Data Science Brochure

1. Click To Chat on Whatsapp Learnbay IBM Certified Data Science Program For Working Professionals 12+ Real Time Project And Placement Assistance Program Bangalore Pune Delhi Live Online

2. Learnbay Offers Job Oriented Data Science Certification Program in association With IBM especially designed for working professional. Course Covers Python Programming,R,SAS, Statistics ,Advance Machine Learning,Deep learning using tensor- flow ,Deployment of Machine learning model ,Tableau, Mongo-db And Hadoop/spark. Course is especially designed for working professionals who have experience in other domain/technology(IT or Non-IT) and want to start his career in data Science/Analytics. Who Should Attend: Software developers/Programmers Database Admin and System Admin and telecom engineer Manual And Automation Test Engineer, Java and .net Developer SAP domain expert Python Developer ,Embedded developer Program Manager, Program Manager Eligibility : Working professionals having exp. of 1+ yrs in any domain (Technical/Non- Technical) Course Modules And Tools Covered  Python For Data Science R Programming  Statistics For Data Science Machine Learning Algorithms Tensor-Flow And Deep Learning Natural Language Processing Time Series Forecasting Certification From IBM SQL And Mongodb Cloud Deployment Of ML Model Tableau And PowerBI 2 Capstone Project in ML Hadoop & Spark Analytics Resume Prep Session Interview Prep & Mock Interview 12+ Real Time Project Job Referral And Placement Assurance Course Duration            Training Mode Weekday: 3.5 Months (Mon to Fri - 2 hrs everyday, 8:00 am to 10:00 am IST) Classroom Training in Bangalore | Pune | Delhi Course Fee for Classroom:Rs. 59,000 /- + taxes Instructor Led Live Online Training Course Fee for Live Online :Rs. 49,000 /- + taxes Weekends: 6 Months Sat & Sun : 4 hrs on Sat & Sun 6 Months No-cost/Interest free EMI On Credit Cards Loan option is available without credit cards

3. Course Highlights: 200+ hours of classroom training(Bangalore, Pune, Delhi) from Industry expert 12+ Real Time Industry Projects 300+ hours of coding Assignment & Case Studies 100% Interview Call Guarantee for working professionals (Eligibility : 1.5+ Yrs. of exp. in any domain) Card-less EMI(6 Months) And Loan Available(Subjected to Loan Approval) Certification From IBM in Data Science and AI  How Flexi Classroom Subscription Helps? Flexibility to attend multiple batches from Weekends and weekdays. Flexibility to revise the modules and attend training multiple times from different instructor. Attend Classroom from multiple location(Bangalore, Pune, Delhi) Lifetime Access to Classroom Videos/Recorded Session and LMS Flexibility to attend instructor led live online session as well. One Year Subscription for unlimited Classroom Session & Project Mentorship Job Roles You Can Target After Course Data Scientist Data Analyst Machine Learning Engineer Data Science Manager Data Analytics Manager Course Fee : Classroom Course Fee: Rs 59,000/- + taxes  Live Online Course Fee:  Rs 49,000/- + taxes  Appply For6 Months No Cost EMI  Option 1 : No Cost EMI On Credit Card Available on ICICI, HDFC, RBL, Standard Chartered, Axis bank credit cards Option 2 : Interest Free Instant Loan | Without Credit Card Instant Online Approval in 24 hours: Only Aadhar And PAN required Click To Apply Interest Free Loan | Pay in 6 EMI  Whatsapp Now For Applicable Discount Coupon

