Data Science Training in Bangalore

We’re leading Data Science training institute in Bangalore, offering advanced data science course to all our students who are hunger to explore the technology under Certified Industry Experts.

Are you the one who is hunting for the best data science training institutes?


The one who is locally searching for a training institute?

Don’t just hunt for the best training place hunt for the Data Science training institute in Bangalore who can share advanced concepts of Data Science , Certification , skills, Online Training & 100% placement assistance.

Best Data Science training in Bangalore

As a result of our Data Science Training Institute in Bangalore offering Flexible Training facilities to all of our students and technology enthusiasts, so one can take the Data Science training without worrying about the Timings. Therefore we designed data science training course in Bangalore, Structure based on the current IT Industry standards & trends which are gaining huge demand. As a result, we understand the Gap between IT Industry and student skill, so we tailored Our Data science Training on the basis of the current trend which is helping data scientist enthusiasts to crack any kind of challenges in an easier manner.

Then what are you waiting for Enroll now for our Data science Training in Bangalore and get the training under 20+ Years experienced Certified Professionals? So you can Access our data science training institute easily to the Prwatech Nearest Hub.Thus we help Enthusiasts to Learn Advanced Concepts in Tableau, R-Programming, Python, SAS With our Industry certified professionals as a part of our data science training institute in Bangalore.

Best Data Science Training Institute in Bangalore


Anyone can easily dream to become the certified pro Data Scientist but only desired & Passionate candidates can achieve the dreams. Are you also dreaming to become a Certified Professional? Then Step into Our Data Science certification courses in bangalore, We make your dreams come true with our Advanced-Data scientist Certification Programs.

Therefore this technology is said to be one of the trending technology in the current IT market which is creating huge Buzz and Massive Job opportunities. To begin with, we understand the Industry requirements of the Data Science Training in Bangalore and we stand out among other competitors with our advanced-Data scientist certification course.

So then it is necessary to learn the course under Experienced Professionals of Data science Training Institute in Bangalore is more than ever in the present business scenario. The need to fill the void for an able data scientist is necessary ever than before. Gone are the days of brick and mortar business. The present business needs to have powerful tools to meet global competition. Unknown challenges, unexpected changes in political and economic status in the global market need to be met suitably by effective business strategies.

Likewise choosing data science online courses for the following reasons explained below for the benefit of those who wish to take up this courses. Connect with consumers effectively, knowing them directly help to develop higher-end products that are beneficial both the ways.

Contact Us +91 8147111254


Rs. 16000/-Enroll Now


Rs. 16000/-Enroll Now


Rs. 16000/-Enroll Now


Rs. 16000/-Enroll Now

Why Data Science Training In Bangalore at Prwatech

Our institution has been regarded as one of the best as they offer industry-standard courses for those who want to get the skills to work in the data science field. We help tech enthusiasts get fantastic in tableau, R programming, python, among other skills that they require to rise in the IT industry. Of course, there is no shortage of data science training courses as well as videos online. However, you need proper training that will help you land a job. This is why you should join us as we can help you sharpen your skills.


Best Trainers For Data Science Training in Bangalore.


No amount of videos is going to if you do not get hands-on training. This knowledge is not enough practical training is also required. This is why we have educators who have years of experience working in the leading MNCs. With their guidance, you will be able to be the very best.


Join Us Today!


Go to our nearest hub and get yourself enrolled. We are going to start from the beginner level and then push you to become an advanced professional data scientist with our expert guidance. Some of the best minds in the industry, you will get hands-on training and ace any interview set your eyes for. Choose the best for yourself, and do not wait any longer.

We’re India’s Leading and best python training institute in Bangalore, offers Courses with 100% Job assistance.

  • Wi-Fi Class Rooms
  • Get trained by the finest qualified professionals
  • 100% practical training
  • Flexible timings
  • Real-Time Projects
  • Resume Writing Preparation
  • Mock Tests & interviews
  • Access to Our Learning Management System Platform
  • Access to 1000+ Online Video Tutorials
  • Weekend and Weekdays batches
  • Affordable Fees
  • Complete course support
  • Guidance till you reaches your goal.

