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Data Science & AI Certification

Domain Specialization

Learnbay offers Industry Accredited Data Science and Artificial Intelligence Certification with DOMAIN ELECTIVE Option.Program for professionals with 1+ years of experience in any domain.

Course Fee

65000
7.5 months (200+ hrs)
This course includes:

Domain Elective Specialization

12+ Real World Industry Project

From Basics to Advance

 Guaranteed Job Referrals

 Special Classes for non-programmers

Live Faculty-led Online Training

One Year Flexible Subscription

Eligibility

1+ yrs work exp.

Certification

Accredited with IBM

Projects

12+ Real Time Projects

No Cost EMI

₹ 7k/mon* (9 months)

ABOUT COURSE
About This Program

Eligibility

Best suitable for working professional with 1+ yrs of work exp. in any domain looking to start their career in data science.

Job Assistance

Resume sessions, Mock interviews and job referrals to land your dream job or data science and ML engineer.

Live Projects

12+ Real-time projects from multiple domains like healthcare, BFSI, retail, manufacturing, e-commerce, and marketing.

Data Science & AI Certification

Domain Specialization For Professionals

Learnbay offers Industry Accredited Data Science and Artificial Intelligence Certification with DOMAIN ELECTIVE Option.This foundation program is suitable for professionals having more than 1+ years of work experience in any programming or non programming domain.

Course Fee

65000
7.5 months (200+ hrs)
This course includes:

Domain Elective Specialization

12+ Real World Industry Project

From Basics to Advance

 Guaranteed Job Referrals

 Special Classes for non-programmers

Live Faculty-led Online Training

One Year Flexible Subscription

Eligibility

1+ yrs work exp.

Certification

Accredited with IBM

Projects

12+ Real Time Projects

No Cost EMI

₹ 7k/mon* (9 months)

ABOUT COURSE
About This Program

Eligibility

Best suitable for working professional with 1+ yrs of work exp. in any domain looking to start their career in data science.

Job Assistance

Resume sessions, Mock interviews and job referrals to land your dream job or data science and ML engineer.

Live Projects

12+ Real-time projects from multiple domains like healthcare, BFSI, retail, manufacturing, e-commerce, and marketing.
Key Highlights
Program Overview

  Designed for Working Professionals

  Real-Time Use Cases from Multiple Domains

  Industrial Certificate which is accredited with IBM

  One-on-One with Industry Mentors

  Guaranteed Job Referrals For Working Professionals

  Interview Prep Session And Mock Interview

  Industry Accredited Global Certification Course

  12+ Industry Projects & 2 capstone

  Special Classes For Non-Programming Background

   Interest Free Loan and No Cost EMI on Credit cards

Elective Details
Domain Specialization elective
Key Highlights
Program Overview

  Designed for Working Professionals

  Real-Time Use Cases from Multiple Domains

  Industrial Certificate which is accredited with IBM

  One-on-One with Industry Mentors

  Guaranteed Job Referrals For Working Professionals

  Interview Prep Session And Mock Interview

  Industry Accredited Global Certification Course

  12+ Industry Projects & 2 capstone

  Special Classes For Non-Programming Background

   Interest Free Loan and No Cost EMI on Credit cards

Elective Details
Domain Specialization elective
MODULES
Course Modules

Python for Data Science

Statistics Mathematics

Machine Learning

Deep Learning

NLP / Text Analytics

Computer Vision

Project Management

R Programming

Domain Training

Google Cloud

mySQL

Mongo DB

Power BI

Tableau

Apache Spark

Reinforcement Learning

Real Time Projects

AI/ML Project Management

MODULES
Course Modules

Python for Data Science

Statistics Mathematics

Machine Learning

Deep Learning

NLP / Text Analytics

Computer Vision

Project Management

R Programming

Domain Training

Google Cloud

mySQL

Mongo DB

Power BI

Tableau

Apache Spark

Reinforcement Learning

Real Time Projects

AI/ML Project Management

TOP SKILLS
Top Skills You Will Learn

Top Skills You Will Learn

Python for data science, Statistics, Machine Learning, Tensorflow, NLP, SQL, Tableau, MongoDB, PowerBI, R, Time Series Analysis, and Forecasting, Github, Cloud Deployment using AWS.

