Data Science Training in Bangalore
Learnbay Provides data science training in bangalore from certified experts.Our course helps you to learn various data analytics techniques using R and Python programming.Data Science Course content is designed by experts to match with the real world requirements for both beginner and advance level.Many real world problems and case studies are implemented throughout the course and discussed in the class with tons of assignments for practice.If you are looking for data science training in Bangalore,Enroll for the Free Demo Session.
This course will benefit you to master data science skills and will help you to to handle interview with more confidence if you are looking for job in data science domain.
Note: Data Science Course Update : Tensor Flow And Deep Learning With Projects is added to data science Course
Who Should Attend:
Those who want to become master in data science and Data Analytics in R Programming.
Business Analysts who want to learn machine learning
Data Analysts who wants to improve their skills.
Developers aspiring to become data scientist.
Freshers/Experienced Professional,Managers,IT professional
Prerequisite For Data Science Course:
There is no prerequisite for this course.If you are new to data science,this course is best for you.Basic Understanding of statistics/Linear Algebra/Probability and R/Python will help.
Duration Of Course – 130 Hours ( Around 4.5 months) Instructor led Classroom Training
Course Modules :
Deep Learning And Tensor Flow
5 Real Time Project,Interview Prep And Resume Guidance
Live Classroom data science training in Bangalore by industry experts
Classes with 40% theory and 60% hands on
Trainers having more than 10+ years of experience in multiple domains like finance,Healthcare ,Retails.
Practical Approach With Mini Projects And Case studies.
Job Assistance And Placement Support After end of Course.
INTRODUCTION TO DATA SCIENCE:Course Content
|INTRODUCTION TO DATA SCIENCE:
What is data Science? – Introduction.
Importance of Data Science.
Demand for Data Science Professional.
Brief Introduction to Big data and Data Analytics.
Lifecycle of data science.
Tools and Technologies used in data Science.
Business Intelligence vs Data Science.
Role of a data scientist.
PART A – R PROGRAMMING BASICS
|1. Introduction to R
R Basics, background.
Comprehensive R Archive Network
Demo of Installing R On windows from CRAN Website
Installing R Studios on Windows OS
Setting Up R Workspace.
Getting Help for R-How to use help system
Installing Packages – Loading And Unloading Packages
|2. Starting with R : Getting familiar with basics
Operators in R – Arithmetic,Relational,Logical and Assignment Operators
Variables,Types Of Variables,Using variables
Loops: For Loops,While Loops,Using Break statement,Switch
|3. The R Programming Language- Data Types And Functions
Use R for simple maths, creating data objects from the keyword.
How to make different type of data objects.
Understand the various data types that the language supports.
Introduction to Functions in R
Types of data structures in R
Arrays And Lists- Create Access the elements
Vectors – Create Vectors,Vectorized Operations,Power of Vectorized Operations
Matrices- Building the first matrices,Matrix Operations,Subsetting,visualising subset,Visualising with matplot()
Factors – Creating a Factor
Data Frames- create and filter data frames,Building And Merging data frames.
|4. Functions And Importing data into R
Function Overview – Naming Guidelines
Arguments Matching,Function with Multiple Arguments
Additional Arguments using Ellipsis,Lazy Evaluation
Multiple Return Values
Function as Objects,Anonymous Functions
Importing and exporting Data into R- importing from files like excel,csv and minitab.
Import from URL and excel Files
Import from database.
|5. Data Descriptive Statistics,Tabulation,Distribution
Summary Statistics for Matrix Objects. apply() Command.Converting an Object into a Table Histograms, Stem and Leaf Plot, Density Function. Normal Distribution
|6. Graphics in R – Types of graphics
Bar Chart,Pie Chart,Histograms- Create and edit.
Box Plots- Basics of Boxplots- Create and Edit
Visualisation in R using ggplot2.
More About Graphs: Adding Legends to Graphs Adding Text to Graphs, Orienting the Axis Label.
PART B – INTRODUCTION TO SQL
|1. Introduction to SQL Server and RDBMS
Covers an overview of using relational databases. You’ll learn basic terminology used in future modules,SQL Server Management Studio is the primary tool used to create queries and manage objects in SQL Server databases
|2. SQL Operations
Single Table Queries – SELECT,WHERE,ORDER BY,Distinct,And ,OR
Multiple Table Queries: INNER, SELF, CROSS, and OUTER,oin, Left Join, Right Join, Full Join, Union and MANY MORE…..
|3. SQL Advance -Operations
Data Aggregations and summarizing the data
Ranking Functions: Top-N Analysis
Advanced SQL Queries for Analytics
PART C- PYTHON FOR DATA SCIENCE
|1. Python Programming Basics
Installing Jupyter Notebooks
Python 2.7 vs Python 3
Various Operators and Operators Precedence
Getting input from User,Comments,Multi line Comments.
|2. Making Decisions And Loop Control
Simple if Statement,if-else Statement
Introduction To while Loops.
Introduction To for Loops,Using continue
|3. Python Data Types: List,Tuples,Dictionaries
Indexing, Slicing, and Matrixes
Built-in Functions & Methods
Exercises on List,Tuples And Dictionary
|4. Functions And Modules
Introduction To Functions – Why
Functions With Multiple Arguments.
