Best Data Science Training institute in Bangalore

Best  Data Science Training in Bangalore at HSR , BTM & Koramangala:

Akshara Software Technologies is providing the best Data Science training in HSR layout, BTM Layout, and koramangala with most experienced professionals. Our trainer working in Data Science and related technologies for more 11 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 Corporate Training 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. 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 HSR Layout, BTM & Koramangala:
Data Science Course Details:
  • Duration : 100-120 Hours (R Programming+Data Science)
  • Demo and First 3 classes free
  • Real Time training with  hands on Project
  • Assignment and Case Studies
  • Week Day & Week End Batches

The course will help the students by:

(i) Providing guidelines to identify and describe real life problems so that relevant data can be collected

(ii) Linking data generation process with statistical distributions, especially in the multivariate domain

(iii) Linking the relationship among the variables (of a process or system) with multivariate statistical models

(iv) Providing step by step procedure for estimating parameters of a model developed

(v) Analyzing errors along with computing overall fit of the models

(vi) Interpreting model results in real life problem solving

(vii) Providing procedures for model validation. R


1 Introduction to Software and Operating Systems (OS)
2 Introduction to programming
3 Control Structures
4 Functions and Algorithms
5 Complexity 6 File operations

Statistical Techniques and Prerequisite for data science

1.Algebra of Sets
2 Probability
3 Random Variables
4 Special Distributions
5 Function of a random variable, problems
6 Joint Distributions
7 Transformations
8 Sampling Distributions
9 Descriptive Statistics
10 Estimation 11 Testing of Hypotheses

Modelling and Machine Learning

1 Introduction to multivariate statistical modeling
2 Univariate descriptive statistics
3 Multivariate descriptive statistics
4 Multivariate normal distribution
5 Multivariate Inferential statistics
6 Analysis of variance (ANOVA)
7 Multivariate analysis of variance (MANOVA)
8 Tutorial: ANOVA
9 Case study: MANOVA
10 Multiple linear regression (MLR): Introduction
11 MLR: Sampling distribution of regression coefficients
12 MLR: Model adequacy tests
13 MLR: Test of assumptions
14 MLR: Model diagnostics
15 MLR: Case study
16 Multivariate linear regression (MvLR): Introduction
17 MvLR: Estimation
18 MvLR: Model adequacy tests
19 Regression modeling using R/SAS
20 Principle component analysis (PCA): Introduction
21 PCA: Model adequacy and interpretation
22 Factor analysis (FA): Introduction
23 FA: Estimation and model adequacy testing
24 FA: Rotation, factor scores, and case study
25 Cluster analysis (CA)
26 Introduction to structural equation modeling (SEM)
27 Correspondence analysis
28 Decision Tree
29 Bayes Classifier
30 Logistic Regression
31 Time Series Modelling
32 Support Vector Machine
33 Random Fores

Data Visualization (Any one tool from below mentioned

2 Tableau
3 Power BI
4 Shiny


Demo & More Details ?

Whats-App Or Call –  9686770604

Akshara Software Technologies

HSR Ring Road , Near Silk Board

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