Job Opportunities

Data Analyst, Data Scientist, Data Engineer, Product Analyst, Machine Learning Engineer, Decision Scientist.

Details About Real Time Projects.

2 Capstone Project and 12+ Real time projects from multiple domains help you to showcase practical skills to recruiter and get your dream job.

Minimum Eligibility

Best for Professionals from non-programming background having 1+ yrs of work exp. If you are from coding background, check our AI & ML Program for Coders.
TOP SKILLS
Top Skills You Will Learn

Top Skills You Will Learn

Python for data science, Statistics, Machine Learning, Tensorflow, NLP, SQL, Tableau, MongoDB, PowerBI, R, Time Series Analysis, and Forecasting, Github, Cloud Deployment using AWS.

Job Opportunities

Data Analyst, Data Scientist, Data Engineer, Product Analyst, Machine Learning Engineer, Decision Scientist.

Details About Real Time Projects.

2 Capstone Project and 12+ Real time projects from multiple domains help you to showcase practical skills to recruiter and get your dream job.

Minimum Eligibility

Best for Professionals from non-programming background having 1+ yrs of work exp. If you are from coding background, check our AI & ML Program for Coders.

Download Program Brochure

FEATURES
Course Features
Program Eligibility

Working Professionals With 1+ Years of work experience in any domain , preferred is non programming/non-tech domain. We have a different course for Programmers & Core tech background professionals. (AI and ML).

To know which Program is best suitable for your profile, Apply for Career counselling   session with our expert.

Course Prerequisite

There is no Prerequisite for this course as we cover programming and statistics from basics. We provide special classes & support for professionals from non-programming/non-technical backgrounds.

Special Support to Non Programmers

we provide special Python programming fundamentals classes designed for non-programmers and non-technical domain professionals which helps you to learn programming concepts easily.

Who Should Apply

Software developers/Programmers, Project Managers, Manual And Automation Test Engineer, Java and .net Developer, Informatica, Business Analyst.

Database Admin, System Admin, Professionals from Sales, Marketing, Operations.SAP domain expert, Python , Embedded developer , Android/ios developer.

To know which Program is best suitable for your profile, Apply for Career counselling session with our expert.

About Instructors
  • Our instructors are working professionals who graduated from premier institutes like BITS Pilani, IIT Roorkee, and working in companies as Data Scientist/Machine Learning Engineer and Artificial Intelligence expert.
  • Students will be guided by our mentors until the end of a course. A student can seek help by our mentors even after course completion for knowledge and placement purposes.
Fees and Duration
Weekday Program Batches: 6 Months

Monday – Friday – 2 hours every day

Weekend Batches: 7.5 Months

Saturday & Sunday – 4 hours every day

Program Fee: Rs. 65,000/-

INTEREST-FREE INSTANT LOAN WITHOUT CREDIT CARD

NO COST EMI ON MAJOR CREDIT CARDS

Hybrid Model Classes

Attain 2 Capstone & 12+ Real Time AI and ML Projects in Classroom-mode OR Live-online led by industrial experts.
Locations: Pune, Mumbai, Delhi, Hyderabad, Chennai, Bengaluru

It is completely optional to attain project sessions in classroom. You can also prefer Live-Online Interactive Mode guided by Industrial Expert.

Domain Specialization

With domain expertise you will have a deeper understanding of the subject than anyone else in your company. Become well-versed and understand the best practices in your respective fields. Be aware of the issues you and your company may encounter in the future. Most crucially, a well-known Domain Specialist boosts company’s market value.
Domain Specialization will help you and your company in following ways:

Formulating Problem,Data Collection, Data Pre-processing, Feature Engineering,
Model Creation, Enhancement and Deployment

COURSE
Syllabus

Introduction to Programming 3hr

What is a programming language ? Source code Vs bytecode Vs machine code
Compiler Vs Interpreter ,C/C++, Java Vs Python

Jupyter notebook basics 1hr

Different type of code editors in python introduction to Anaconda and
jupyter notebookFlavours of python.