4. Modules/Tools Covered SQL Time Series Natural Language Processing

5. Sample Certificate

6. Sample Class And Demo Recordings

7. Success Stories & Placements Shakti Suwan Lead Analyst at Amex Bikash Bhuyan Data Scientist at Shell All the faculties/trainers are superb.They know the concepts of their respective areas.. They are well versed that what a new comer wants to know & understand..Really a superb institute & awesome trainers.Outstanding institute for Data Science for professionals. Srikanth Saurav Senior Data Scientist at EY Machine Learning concepts & Statistics are very well explained by Utkarsh. Best thing was completing the syllabus on-time as they have promised. Trainers are clearing the doubts in classroom.Got multiple joining offers from different MNCs for Data Science and AI developer role I Joined Learnbay as Fresher And Attended training in data science And Artificial Intelligence.Course is job oriented, Practical and in-depth .To the point, well versed trainers, well engineered course. Superb!! View Linkedin Profile View Linkedin Profile View Linkedin Profile Vidya Ashish Kumar Swain Working in Accenture AI Aswini Dindukurthy Working in Deloitte My name is Aswini Dindukurthy, I have taken Data Science course from Learnbay 3 years back, it is Excellent training center. After my training I was equal to 3+ exp. I had a very good trainer , Real-Time Project Oriented Classes, but one thing I have to say to all that daily practice is very much needed. Senior Analyst At Allegion This course helped me to understand the datascience concepts clearly with adequate hands on sessions.The curriculum is awesomely designed in a way that all the basics were covered by expert tutors before the actual machine learning session. The curriculum is very particular and lean enough to give detail knowledge in data science.Course is good for working professionals.Trainers are from industry and expert in their domain View Linkedin Profile View Linkedin Profile View Linkedin Profile Rajeev Kumar Suman Karmakar Technical specialist At IBM It was a good and effective course with dedicated faculties for modules.You get flexibility to attend classes from multiple instructors.Very Supportive environment for learning. Read More Reviews Consultant at Tata Group Good Trainer and nice supportive environment.One of the best classroom institute in Bangalore for working professionals looking to change their domain to data science. View Linkedin Profile View Linkedin Profile

8. Real Time Project Work On Real Time Projects From Multiple Domain With Industry Expert Download List Of Projects Click To Watch Project Session Recordings Job Assistance Resume Prep Session Mock Interview And Get Referral in Companies For Data Science Roles Click To Read Google Reviews 

9. How Job Assistance Works For Any Queries About Placement Assistance or Real Time Project ,Feel Free to Chat on Whatsapp Now or Schedule Telephonic Counselling Session  Click Here To Read Course FAQ Apply For Free Profile Review  Live Chat On Whatsapp

10. Table Of Contents With Duration 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- PYTHON FOR DATA SCIENCE (4 Weeks : 32 hours) 2. Making Decisions And Loop Control Simple if Statement,if-else Statement if-elif Statement. Introduction To while Loops. Introduction To for Loops,Using continue and break, 1. Python Programming Basics Installing 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. 3. Python Data Types: List,Tuples,Dictionaries Python Lists,Tuples,Dictionaries Accessing Values Basic Operations Indexing, Slicing, and Matrixes Built-in Functions & Methods Exercises on List,Tuples And Dictionary 4. Functions And Modules Introduction 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 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 Vector stacking

11. 7. Pandas And Matplotlib And Seaborn 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 Intro to Seaborn And Visualizing statistical relationships .Plotting with categorical data and Visualizing linear relationships 8. Case Studies Using Numpy,Pandas 3 Case Studies on Numpy,Pandas And Matplotlib PART B – R PROGRAMMING (3 Weeks : 24 hours) 1. 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. Getting familiar with basics Operators 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 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. 3. The R Programming Language- Data Types creating data objects from the keyword. How to make different type of data objects. 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 Data Frames- create and filter data frames,Building And Merging data frames. 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. 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

12. PART C- STATISTICS FOR DATA SCIENCE (3 Week -24 hours) 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. Class Hand-on 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. Class Handson: 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 Class 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 Testing 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

13. PART D – MACHINE LEARNING ALGORITHMS (6 Week - 48 hours) 2. Linear Regression Introduction to Linear Regression Linear Regression with Multiple Variables Disadvantage of Linear Models Interpretation of Model Outputs Understanding Covariance and Colinearity Understanding Heteroscedasticity Case Study – Application of Linear Regression for Housing Price Prediction 1. Introduction To Machine Learning What is Machine Learning? What is the Challenge? Introduction to Supervised Learning,Unsupervised Learning What is Reinforcement Learning? 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. Naive Bayes Random Forests and Support Vector Machines Interpretation of Model Outputs Case Study: 1 Business Case Study for Kart Model 2 Business Case Study for Random Forest 3 Business Case Study for SVM 5. Unsupervised Learning Hierarchical 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. Natural language Processing Introduction to natural Language Processing(NLP). Word Frequency Algorithms for NLP Sentiment Analysis Case Study : Twitter data analysis using NLP 8. ARIMA and Multivariate Time Series Analysis Introduction to ARIMA Models,ARIMA Model Calculations,Manual ARIMA Parameter Selection,ARIMA with Explanatory Variables Understanding Multivariate Time Series and Their Structure,Checking for Stationarity and Differencing the MTS Case Study : Performing Time Series Analysis on Stock Prices 7. Introduction to Time Series Forecasting Basics of Time Series Analysis and Forecasting ,Method Selection in Forecasting Moving Average (MA) Forecast Example,Different Components of Time Series Data ,Log Based Differencing, Linear Regression For Detrending Important Note : All Machine Learning Algorithms are covered in depth with Real time case studies for each Algorithm Once 60% of ML is completed ,Capstone Project will be released for the batch.

