching Data Science in most simple manner for the benefit of sudents.
We have advanced lab facilities for students to practice Data Science course and get hands-on experience in every topics that are covered under Data Science Training. In the presence of Data Science Trainer, students can execute all the techniques that has been explained by the instructor. Course Material for Data Science is specifically designed to cover all the advanced topics and each of the module will have both theory and practical classes. Data Science Batch Timings at TecMax are flexible and students can choose to join the batch as per their requirements. We have a batch starting every week for Data Science for regular students. Weekend batches and fast track batches for Data Science training can be arranged based on the requirement.
All our students will get placement assistance in Data Science after successfully completing the Data Science training from our institute. We are committed to provide high-quality training and provide assistance to get you the right job.
Data Science Course Content
vMeasure of Central Tendency
vMean( Arithmetic, Geometrical, Harmonic)
v1st, 2nd, 3rd, nth-Quartile
vMeasures Of Dispersion
v Frequency Distributions
Normal Distribution ( Symmetric)
vEstimate Sampling Errors
vDegrees of Freedom
vLinear Regression (Simple/Multi)
vNon- Linear Regression
vHypothesis Testing Models
vInferential Test Metrics
vAll above +
vData Clustering/ Categorizations
vKNN( K Nearest Neighbour)
vDecisions Science(Operational Research)
Time Series Analysis(Time Line Estimation)
Bend Moment Analysis
Different Basket Analysis(Ex- Recommending)
ARM(Association Role Management):
vAll Stat + Mining
vData Sets used in ML
vGradient Desent Algorithm
vDeployment of Modelfit in to Production
vTesting Accuracy ( Performance ) of Production Modelfits
vStar + Mining + ML
vPower of Brain
vWhere Brain Bad
vHow Different Neurons Will Communicate
vDeveloping Learning Systems Using Human Brain Models
vFeature Extractions( Feature Engineering)
vRecent Data Buffering
vInfluenced Data Buffering
vForward / Backward Propagation Algorithms
vIntegrated Best Features of Both Machine Learning and Neural Networks
vUser Behavioural Analysis
vGraph Data Processing
vCustomer Review Analysis
vIdentifying User Expectations and Preferences
vImportance of Big Data for Data Science
vData Streaming for live Systems ( Ex-Flume/ Kufka)
vLive Analytics ( Real time-Analytics) (Ex: STORM)
vMicro Batch Process( Ex: Spark Streaming)
vBatch Process(Ex: Hadoop)
We Also Provide Data Science Training in Marathahalli
Data Science Interview Questions
1.What is data science in simple words?
Answer : Data science is the study of data. It involves developing methods of recording, storing, and analyzing data to effectively extract useful information. The goal of data science is to gain insights and knowledge from any type of data — both structured and unstructured.
2.Why is data science used?
Answer : Data science uses techniques such as machine learning and artificial intelligence to extract meaningful information and to predict future patterns and behaviors. The field of data science is growing as technology advances and big data collection and analysis techniques become more sophisticated
3.What is selection Bias ?
Answer : Selection bias occurs when sample obtained is not representative of the population intended to be analysed.
4.What are the different kernels functions in SVM ?
Answer : There are four types of kernels in SVM.
- Linear Kernel
- Polynomial kernel
- Radial basis kernel
- Sigmoid kernel
5. What is pruning in Decision Tree ?
Answer : When we remove sub-nodes of a decision node, this process is called pruning or opposite process of splitting.
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