Python Programming Basics 2hr

Variable Vs identifiers Vs strings, Operators Vs operand Procedure
oriented Vs modular programming

Statistics basics 2hr

Introduction to statisticsMean,median, mode, Standard deviation,
AverageIntroduction to probability,permutations and
combinationsIntroduction to linear Algebra

Git and GitHub 2hr

Learn the key concepts of the Git source control system,Configure SSH for authentication,Create and use a remote repository on GitHub,Git Overview

Tools Covered

Programming Basics &Environment Setup hr

Installing Anaconda, Anaconda, Basics, and Introduction, Get familiar with version control, Git, and GitHub.Intro to Jupyter Notebook environment. Basics Jupyter notebook Commands.Programming language basics.

Python Programming Overview hr

Python 2.7 vs Python 3,Writing your First Python Program
Lines and Indentation,Python Identifiers,Various Operators and Operators
Precedence ,Getting input from User,Comments,Multi line Comments.

Strings, Decisions And Loop
Control
hr

Working With Numbers, Booleans and Strings,String types and
formatting, String operations Simple if Statement, if-else Statement
if-elif Statement. Introduction to while Loops. Introduction to for Loops,Using
continue and break.

Python Data Types hr

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

Functions And Modules hr

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

File I/O And Exceptional Handling and Regular Expression hr

Opening and Closing Files,open Function,file Object Attributes
close() Method ,Read,write,seek.Exception Handling, try-finally Clause
Raising Exceptions, User-Defined Exceptions. Regular Expression- Search
and Replace. Regular Expression Modifiers. Regular Expression Patterns,re module.

Data Analysis Using Numpy And
Pandas
hr

Introduction to Numpy. Array Creation, Printing Arrays, Indexing, Slicing and Iterating, Shape Manipulation -Changing the shape, stacking, and splitting of the array. Vector stacking, Broadcasting.

Pandas : 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.

Data Visualisation using Python:
Matplotlib and Seaborn
hr

Matplotlib: Introduction,plot(),Controlling Line Properties,Subplot with Functional Method, MUltiple Plot, Working with Multiple Figures,Histograms

Seaborn :Intro to Seaborn And Visualizing, statistical relationships , Import and Prepare data.Plotting with categorical data and Visualizing linear relationships Seaborn Exercise

Tools Covered

Fundamentals of Math and
Probability
hr

Basic understanding of linear algebra,Matrics, vectors. Addition and
Multimplication of matrics.Fundamentals of Probability
Probability distributed function and cumulative distributed function.

Descriptive Statistics hr

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.

Inferential Statistics hr

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 yesting,

Hypothesis Testing hr

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. The Chi-Square Test of, Independence.Regression. Factorial Analysis of
Variance. Pearson Correlation, Coefficients in Depth. Statistical,Significance, Effect Size

Data Processing & Exploratory
Data Analysis
hr

What is Data Wrangling, Data Pre-processing and cleaning?
How to Restructure the data? What is Data Integration and Transformation

Introduction To Machine Learning 8hr

What is Machine Learning? What is the Challenge?
Introduction to Supervised Learning, Introduction to Unsupervised
Learning, What is Reinforcement Learning? Machine Learning applications
Difference between Machine Learning and Deep Learning

Supervised Learning 8hr

Support Vector Machines,Linear regression,Logistic regression
Naive Bayes, Linear discriminant analysis, Decision tree
the k-nearest neighbor algorithm, Neural Networks (Multilayer perception) Similarity learning

Linear Regression 8hr

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

Logistic Regression hr

Introduction to Logistic Regression.–Why Logistic Regression .
Introduce the notion of classification, The 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.