14. PART E – TENSORFLOW AND DEEP LEARNING ( 3 Week : 20 hours) 1. Introduction to Deep Learning And Tensor Flow Neural Network Understaing Neural Network Model Installing TensorFlow Simple Computation ,Contants And Variables Types of file formats in TensorFlow Creatting A Graph – Graph Visualization Creating a Model – Logistic Regression Model Building using tensor flow TensorFlow Classification Examples 2.Convolutional Neural Network(CNN) Convolutional Layer Motivation Convolutional Layer Application Architecture of a CNN Pooling Layer Application Deep CNN 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 Fine tuning, Using TensorBoard with TFLearn Projects And Case Studies Building a CNN for Image Classification PART F – Introduction To Tableau ( 1 Week : 8 hours) 1. Introduction to Tableau Connecting to data source Creating dashboard pages How to create calculated columns Different charts Hands-on: Hands on on connecting data source and data cleansing Hands on various charts 2. Visual Analytics Getting Started With Visual Analytics Sorting and grouping Working with sets, set action Filters: Ways to filter, Interactive Filters Forecasting and Clustering Hands-on: Hands on deployment of Predictive model in visualisation

15. PART G : NATURAL LANGUAGE PROCESSING ( 3 Week : 20 hours) 2. Text Pre Processing Techniques Need of Pre-Processing Various methods to Process the Text data Tokenization ,Challenges in Tokenization Stopping ,Stop Word Removal Stemming - Errors in Stemming Types of Stemming Algorithms - Table lookup Approach ,N-Gram Stemmers 1. Introduction to NLP & Text Analytics Introduction to Text Analytics Introduction to NLP What is Natural Language Processing? What Can Developers Use NLP Algorithms For? NLP Libraries Need of Textual Analytics Applications of Natural Language Procession Word Frequency Algorithms for NLP Sentiment Analysis 3. Distance Algorithms used in Text Analytics string Similarity Cosine Similarity Mechanishm - Similarity between Two text documents Levenshtein distance - measuring the difference between two sequences Applications of Levenshtein distance LCS(Longest Common Sequence ) Problems and solutions ,LCS Algorithms 4. Information Retrieval Systems Information Retrieval - Precision,Recall,F- score TF-IDF KNN for document retrieval K-Means for document retrieval Clustering for document retrieval 5. Projects And Case Studies a. Sentiment analysis for twitter, web articles b. Movie Review Prediction c. Summarization of Restaurant Reviews PART H – Introduction To Tableau ( 1 Week : 8 hours) 1. Introduction to Tableau Connecting to data source Creating dashboard pages How to create calculated columns Different charts Hands-on: Hands on on connecting data source and data cleansing Hands on various charts 2. Visual Analytics Getting Started With Visual Analytics Sorting and grouping Working with sets, set action Filters: Ways to filter, Interactive Filters Forecasting and Clustering Hands-on: Hands on deployment of Predictive model in visualisation