Why Data Science Training in Bangalore?

Data Scientist is a person who Usually analyses entire accumulated data from various sources, across various machines concerned with the subject or a product in order to provide suitable insights to his company. The growth of the company depends upon the strategically plans drawn Particularly by forecasting marketing changes depending upon the insights provided by their data science.

To summarize Dial Now 8147111254 for any queries regarding data science course fee & Job Assistance.

Who can take Data Science Training in Bangalore?

  • IT professionals
  • Business Analysts
  • Data Analysts
  • Warehouse managers
  • Application Developers
  • Job seekers in the IT industry
  • Business operators


Data Science Training Modules

Module 1: Python Essentials

Learning Objective: To understand why Python is a useful scripting language for developers. To learn how to design and program Python applications.


  • What is Python..?
  • A Brief history of Python
  • Why Should I learn Python..?
  • Installing Python
  • How to execute Python program
  • Write your first program

Variables & Data Types

  • Variables
  • Numbers
  • String
  • Lists, Tuples & Dictionary

Conditional Statements & Loops

  • If…statement
  • If…else statement
  • elif…statement
  • The while…Loop
  • The for….Loop

Control Statements

  • Continue statement
  • Break statement
  • Pass statement


  • Define function
  • Calling a function
  • Function arguments
  • Built-in functions

Modules & Packages

  • Modules
  • How to import a module…?
  • Packages
  • How to create packages
  • Classes & Objects

Introduction about classes & objects

  • Creating a class & object
  • Inheritance
  • Methods Overriding
  • Data hiding

Files & Exception Handling

  • Writing data to a file
  • Reading data from a file
  • Read and Write data from CSV file
  • Try…except
  • Try…except…else
  • Finally
  • OS module

Module 2: Statistics

Learning Objective: Demonstrate knowledge of statistical data analysis techniques utilized in decision making. Apply principles of Data Science to the analysis of problems. Be able to restate an investigative question in terms of a statistical model or algorithm.

Inferential statistics

  • Standard error
  • Confidence interval
  • Hypothesis testing
  • p-value

Sample vs. population mean

  • Variance of population

Probability and statistics

  • Probability density functions
  • Poisson Process
  • Law of large numbers
  • Normal distribution
  • Sampling distribution
  • Mean
  • Variance of Bernoulli
  • Regression

Module 3: Introduction to Machine Learning (ML)

Learning Objective: The purpose of machine learning is to discover patterns in your data and then make predictions based on those often, complex patterns to answer business questions, and help solve problems. Machine learning helps analyze your data and identify trends.


  • What is Machine learning?
  • Overview about sci-kit learn and tensorflow
  • Types of ML
  • Some complementing fields of ML
  • ML algorithms
  • Machine learning examples

NumPy Arrays

  • Creating multidimensional array
  • NumPy-Data types
  • Array attributes
  • Indexing and Slicing
  • Creating array views and copies
  • Manipulating array shapes
  • I/O with NumPy

Working with Pandas

  • Installing pandas
  • Pandas data frames
  • Pandas Series
  • Data aggregation with Pandas Data Frames
  • Concatenating and appending Data Frames
  • Joining Data Frames
  • Handling missing data

Python Regular Expressions

  • What are regular expressions?
  • The match Function
  • The search Function
  • Matching vs searching
  • Search and Replace
  • Extended Regular Expressions
  • Wildcard

Python Oracle Database Access

  • Install the cx_Oracle and other Packages
  • Create Database Connection
  • DML and DDL Operation with Databases
  • Performing Transactions
  • Handling Database Errors
  • Disconnecting Database

Regression based learning

  • Simple regression
  • Multiple regression
  • Logistic regression
  • Predicting house prices with regression