Decision Trees hr

Decision Tree – data set How to build decision tree? Understanding Kart Model
Classification Rules- Overfitting Problem. Stopping Criteria And Pruning. How to Find the final size of Trees? Model A decision Tree.
Naive Bayes. Random Forests and Support Vector Machines.Interpretation of Model Outputs

Unsupervised Learning hr

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

Natural language Processing hr

Introduction to natural Language Processing(NLP).
Word Frequency Algorithms for NLP Sentiment Analysis

Case Study :Twitter data analysis using NLP

Introduction to Time Series Forecasting hr

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

ARIMA and Multivariate Time
Series Analysis
hr

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

Introduction to Deep Learning And Tensor Flow hr

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

Introduction to Tensor Flow hr

Installing TensorFlow Simple Computation,Contents And Variables Types of file formats in TensorFlow Creatting A Graph – Graph Visualization Creating a Model – Logistic Regression Model Building TensorFlow Classification Examples

Understanding Neural Networks With Tensor Flow hr

Basic Neural Network, Single Hidden Layer Model,Multiple Hidden Layer Model,Backpropagation – Learning,Algorithm and visual representation,
Understand Backpropagation – Using Neural Network Example TensorBoard
Project on backpropagation

Convolutional Neural Network(CNN) hr

Convolutional Layer Motivation ,Convolutional Layer Application,
The architecture of a CNN, Pooling Layer Application Deep CNN, Understanding and Visualizing a CNN,Project: Building a CNN for Image Classification

Introduction to NLP & Text Analytics hr

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

Text Pre Processing Techniques hr

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

Distance Algorithms used in Text Analytics hr

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

Information Retrieval Systems hr

Information Retrieval -Precision,Recall,F- score,TF-IDF,KNN for document retrieval,K-Means for document retrieval,Clustering for document retrieval

Topic Modelling & Dirchlett Distributions hr

Introduction to Topic Modelling,Latent Dirchlett Allocation,Adavanced Text Analytics & NLP,Introduction to Natural Language,Toolkit,POS Tagging NER

Projects And Case Studies hr

a. Sentiment analysis for twitter, web
articles
b. Movie Review Prediction
c. Summarization of Restaurant
Reviews

RDBMS And SQL Operations hr

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

NoSQL Databases hr

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

Programming with SQL hr

Mathematical Functions,Variables,Conditional Logic Loops,Custom Functions
Grouping and Ordering, Partitioning, Filtering Data, Subqueries

MongoDB Overview hr

Where MongoDB is used? MongoDB Structures,MongoDB Shell vs MongoDB Server
Data Formats in MongoDB,MongoDB Aggregation Framework,Aggregating Documents
What are MongoDB Drivers?

Basics and CRUD Operation hr

Databases, Collection & Documents,Shell & MongoDB drivers
What is JSON Data Create, Read, Update, Delete Finding, Deleting, Updating,
Inserting Elements Working with Arrays Understanding Schemas and Relations

Introduction to MongoDB hr

What is MongoDB?Characteristics and Features,MongoDB Ecosystem,Installation process Connecting to MongoDB database,Introduction to NoSQL
Introduction of MongoDB module,What are ObjectIds in MongoDb

Introduction to Tableau hr

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

Visual Analytics hr

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 the deployment of Predictive
model in visualization

Dashboard and Stories hr

Working in Views with Dashboards and Stories,Working with Sheets
Fitting Sheets, Legends and Quick Filters, Tiled and Floating Layout Floating Objects

Mapping hr

Coordinate points,Plotting Latitude and Longitude
Custom Geocoding,Polygon Maps,WMS and Background Image

Getting Started With Power BI hr

Installing Power BI Desktop and Connecting to Data,Overview of the Workflow in Power BI Desktop,Introducing the Different Views of the Data Mode Query Editor Interface Working on Data Model

Programming with Power BI hr

Working with Timeseries,Understanding aggregation and granularity
Filters and Slicers in Power BI Maps, Scatterplots and BI Reports
Connecting Dataset with Power BI Creating a Customer Segmentation
Dashboard Analyzing the Customer Segmentation Dashboard

Introduction To Hadoop hr

Distributed Architecture – A Brief Overview Understanding Big Data,
Introduction To Hadoop ,Hadoop,Architecture,HDFS ,Overview of MapReduce
Framework,Hadoop Master – Slave Architecture,MapReduce Architecture
Use cases of MapReduce