16. PART G: HANDLING BIG DATA USING APACHE SPARK AND HADOOP ( 3 Weeks : 24 hours) 1. Introduction To Hadoop : 6 hours Introduction To Hadoop ,Hadoop Architecture HDFS ,Overview of MapReduce Framework Hadoop Master – Slave Architecture MapReduce Architecture Use cases of MapReduce Hands-on: Map reduce Use Case 1 : Youtube data analysis Map reduce use case 2: Uber Data Analytics 2. Apache Spark Analytics : 6 hours What 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 4. NoSQL Databases : 6 hours Topics - What is HBase? HBase Architecture, HBase Components, Storage Model of HBase, HBase vs RDBMS Introduction to Mongo DB, CRUD Advantages of MongoDB over RDBMS Use cases 3. RDBMS And SQL Operations : 6 hours Introduction To RDBMS Single Table Queries - SELECT,WHERE,ORDER BY,Distinct,And ,OR Multiple Table Queries: INNER, SELF, CROSS, and OUTER, Join, Left Join, Right Join, Full Join, Union Advance SQL Operations: Data Aggregations and summarizing the data Ranking Functions: Top-N Analysis Advanced SQL Queries for Analytics TRAINING AND DEPLOYING MACINE LEARNING MODEL USING GCP ( 2 Week : 12 hours) 1. Introduction To GCP Cloud ML Engine Introduction to Google CloudML Engine CloudML Engine in Machine Learning WorkFlow Components of Cloud ML Engine - Google Cloud Platform Console. gcloud command-line tool and Rest API 2. Training Machine Learning Model Developing a training application Packaging a training application Running and monitoring a training job Using hyperparameter tuning Using GPUs for training models in the cloud 2. Deploying Machine Learning Model Deploying Models ,Understanding training graphs and serving graphs ,Check and adjust model size Build an optimal prediction graph Creating input function creating a model version Getting Online Prediction

17.  Real Time Projects Lists Projects From Retail ,Banking ,Finance ,Insurance ,Sales,Marketing ,Healthcare ,Manufacturing Project 1 : Marketing Domain Customer Conversion / Segmentation Problem: A bank Facing Challenges With Lead Conversion Description: Identify the leads' segments having a higher conversion ratio (lead to buying a product) so that organisation can specifically target these potential customers through additional channels and re-marketing Project 2 : Banking Domain Credit Risk Analytics Problem: efficiently build or validate in- house models for credit risk management. Description: Create a classifier that leverages financial information from bank accounts to estimate customer risk. Project 3 : Project on Natural Language Procession Problem : training a machine learning model that classifies a given line of text as belonging to one of the books/Articles. developing a machine learning model (deep learning preferred) for the same. Project 4 : Price Analytics Description: Creating auto calculating pricing model Problem: build an algorithm that automatically suggests the right product prices Project 5 : Classifying Loan Application Problem : Work With credit dataset using classification techniques like Decision Tree, Neural Networks etc to classify loan applications Project 6 : Identify And Predict Customer churn in telecom industry Description:Understand the customer behavior and reasons for churn.Apply multiple classification models to predict the customer churn in telecom industry

18. Project 7 :Retail Domain Coupon Purchase Prediction Project Description:Understand Retail Transactional Data set And Using past purchase and browsing behavior of customers ,create a machine learning model which Predict which coupons a customer will buy in a given period of time. Project 8 : Predicting Demand For Airline Travel Description: preparing Data and Building Your Multilayer Perceptron Model.Training and Testing Your Mode Project 9 : Manufacturing And Production Predict Internal Failures Using Production Line Dataset Description:Understanding about Manufacturing domain and its failures. Use production line dataset to predict internal failures using thousands of measurements/tests made for each component along the assembly line Project 10 : Insurance Purchase Prediction Description:Predicting which insurance option the customer will choose.Building machine learning models and Using a customer’s shopping history, can you predict what policy they will end up choosing? Project 12 : AI Based Live Face Identification System for Crowd Description: Artificial intelligence-based facial recognition systems for security purpose . Track down criminals in crowded place like malls ,airport and other crowded public places Project 11 : Sentiment analysis for twitter, web articles Description: Real-Time Twitter Sentiment Analysis using Naive Bayes classifier in Python

19. Job Readiness Program (15 Hours) Resume Preparation Session (4 Hours) Expert guidance for writing a resume for data scientist Role Preparing Project For interviews( 4 Hours) Will help you to prepare and writing project description in your resume Interview Guidance And Prep Session(6 hours) 6 hours of interview readiness session to help you to prepare for interviews One on One Mock Interviews(1 Hour) 100% Interview Calls guarantee for working professionals (Eligibility : 1.5+ Yrs. of exp in any domain) Still have Queries/Concerns regarding the course,Read FAQ or Contact with our Course Manager/Counsellor Read Course FAQ Live Chat on Whasapp Click Here To Watch Demo/Sample Class Recordings