## Course description

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.

**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.

**Schedule Career Counselling Session**

**Duration Of Course** – 70 Hours Instructor led Classroom Training

**Course Features:**

**Online And 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.

**Download Course Brochure**

INTRODUCTION TO DATA SCIENCE:**Course Content**

INTRODUCTION TO DATA SCIENCE

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 RR 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 basicsOperators in R – Arithmetic,Relational,Logical and Assignment Operators Variables,Types Of Variables,Using variables Conditional statements,ifelse(),switch Loops: For Loops,While Loops,Using Break statement,Switch |

3. The R Programming Language- Data Types And FunctionsUse 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 RFunction 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,DistributionSummary 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 graphicsBar 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 RDBMSCovers 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 OperationsSingle 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 -OperationsData Aggregations and summarizing the data Ranking Functions: Top-N Analysis Advanced SQL Queries for Analytics |

** PART C- PYTHON FOR DATA SCIENCE**

1. Python Programming BasicsInstalling Jupyter Notebooks Python Overview Python 2.7 vs Python 3 Python Identifiers Various Operators and Operators Precedence Getting input from User,Comments,Multi line Comments. |
2. Making Decisions And Loop ControlSimple if Statement,if-else Statement if-elif Statement. Introduction To while Loops. Introduction To for Loops,Using continue and break, |

3. Python Data Types: List,Tuples,DictionariesPython Lists,Tuples,Dictionaries Accessing Values Basic Operations Indexing, Slicing, and Matrixes Built-in Functions & Methods Exercises on List,Tuples And Dictionary |
4. Functions And ModulesIntroduction To Functions – Why Defining Functions Calling Functions 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 HandlingOpening 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. NumpyIntroduction to Numpy. Array Creation,Printing Arrays Basic Operations- Indexing, Slicing and Iterating Shape Manipulation – Changing shape,stacking and spliting of array Vector stacking |

7. Pandas And MatplotlibIntroduction 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 ProbabilityBasic understanding of linear algebra, Matrics, vectors Addition and Multimplication of matrics Fundamentals of Probability Probability distributed function and cumulative distributed function. Class Hand-onProblem solving using R for vector manupulation Problem solving for probability assignments |
2 Descriptive StatisticsDescribe 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. Class Hands-on:5 Point summary BoxPlot Histogram and Bar Chart Exploratory analytics R Methods |

3. Inferential StatisticsWhat 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 , Type-l error and Type-ll errorsHypothesis restingP-Value and Z-Score Method T-Test, Analysis of variance(ANOVA) and Analysis of Co variance( ANCOVA)Regression analysis in ANOVAClass Hands-on: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 TestingHypothesis Testing Basics of Hypothesis Testing Type of test and Rejection Region Type o errors-Type 1 Errors,Type 2 Errors P value method, MethodZ score |

**PART E- UNDERSTANDING AND IMPLEMENTING MACHINE LEARNING**

1. Introduction To Machine LearningWhat is Machine Learning? What is the Challenge? Introduction to Supervised Learning,Unsupervised Learning What is Reinforcement Learning? |
2. Linear RegressionIntroduction to Linear Regression Linear Regression with Multiple Variables Disadvantage of Linear Models Interpretation of Model Outputs Understanding Covariance and ColinearityUnderstanding Heteroscedasticity – Application of Linear Regression for Case StudyHousing Price Prediction |

3. Logistic RegressionIntroduction 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 or not spam using email as spamlogistic Regression. |
4. Decision Trees And Supervised LearningDecision 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. Naive Bayes Random Forests and Support Vector Machines Interpretation of Model Outputs Case Study:1 Business Case Study for Kart Model2 Business Case Study for Random Forest3 Business Case Study for SVM |

5. Unsupervised LearningHierarchical Clustering k-Means algorithm for clustering – groupings of unlabeled data points. Principal Component Analysis(PCA)- Data Independent components analysis(ICA) Anomaly Detection Recommender System-collaborative filtering algorithm Case Study– Recommendation Engine for e-commerce/retail chain |
6. Introduction to Deep LearningNeural Network Understaing Neural Network Model Understanding Tuning of Neural Network Case Study:Case study using Neural Network |

7. Natural language ProcessingIntroduction to natural Language Processing(NLP). Word Frequency Algorithms for NLP Sentiment Analysis Case Study :Twitter data analysis using NLP |
8. Apache Spark AnalyticsWhat is Spark Introduction to Spark RDD Introduction to Spark SQL and Dataframes Using R-Spark for machine learning Hands-on: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/SpotfireConnecting to data source Creating dashboard pages How to create calculated columns Different charts Hands-on:Hands on on connecting data source and data clensingHands on verious charts deployment of Predictive model in visualisationHands on |

*Certificates On successful Completion of Project And Assessment.*

5out of 5Nivedita– :.

4out of 5Vipul– :I would like to recommend any one who wants to be a Data Scientist.Explanations are clean, clear, easy to understand. Their support team works very well such any time you have an issue they reply and help you solving the issue.Good trainer for Machine Learning and R.

4out of 5Apporva– :I opted for data Science classroom Program.Trainers are good and have industry experience in data science domain.Content is well organized and real time case studies are covered.Got very good response from support team .Thanks Abhisek for your support.

5out of 5Tanmay– :I liked the data science course by learnbay. They provide both Python And R Programming .Also Course has SQL,Introduction to Apache Spark And Tableau.Many Case studies and Project helps a lot.

I would suggest working professional who wants to start their career in data science domain can enroll for the course.

5out of 5Vikas– :Good mixture of theoretical and practical training.Course helped me in all areas and understanding the vital concepts of machine learning and statistics.Very Flexible timing.

5out of 5Pushpraj– :Whole experience was great.Instructor carries good knowledge in subject and has rich experience in data science domain.