Apache Spark Analytics hr

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
Using R-Spark for machine learning
programming

Apache Spark Analytics hr

Getting to know PySpark,Pyspark Introduction,Pyspark Environment Setup
pySpark – Spark context,RDD , Broadcast and Accumulator,Sparkconf and Sparkfiles,Spark MLlib Overview,Algorithms and utilities in Spark
Mlib

Case Study hr

Hands-on:
Map-reduce Use Case 1: Youtube
data analysis
Map-reduce Use Case 2: Uber Data
Analytics
Hands-on: Spark RDD programming
Hands-on: Spark SQL and Dataframe programming

Introduction To R hr

Installation Setup Quick guide to RStudio User Interface
RStudio’s GUI3 Changing the appearance in RStudio
Installing packages in R and using the library Development Environment Overview
Introduction to R basics Building blocks of R Core programming principles
Fundamentals of R

Programming with R hr

Creating an object,Data types in R,Coercion rules in R
Functions and arguments, Matrices, Data Frame, Data Inputs and Outputs with R, Vectors and Vector operation Advanced Visualization, Using the script vs. using the console

Manipulating Data hr

Data transformation with R – the Dplyr package – Part
Data transformation with R – the Dplyr package – Part
Sampling data with the Dplyr package,Using the pipe operator in R
Tidying data in R – gather() and separate(),Tidying data in R – unite() and
spread()

Visualizing Data hr

Intro to data visualization,Introduction to ggplot2
Building a histogram with ggplot2, Building a bar chart with ggplot2
Building a box and whiskers plot with ggplot2, Building a scatterplot with ggplot2

Introduction To GCP Cloud ML Engine hr

Introduction to Google CloudML Engine,CloudML Engine in Machine,Learning WorkFlow,+3Components of Cloud ML Engine -Google Cloud Platform Console.
gcloud command-line tool and Rest API
API

Training Machine Learning
Model
hr

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

Project : Loan Default Prediction

Domain – Banking & Finance

DataSet : Banking Data

The bank wants to improve its services by finding interesting
groups of clients. Fortunately, the bank stores data about their clients, the accounts (transactions within several months), the loans already granted, the credit cards issued. This process of loan default prediction can be done with
machine learning algorithms.

Project : Clustering Customers
Domain – Retail industry
DataSet : BigBazar/Future Group

Big Bazaar has retail outlets across major metropolitan cities in India. With the help of machine learning algorithms, we can better understand customer behavior and understand their buying needs better.BigBazaar runs various loyalty
programs, festive offers which provide their customer more opportunities to
avail discounts.

Project : IBM HR Analytics
Domain – Demand/Supply
DataSet : IBM

Applying analytic processes to the human resource department of an
organization in the hope of improving employee performance and therefore getting a better return on investment.This is especially concerning if your business is customer facing, as customers often prefer to interact with familiar people.

Project : Forecasting Uber Demand
Domain – Demand/Supply
DataSet : Uber & Rapido

The goal is to create an interactive dashboard using Tableau This Tableau Dashboard can be used to get historical insights into a neighborhood,For example,see its upcoming forecasted demand,increase the accuracy,decrease surge pricing events.

Project : Analyzing Health Data and tracking human activity
Domain – Healthcare
DataSet : Samsung

The goal is to breakdown all the data that the Samsung Health
app has collected and see what useful insights we can gain by analyzing it.

Project : Identify fraudulent credit card transactions.
Domain – Banking & Finance
DataSet :Banking Dataset

To recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase.It involves various processes like
Data Cleaning, Data Visualization, Insights generation, Model generation, Feature Engineering and so on.

Project : Consumer Reviews of Amazon Products
Domain – E-Commerce
DataSet :Amazon Data

The goal is to analyze Amazon’s most successful consumer electronics
product launches; discover insights into consumer reviews and assist with
machine learning models.What are the most reviewed Amazon products?
How do the reviews in the first 90 days after a product launch?