Clustering based learning

  • Definition
  • Types of clustering
  • The k-means clustering algorithm

Data mining

  • Introducing data mining
  • Decision Tree
  • Affiity Analysis
  • Clustering

Introducing matplotlib

  • Bar Charts
  • Line Charts
  • Scatter plots
  • Bubble charts

Working with openCV

  • Setting up opencv
  • Loading and displaying images
  • Applying image filters
  • Tracking faces
  • Face recognition

Module 4: Performing predictions with Linear Regression

Learning Objective: The basic objective of this data science course is to learn How to calculate a simple linear regression step-by-step, How to perform all of the calculations using a spreadsheet, How to make predictions on new data using your model, A shortcut that greatly simplifies the calculation.

Simple linear regression

  • Multiple regression
  • Training and testing model

Introduction to Python

  • Installation of Python framework and packages: Anaconda & pip Writing/
  • Running python programs using Spyder Command Prompt Working with Jupyter
  • Creating Python variables
  • Numeric, string and logical operations
  • Data containers: Lists, Dictionaries, Tuples & sets Practice assignment

Iterative Operations & Functions in Python

  • Writing for loops in Python
  • While loops and conditional blocks List/Dictionary comprehensions with loops
  • Writing your own functions in Python Writing your own classes and functions

Data Handling in Python using NumPy & Pandas

  • Introduction to NumPy arrays, functions & properties Introduction to Pandas &
    Data frames
  • Importing and exporting external data in Python Feature engineering using

Data Science & Machine Learning in Python

  • Converting business problems to data problems
  • Understanding supervised and unsupervised learning with examples
  • Understanding biases associated with any machine learning algorithm Ways of
  • Reducing bias and increasing generalization capabilities Drivers of machine
  • Learning algorithms
  • Cost functions
  • Brief introduction to gradient descent
  • Importance of model validation
  • Methods of model validation
  • Cross validation & average error

Generalized Linear Models in Python

  • Linear Regression
  • Regularization of Generalized Linear Models
  • Ridge and Lasso Regression
  • Logistic Regression
  • Methods of threshold determination and performance measures for Classification score models

Tree Models using Python

  • Introduction to decision trees
  • Tuning tree size with cross validation Introduction to bagging algorithm Random
  • Grid search and randomized grid search ExtraTrees (Extremely Randomised
    Trees) Partial dependence plots
  • Support Vector Machines (SVM) & kNN in Python
  • Introduction to idea of observation based learning Distances and similarities
    k Nearest Neighbors (kNN) for classification Brief mathematical background on
    SVM Regression with kNN & SVM

Unsupervised learning in Python

  • Need for dimensionality reduction
  • Principal Component Analysis (PCA) Difference between PCAs and Latent
  • Factors Factor Analysis
  • Hierarchical, K-means & DBSCAN Clustering

Text Mining in Python

  • Gathering text data using web scraping with urllib
  • Processing raw web data with BeautifulSoup
  • Interacting with Google search using urllib with custom user agent collecting twitter data with
  • Twitter API
  • Naive Bayes Algorithm
  • Feature Engineering with text data
  • Sentiment analysis

Leading Data Science Training Institute in Bangalore

Certified Data Science Training

If you have a thirst for learning under the very best, join our certified program where you will be under accredited industry professionals’ guidance. Their expertise in teaching and knowledge is going to not only teach you the course but will also help you with 100% placement assistance.

Our Students Come First

Overview About Data Science Course in Bangalore

Once we’re done with that. We’ll cover the basics of machine learning where we’ll see what exactly machine learning is and the different types of machine learning next. We will move onto the K means algorithm and we’ll discuss a use case of the k-means clustering after which we Discuss the various steps involved in the k-means algorithm and then we will finally move on to the Hands-On part where we use the k-means algorithm to Cluster movies based on their popularity on social media platforms, like Facebook at the end of today’s session will also discuss what a data science certification is and why you should take it up.

