Working Professionals With 1+ Years of experience in any domain (tech or non technical)
BE/B.Tech (from any branch) , BBA/MBA, MCA/M.Tech, B.Com, Graduation in Mathematics, Statistics, IT
To know which Program is best suitable for your profile, Apply for Career counselling session with our expert.
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 background.
we provide special Python programming fundamentals classes designed for non-programmers and non-technical domain professionals which helps you to learn programming concepts easily.
Monday – Friday – 2 hours every day
Saturday & Sunday – 3.5 hours every day
INTEREST-FREE INSTANT LOAN WITHOUT CREDIT CARD
NO COST EMI ON MAJOR CREDIT CARDS
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
You can attain project session in live online mode or in classroom mode (Bangalore HSR,layout) guided by by Industrial Expert.
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
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
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 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
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 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
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.
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.
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
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.
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.
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.
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.
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.
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.
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
Contact course manager/Counsellor to know the eligibility of job assistance program
Note: We don’t provide any 100% Job Guarantee
Group Discount: Group discount is applicable if you are joining with your friends.
Click here to download the list of project
A wide range of subjects can be learned in the Data Science & AI Certification courses. The five stages of the Data science lifecycle are covered, including data engineering, math, statistics, advanced computing, and visualizations. Fresh graduates from any field (science, commerce, or arts) can enroll in the domain specialization for professionals course to learn everything from the ashes of the old.
During our Data Science & AI Certification course, we teach Machine Learning, NLP, TensorFlow, etc
Data science is increasingly used to measure success and plan future goals. It involves gathering and analyzing data from relevant sources. Companies nowadays heavily rely on data science.
Role of a Data Scientist in Bangalore?
Data scientists use technical, social, and data science knowledge to analyze data. They solve problems using industry knowledge, context awareness, and the idea that established assumptions may be inaccurate.
Data scientists are hired for jobs such as:
Data science jobs are in high demand all around the world and across many industries.
Eligible candidates have 1+ years of work experience in non-programming/non-tech and programming fields. We have separate courses for programmers and core tech experts. (AI/ML)
The Data Science & AI Certification course: New graduates can take a foundation or domain specialization for professionals from any stream (science, commerce, or arts). Weekend classes are open to all college students.
We let you pick your courses and data, science mentors. We are devoted to producing high-quality data scientists for all industries in the next few years.
Why Should One Take The Data Science & AI Certification Course?
While there are numerous reasons why this could be the perfect career for you, let’s focus on the benefits.
Learnbay provides data Science Training in Bangalore. The program offers Data Science & AI Certification in partnership with IBM, based in New York.
Following simple methods can get you Data Science & AI Certification online training in Bangalore. Get certified in data science in Bangalore by consistently passing exams. Receive Job Assistance and get fantastic job opportunities in Bangalore and India.
Benefits of taking a Data Science course?
It trains students for the increased demand for Big Data skills. It includes Hadoop, R, Flume, Sqoop, Machine Learning, Mahout, etc. Here is a list of benefits of taking a data science & AI Certification course:
The Data Science & AI certification course includes powerful machine learning and AI. Learnbay has developed AI methods. Our Data Scientists in Bangalore have developed the following core competencies.
Python, Machine Learning, Natural language processing, TensorFlow, Deep Learning, SQL and MongoDB, Tableau and PowerBI, and R are among our Data Scientists’ languages of expertise. Data Science demands good coding skills. So does our curriculum.
Another data science skill is data preparation and storage. We need a data scientist who can build, extract, transform, and store data pipelines. Learnbay industrial projects help master data engineering abilities.
Data science is primarily analytic. Data collection takes time, but finding valuable patterns takes even longer—data visualization in histograms, box plots, heat maps, etc. Only a data whiz with analytical skills can do this. Data visualization training is part of Learnbay’s data science course. Develop Business Analytics skills to specialize.
A data scientist’s primary role is to build data models. Machine learning courses in Bangalore should enable a data scientist to use machine learning and AI approaches. The programs can help them spot patterns in data. Modeling requires significant testing.
After constructing a machine learning model, it must be tested. Few factors must be considered before implementing a machine learning model. Flexibility is a performance parameter.
It often incorporates software security. Machine learning models require training. Learnbay’s data science courses provide these skills at a minimal cost.
Learnbay provides job assistance and practice interviews for budding Data Science Certification Course. We assist candidates with resume writing, interview preparation, and job profile selection in Bangalore. Our strong ties with MNCs and IT firms help with bulk placements. So just relax, take classes, and let us do our thing!
How do I enroll for the Data Science Certification Course?
Get enrolled in the Data Science Certification Course or Data science online training in Bangalore by contacting our counseling team. Pay the Data Science course fees with 0% EMI on Major credit cards. Please check with our sales team to know how 0% EMI on credit cards works.
Data science course training fees in Bangalore are lowered with our promotions and early bird offer from time to time. The amazing applicable discounts offered to Data Science enthusiasts are as follows:
Group Discount: Group discount is applicable if you are joining with your friends.
Yes, the Data Science Certification Course offered by Learnbay data science institute is suitable for freshers, college-going students, managers, and working professionals. At Learnbay, we provide foundation courses and fresher programs. We offer a range of specialized courses for different groups of learners. You can visit our website for course descriptions and data science projects for each program in detail.
Learnbay data science Institute provides Data Science Certification Course along with a unique feature of job assistance. We help data science enthusiasts to get through job interviews and resume preparation. Our strong relations with corporates and the IT industry helps in bulk data scientist placements. We ensure to support candidates after the course completion. Career counselling and interview preparation are our primary concerns for every candidate. Getting placed in a firm depends on the ability of the candidate to crack mock interviews and obtain good scores during regular assessments.
Learnbay is an ideal Data Science Certification Course. We are one of the best data science learning institutes in metropolitan cities such as Bangalore. The primary focus of Learnbay is to:
IBM and Learnbay have joined hands to provide an intensive data science learning course to aspiring data scientists in Bangalore and other parts of the country. IBM and Learnbay work together to fulfill the following goals:
The data science courses by Learnbay are conducted through both online mode and classroom mode. The live data science course online classes are accessible to our online learners. The full-time or classroom mode classes are accessible in our institute premises on the 2nd Floor, Classic Aura, Service Rd, above Hyderabadi biryani house, Kadubeesanahalli, Bengaluru Karnataka.
The duration of this Data Science Certification Course varies depending on the type of Data Science course one chooses. The maximum period of a data science course is six months to 12 months. It includes live lectures and hands-on practical training on industrial projects. The classes are accessible even after a year of course completion.
Yes, you can pursue a data science course in Bangalore after graduation. You can choose the foundation program or the freshers program depending on your needs. We also have a unique course in data science for professionals
The eligibility for the Data Science Certification Course are:
₹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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