Project :Airbnb New User Bookings
Domain – Travel & Hospitality
DataSet :Airbnb

The goal is to predict which country a new user’s first booking destination
will be.By accurately predicting where a new user will book their first travel
experience, Airbnb can share more personalized content with their community, decrease the average time to first booking, and better forecast demand.

Project : Netflix Movies and TV Shows
Domain – Media and Entertainment
DataSet :Netflix

Explore what all other insights can be obtained from the list of tv shows and movies available on Netflix as of 2019. Understanding what content is
available in different countries Identifying similar content by matching text-based features Network analysis of Actors / Directors and find interesting insights.

Project : Walmart Sales Forecasting
Domain – Retail
DataSet :Walmart

This dataset contains the sales for each department from Walmart
a dataset containing data of 45 Walmart stores, selected holiday markdown events are also included. These markdowns are known to affect sales, but it is challenging to predict which departments are affected and the extent of the impact.

Project : -BMW Pricing Challenge
Domain – Automation
DataSet :BMW dataset

To find a good statistical model to describe the value of a used car
depending on the basic description How does the estimated value of a car change over time? Can you detect any patterns? How big is the influence of the
factors not represented in the data on the price?

Project : -Bosch Production Line Performance
Domain – Manufacturing
DataSet :Bosch

To predict internal failures using thousands of measurements and tests made for each component along the assembly line. This would enable Bosch to bring quality products at lower costs to the end user. The goal is to predict which parts will fail quality control

Project : -Smart Supply Chain for Big Data Analysis
Domain – Supply Chain
DataSet :Dataco

A DataSet of Supply Chains used by the company DataCo Global is used
for the analysis. Dataset of Supply Chain, which allows the use of
Machine Learning Algorithms and R Software.It also allows the correlation of
Structured Data with Unstructured Data for knowledge generation.

Project : -Trending YouTube Video Statistics
Domain – Social Media
DataSet :youtube

The dataset of this project are daily record of the top trending YouTube
videos, to generate insights like : Sentiment analysis in a variety of
forms Categorising YouTube videos based on their comments and statistics
Training ML algorithms like RNNs to generate their own YouTube comments.

Project : -Identify And Predict Customer churn in telecom industry
Domain – Telecom
DataSet :Telecom

The goal is to develop a churn prediction model that assists telecom operators to predict customers who are most likely subject to churn.Also to
understand the customer behavior and reasons for churn.Apply multiple
classification models to predict the customer churn in the telecom industry.

200+

Content Hours Available

12+

Industry Projects

20+

Live Learning Sessions

25

Expert Coaching Sessions

14

Programming Tools/ Languages
Program Schedule
Data Science & AI Certification Program

Module 0: Special Sessions For Non-Programmers

 Time duration: 4 days (10 hours)
 One Week before your Batch Starts

We believe that professionals coming from non-programming backgrounds need special assistance and support. we provide special programming fundamentals classes designed for non programmers and non technical domain professionals which helps you to learn programming concepts easily.

Term 1: Python For Data Science

 Time duration: 4 days (10 hours)
 1.5 Months

Python is one of the most commonly used programming languages in the field, in this section, we start Basic python & Environment Setup and advanced Python scripts such as List, Tuples, Dictionaries, File Operations, Regular Expressions, dealing with binary data, and using the extensive Python module features. Python libraries and tools,Numpy ,Pandas,Matplotlib and Seaborn etc

Term 2: Statistics And Machine Learning

 Time duration: 9 Weeks (72 hours)
 2 Months

Statistics are the use of mathematics to conduct technical predictive analytics, IN statistics covering math and probability fundamentals, hypothesis testing, data processing, and exploratory data analysis, etc. Machine learning curriculum that will provide you to learn techniques such as supervised learning, unsupervised learning, and natural language processing, etc.

Term 3: TensorFlow, Deep Learning & Natural Language Processing

 Time duration: 5 Weeks ( 40 hours)
 1 Months

TensorFlow is a platform built by Google. Deep Learning is a machine learning category. TensorFlow and deep learning cover topics such as Neural Network Model, Graph Visualization, Logistic Regression, CNN Architecture, Deep CNN Pooling Layer Application, etc.