Anonymous Functions – Lambda
Using Built-In Modules,User-Defined Modules,Module Namespaces,
Iterators And Generators
|5. File I/O And Exceptional Handling
Opening and Closing Files
open Function,file Object Attributes
close() Method ,Read,write,seek.Exception Handling,the try-finally Clause
Raising an Exceptions,User-Defined Exceptions
Regular Expression- Search and Replace
Regular Expression Modifiers
Regular Expression Patterns,re module
| 6. Numpy
Introduction to Numpy. Array Creation,Printing Arrays
Basic Operations- Indexing, Slicing and Iterating
Shape Manipulation – Changing shape,stacking and spliting of array
|7. Pandas And Matplotlib
Introduction to Pandas
Importing data into Python
Pandas Data Frames,Indexing Data Frames ,Basic Operations With Data frame,Renaming Columns,Subletting and filtering a data frame.
Matplotlib – Introduction,plot(),Controlling Line Properties,Working with Multiple Figures,Histograms
PART D- INTRODUCTION TO STATISTICS
|1. Fundamentals of Math and Probability
Basic understanding of linear algebra, Matrics, vectors
Addition and Multimplication of matrics
Fundamentals of Probability
Probability distributed function and cumulative distributed function.
Problem solving using R for vector manupulation
Problem solving for probability assignments
|2 Descriptive Statistics
Describe or sumarise a set of data
Measure of central tendency and measure of dispersion.
The mean,median,mode, curtosis and skewness
Computing Standard deviation and Variance.
Types of distribution.
5 Point summary BoxPlot
Histogram and Bar Chart
Exploratory analytics R Methods
|3. Inferential Statistics
What is inferential statistics
Different types of Sampling techniques
Central Limit Theorem
Point estimate and Interval estimate
Creating confidence interval for population parameter
Characteristics of Z-distribution and T-Distribution
Basics of Hypothesis Testing
Type of test and rejection region
Type of errors in Hypothesis resting, Type-l error and Type-ll errors
P-Value and Z-Score Method
T-Test, Analysis of variance(ANOVA) and Analysis of Co variance(ANCOVA)
Regression analysis in ANOVA
Problem solving for C.L.T
Problem solving Hypothesis Testing
Problem solving for T-test, Z-score test
Case study and model run for ANOVA, ANCOVA
|4. Hypothesis Testing
Basics of Hypothesis Testing
Type of test and Rejection Region
Type o errors-Type 1 Errors,Type 2 Errors
P value method,Z score Method
PART E- UNDERSTANDING AND IMPLEMENTING MACHINE LEARNING
|1. Introduction To Machine Learning
What is Machine Learning?
What is the Challenge?
Introduction to Supervised Learning,Unsupervised Learning
What is Reinforcement Learning?
|2. Linear Regression
Introduction to Linear Regression
Linear Regression with Multiple Variables
Disadvantage of Linear Models
Interpretation of Model Outputs
Understanding Covariance and Colinearity
Case Study – Application of Linear Regression for Housing Price Prediction
|3. Logistic Regression
Introduction to Logistic Regression.– Why Logistic Regression .
Introduce the notion of classification
Cost function for logistic regression
Application of logistic regression to multi-class classification.
Confusion Matrix, Odd’s Ratio And ROC Curve
Advantages And Disadvantages of Logistic Regression.
Case Study:To classify an email as spam or not spam using logistic Regression.
|4. Decision Trees And Supervised Learning
Decision Tree – data set
How to build decision tree?
Understanding Kart Model
Classification Rules- Overfitting Problem
Stopping Criteria And Pruning
How to Find final size of Trees?
Model A decision Tree.
Random Forests and Support Vector Machines
Interpretation of Model Outputs
1 Business Case Study for Kart Model
2 Business Case Study for Random Forest
3 Business Case Study for SVM
|5. Unsupervised Learning
k-Means algorithm for clustering – groupings of unlabeled data points.
Principal Component Analysis(PCA)- Data
Independent components analysis(ICA)
Recommender System-collaborative filtering algorithm
Case Study– Recommendation Engine for e-commerce/retail chain
|6. Introduction to Deep Learning
Understaing Neural Network Model
Understanding Tuning of Neural Network
Case study using Neural Network
|7. Natural language Processing
Introduction to natural Language Processing(NLP).
Word Frequency Algorithms for NLP
Case Study :
Twitter data analysis using NLP
|8. Apache Spark Analytics
What is Spark
Introduction to Spark RDD
Introduction to Spark SQL and Dataframes
Using R-Spark for machine learning
installation and configuration of Spark
Hands on Spark RDD programming
Hands on of Spark SQL and Dataframe programming
Using R-Spark for machine learning programming
|9. Introduction to Tableau/Spotfire
Connecting to data source
Creating dashboard pages
How to create calculated columns
Hands on on connecting data source and data clensing
Hands on verious charts
Hands on deployment of Predictive model in visualisation
PART F – Deep Learning And TensorFlow
|1. Introduction to Deep Learning And Tensor Flow
Understaing Neural Network Model
Simple Computation ,Contants And Variables
Types of file formats in TensorFlow
Creatting A Graph – Graph Visualization
Creating a Model – Logistic Regression Model Building
TensorFlow Classification Examples
|2.Convolutional Neural Network(CNN)
Convolutional Layer Motivation
Convolutional Layer Application
Architecture of a CNN
Pooling Layer Application
Understanding and Visualizing a CNN.
|3.Understanding Of TFLearn APIs
Getting Started With TFLearn
High-Level API usage -Layers,
Built-in Operations,Training and Evaluatiion-Customizing the Training Process,Visualization APIs
Sequential And Functional Composition
Using TensorBoard with TFLearnProjects And Case StudiesBuilding a CNN for Image Classification
Certificates On successful Completion of Project And Assessment.