Data Science Training in Bangalore

Best Data Science Training in Bangalore:

Akshara Software Technologies is providing the best Data Science Training  Bangalore with most experienced professionals. Our trainers working in Data Science and related technologies for more 9 years in MNC’s. We are offering Data Science Classes in Bangalore in  more practical way. We are offering Data Science Classroom training Bangalore, Data Science Online Training and Data Science training in Bangalore. We framed our syllabus to match with the real world requirements for both beginner level to advanced level. Data Science Classes in HSR conducting in week day ,week end both morning and evening batches based on participant’s requirement. We do offer Fast-Track Data Science Training Bangalore and also One-to-One Data Science Training in Bangalore. Our participants will be eligible to clear all type of interviews at end of our sessions. Our Data Science classes in HSR focused on assisting in placements as well. We have separate HR team professionals located in Data Science Institute in BTM who will take care of all your interview needs. Our Data Science Training Course Fees is very affordable compared to others.Our Training Includes Data Science Real Time Classes Bnaglore , Data Science Live Classes , Data Science Real Time Scenarios

Data Science Training In Bangalore – Syllabus
(Duration – 60 Hours)
Contents:
 Introduction to Data Science
 Statistics
 Probability
 Data Wrangling
 Data Transformation
 Data Visualization
 Steps involved in Data Science
 Regression
 Classification
 Machine Learning Algorithms
 Ensemble Algorithms
 Extra Support
Course Content – In Detail:
Part 1 : Introduction to Data Science
 What is Data Science?
 Areas of Data science
 Career RoadMap
 Data Science toolbox
 What is BigData Analytics
 Steps involved in Data Science
 What skillsets required to be Data scientist
Part 2: Statistics
 Descriptive Statistics
 Inferential Statistics
 Types of variables
 Types of Graphs
 Population and Sample
 Five number summary
 Data Distributions
 Confidence Intervals
 Significance Tests ( Hypothesis)
 Standard Deviation
 Empirical Rule
 Standard Error
 Kurtosis and Skewness
 Center limit theorem
 Correlation and causation
 ANOVA
 Bias-Variance Trade-Off
Part 3: Probability
 Simple Probability
 Conditional Probability
 Baye’s Theorm
 Probability Distributions
Part 4: Data Wrangling
 Data Extraction
 Data Cleansing Techniques
 Exploratory Data Analysis
 Outliers
Part 5: Data Transformation
 Log transformation
 Arcsine transformation
 Box- Cox transformation
 Square root transformation
 Inverse transformation
 Min Max Data normalization
Part 6: Data Visualization
 Types of visualization
 Use cases for different charts
Part 7: Steps involved in Data Science
 Feature selection
 Feature Engineering
 Model assessment and Validation
 Evaluation of Model Performance
 Metrics
 Training and test split
 K-fold cross validation
 Dimension reduction methods
Part 8: Regression
 Simple Linear Regression
 Multiple Linear Regression
 Polynomial Regression
 SVR
 VIF analysis
 Multi-Collinearity
 L1 and L2 Regularization
 Overfitting and Under fitting
 Gradient Descent
Part 9: Classification
 Logistic Regression
 Linear Discriminate Analysis
 Confusion Matrix
Part 10: Machine Learning Algorithms
 Clustering
 Types of clustering techniques
 K-nearest Neighbor
 Naive Bayes
 Support Vector Machine ( SVM )
 CART (Classification and Regression Trees)
 Decision Trees
Part 11: Ensemble Algorithms
 Bagging
 Random Forest
 Boosting and AdaBoost
Part 12: Extra Support
 Mockup Interview in Data Science
 Real time use case implementation
 Overview of Big Data Analytics and Other latest Technologies