Term 4: SQL,MongoDB,Tableau,Power BI,Big Data ,Spark Analytics,R And GCP

 Time duration:9 Weeks (72 hours)
 2 Months

Power BI and Tableau is the data visualization and business intelligence tool. Big data is the data sets that are so voluminous and complex that traditional data processing application software is inadequate to deal with them. Topics in Big data you must cover that is- Hadoop, MapReduce, Hive, Sqoop, Spark e.t.c.

Projects And Job Assistance

 Integrated with the curriculum
 When: After every module

Candidates Effective completion of Term 1, Term 2, and Term 3, become eligible for the Career Assistance Program, In this programmer we have offered Interview Prep Session & Mock Interview, Participating in Live Kaggle Competitions. Guaranteed Career References for Data Science/ML positions of an engineer.
Take part in guided workshops from various domains for real-time projects and get project support/mentorship from expert instructors.

SAMPLE CERTIFICATE

Global Accreditation and Certification

In Collaboration With IBM

Our Data Science & AI Certification Program helps you to start your career in data science and AI.

Enhancing your profile

Add this globally recognized certificate to uplift your profile and stand out of the crowd.

Real Time Project & Job Assurance

Course features 12+ Real time industry projects and Job Assurance program.
SAMPLE CERTIFICATE

Global Accreditation and Certification

In Collaboration With IBM

Our Data Science & AI Certification Program helps you to start your career in data science and AI.

Enhancing your profile

Add this globally recognized certificate to uplift your profile and stand out of the crowd.

Real Time Project & Job Assurance

Course features 12+ Real time industry projects which can be added as a learning experience in your profile with 100% Interview Guarantee.
TRANSITION STEPS

Learning Track

Step 1

Learn Full Stack DS & AI
Learn basics Python, Statistics, ML, NLP , DL, SQL and other modules in depth with real time case studies.

Step 2

Choose domain & get trained
You can select multiple domains and get trained based on your work experience, academics and career goal.

Step 3

Work on real time projects
Work on multiple real time industry projects and capstone from multiple domains with mentorship of our expert.

Step 4

Resume Prep & Mock Interview Session
Update your resume and practice mock interviews. Get 100% guaranteed job referrals for data scientist & ML Engineer.
DATA SCIENCE
Real Time Projects
DATA SCIENCE
Real Time Projects
FAQs
Frequently Asked Questions

What are the Prerequisites for data science certification Course?

There are no prerequisites for this course as all the modules are covered from basics and scratch. If you are from a non-technical and non-programming background, We provide special classes and assistance so that you can learn to program.

What if i miss few classes?What is flexi pass Option ?

In case you miss classes, You will be provided backup classes in other batches. If you miss more classes, You can request for batch change and join our next batch. To solve this issue, We provide a flexi-pass option for our data science course.

Those who enroll for live classroom training are eligible for a Flexi-pass of 6 months. With this option, We share your access to all ongoing batch details for a period of 6 months, so that you can attend live classroom sessions from any batches and learn at your own pace. This option also helps you to finish the course early as you can attend multiple batches simultaneously.
This option is best for someone working in shifts or needs to work on weekends sometimes.

What is the duration of this Course?

Duration Of this course is around 6 months, weekends only (4 hours on Sat and Sunday)
Course Duration: 200 hours (Approx)

The total duration of this course is 6 months but after 4 Months( once50% Of Machine learning is over) you can start working on Real-Time Projects and attend job interviews.

What is the mode of training?

We provide both options i.e Classroom training( in Marathahalli – Bangalore ) and instructor-led live training option. Based on your requirement and need, you can choose training mode.

How long will i get support for Job Assistance?

For 1 Year (After your course completion)

What is Job Readiness Program?

Job Readiness program helps you to prepare for data science interviews.

Resume Preparation Session: Expert guidance for writing a resume for data scientist Role

Preparing Project For interviews: Will help you to prepare and write a project description in your resume

Interview Guidance And Prep Session: 6 hours of interview readiness session to help you to prepare for interviews

One on One Mock Interviews

What is the eligibility for job assistance program?

