Data Science Classes in BTM Layout Bangalore

IT Networks offers the best Data Science Training in BTM Layout with most experienced professionals. We conduct all our students from the Basic Level of Data Science Classes to Advanced level. All these Classes are undergone not only theoretically, but also are executed on the real-time basis. Our Trainers are Real-time Working professionals in the Data Science Field from many years with hands on real time Data Science Project Knowledge.

IT Networks is a highly experienced Data Science Training Institute in BTM Layout. This has helped students to get placed in top MNCs. We Offer Data Science Classes in BTM Layout for working professionals and students. we offer Regular training classes in Morning Batches and Evening Batches we also offer Weekend Training classes and Fast-track Training Classes for Data Science Course.

BEST INSTITUTE FOR DATA SCIENCE TRAINING IN  BANGALORE

INTRODUCTION TO DATA SCIENCE

BASICS OF STATISTICS –

Descriptive Statistics for

Mean, Median, Mode, Quartile, Percentile, Inter-Quartile Range

Standard Deviation, Variance

Descriptive Statistics for two variables

Z-Score

Co-variance, Co-relation

Chi-squared Analysis

Hypothesis Testing

 

PROBABILITY CONCEPTS –

Basic Probability, Conditional Probability

Properties of Random Variables

Expectations, Variance

Entropy and cross-entropy

Estimating probability of Random variable

Understanding standard random processes

 

DATA DISTRIBUTIONS

Normal Distribution

Binomial Distribution

Multinomial Distribution

Bernoulli Distribution

Probability, Prior probability, Posterior probability

Naive Bayes Algorithm

 

BASIC MATHEMATICS FOR DATA SCIENCE

Limits,

Derivatives, Partial Derivatives

Gradients, Significance of Gradients

 

MASTERING PYTHON/R LANGUAGE

How to install python (Anaconda), sciKit Learn

How to work with Jupyter Notebook and Spyder IDE

Strings, Lists, Tuples, and Sets

Dictionaries, Control Flows, Functions

Formal/Positional/Keyword arguments

Predefined functions (range, len, enumerates etc…)

Data Frames

Packages required for data Science in R/Python

 

INTRODUCTION TO NUMPY

One-dimensional Array, Two-dimensional Array

Pre-defined functions (arrange, reshape, zeros, ones, empty)

Basic Matrix operations

Scalar addition, subtraction, multiplication, division

Matrix addition, subtraction, multiplication, division and transpose

Slicing, Indexing, Looping

Shape Manipulation, Stacking

 

INTRODUCTION TO PANDAS

Series, DataFrame, GroupBy

Crosstab, apply and map

 

DATA PREPARATION TECHNIQUES

Applications of PCA: Dimensionality Reduction

Feature Engineering (FE)

Combine Features

Split Features

Reg. for Free Demo

 

DATA VISUALIZATION

Bar Chart

Histogram

Box whisker plot

Line plot

Scatter Plot and Heat Maps

 

Machine Learning Algorithm – Data Preparation and Execution

Linear Regression

Logistic Regression

Optimization (Gradient Descent etc.)

Decision Tree

Random Forest

Boosting and AdaBoost

Clustering Algorithms (KNN and K-Means)

Support Vector Machines

Nave Bayes Algorithm

Neural Networks

Text Mining (NLTK)

Introduction to Deep learning