You should have completed the course with

    • A minimum score of 70% in Assessment
    • Should have completed Assignments and case studies
    • Worked and successfully completed a min. the recommended number of real-time projects

Will i get referral for jobs?

Yes,We provide placement Assistance by referring your profile through our partnered consultancies and companies for relevant positions(Data Science/Data Analyst Roles).

Contact course manager/Counsellor to know the eligibility of job assistance program

Note: We don’t provide any 100% Job Guarantee

Can i Pay in installment?

There ai 0% EMI Available on Major credit cards.Please check with our sales team to know how 0% EMI on credit cards works.

Is there any discounts available ?What about group discounts?

We have promotions and early bird offer from time to time. Please check on the website or talk to our sales team to know more about the applicable discounts.

Group Discount: Group discount is applicable if you are joining with your friends.

  • 5% extra discount: Group of 2
  • 8% extra discount: Group of 3
  • Max 10% group discount: Group of 4+ members

How many minimum number of Projects should i work?

Number of Projects depends on your overall industry experience.Please find the recommended number of projects With industry experience.
2 yrs to 5 Yrs : 3 Projects
5 to 8 yrs: 5 Projects
8 yrs to 12 yrs: 7 + Projects
For your knowledge and practice, You can work on all the projects with us.
Contact our course expert to know more details
Note: Projects are guided by our expert industry mentors.

How many Real Time Projects will be covered in this Course?

There is a set of  12 Real-time projects from multiple domains like Banking, Finance, Insurance, Retail, Healthcare, and manufacturing. Based on your interest and domain, You can choose the project to work on.

Click here to download the list of project

How Projects Will be executed?

Your Project group will be assigned a project mentor and will be given the chance to work on the project With guidance and Assistance.

About our mentors

We strive to deliver the best quality training as it is one of our serious priorities, all our mentors are field professionals and have at least 8+ years of experience in the respective field.

Until when the students gets the facility of mentorship?

Students will be guided by our mentors until the end of a course. A student can seek help from our mentors even after course completion for knowledge and placement purposes.

Can a student choose to study from their desired mentor?

Every batches has respective mentors but if a student is not satisfied with any mentor’s teaching method they can raise the issue with the management and get to facility to choose their mentor.

Project mentors

We have specified mentors for teaching and training purposes, the mentors you seek during project sessions are professionals at carrying out the project courses.

How long a student will be supported for career and job opportunities?

Students will be supported by the management in fetching job opportunities starting from the end term of the course. If any student doesn’t succeed in fetching a job they will be supported by the management for an entire 1 Year after the completion of the course.

What is the eligibility for a job assistance program?

A student will be eligible for job assistance only if they have:

  1. Minimum score of 70% in the assessment
  2. Completed the assignments and case studies
  3. Worked on and successfully completed the minimum recommended number of real-time projects

Does students get a referral for jobs?

Yes, we run Placement Assistance in where we refer eligible student’s profiles to different IT companies through our partnered consultancies and companies for relevant positions (Data Science/Data Analyst Roles).
For further information, you may contact the course manager/Counselor to know the eligibility of the job assistance program
Note: We don’t provide 100% Job Guarantee
SCHEDULE ONE ON ONE PROFILE REVIEW SESSION
Career Counselling,
From Domain Expert
Understand your personalised transition into data science from your current domain.
Know if your prior experience will be considered relevant for your career in data science.
Profile review will help you to resolve your queries about real time projects, job assistance, course modules etc.
Call Us +91 77956 87988 or

Data Science Certification & AI Course

65,000.00

IBM certified Data Science Course For Working Professional to Change Your Existing Domain and start your career in data science.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.

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Additional information

Select Batch

Weekday Batch: 30th September, Thu,7.30 PM To 9.30 PM, Weekend Batch : 3rd October, Sun,12.30 PM To 4 PM

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Note : Our programs are suitable for working professionals(any domain). Fresh graduates are not eligible.
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GET THE LEARNBAY ADVANTAGE FOR YOUR CAREER
Note : Our programs are suitable for working professionals(any domain). Fresh graduates are not eligible.