So, guys, there’s a lot to cover in today’s session. Let’s jump into the first topic. Do you guys remember the times when we have telephones and we had to go to PC your boots in order to make a phone call? Call now those things are very simple because we didn’t generate a lot of data.

We didn’t even store the contacts and our phones or our telephones. We used to memorize phone numbers back then or you know, these have a diary of all our contact but these days we have smartphones with store a lot of data. So there’s everything about us in our mobile phones.

We have images we have contacts. We have various apps. We have games. Everything is stored on a mobile phones these days similarly the PCS that we use in the earlier times.


It used to process very little data. All right, there was A lot of data processing needed because technology was an evolved that much.

So if you guys remember we use floppy disk back then and floppy. This was used to store small amounts of data, but later on hard disks were created and those used to store GBS of data. But now if you look around there’s data everywhere around us. All right, we have a data stored in the cloud. We have data in each and every Appliance at our houses. Similarly, if you look at smart cars these days they’re connected to the internet they connected to a mobile phones and this also generates a lot of data. What we don’t realize is that evolution of technology has generated a lot of data.

All right.

Now initially there was very little data and most of it was even structured only a small part of the data was unstructured or semi-structured. And in those days you could use Simple bi Tools in order to process all of this data and make sense out of it. But now we have way too much data and order to process this much data. We need more complex algorithms. We need a better process. All right, and this is

where data science comes in now guys, I’m not going to get into the depth of data science. Yet I’m sure all of you have heard of iot or Internet of things.

Now, Did you guys know that we produce 2.5 quintillion bytes of data each day. And this is only accelerating with the growth of iot. Now iot or Internet of Things is just a fancy term that we use for network of tools or devices that communicate and transfer data through the internet. So various devices are connected to each other through the internet and they communicate with each other right now the communication happens by exchange of data.


So with the emergence of the internet, we now perform all our activities online. Okay, obviously, this is helping us, but we are unaware of how much data we are generating what can be done with all of this data and what if we could use the data that we generated to our benefit?

Well, that’s exactly what data science doe’s data science is all about extracting the useful insights from data and using it to grow your business.

Frequently Asked Questions

  • What are the types of training available?
    Answer: Prwatech Offering 2 Types of training modes to the candidates, one can choose Wither classroom training or online classes depends on candidate flexibility.


  • Does watch offer placement assistance after the course completion?
    Answer: Yes! We do provide the 100% Placement assistance as a part of our advanced data science training  program once the candidate has done the course with us.


  • What is the companies’ watch tie-up with?
    Answer: we tie-up with Flipkart, Capgemini, Syntel, Synchron, SunGard, HCl & Other Top MNC companies.


  • What are the payment options?
    Answer: We accept Payments from Cash, Card, GooglePay, UPI, and PayPal.

Why is Data Science important?
Importance of Data Science
Due to the growing importance of data, The demand for a Data scientist is also rising. They are now becoming essential parts of products, trades, public agencies, and non-profit organizations.

Data science is gaining popularity in every industry and thus playing a significant role in the functioning and growth of any product. Therefore, the requirement of the data scientists is also increased as they have to perform an important task of handling data and delivering solutions for the specific problems.

With the help of Data Science, the companies will be able to recognize their client in a more improved and enhanced way. Clients are the foundation of any product and play an essential role in their success and failure. Data Science enables companies to connect with their customers in a modified manner and thus confirms the better quality and power of the product. One of the important features of Data Science is that its results can be applied to almost all types of industries such as travel, healthcare and education. With the help of Data Science, the industries can analyze their challenges easily and can also address them effectively.

Today, Data science has extensive ramifications in numerous fields, i.e., in theoretical and applied research areas such as machine interpretation, speech recognition, advanced economy and also in the fields like healthcare, social science, medical informatics. Data Science influences the growth and improvements of the product by providing a lot of intelligence about customers and operations, by using methods such as data mining and Data Analysis.

Data Science is one of the growing fields. It has become an important part of almost every sector. It provides the best solutions that help to fulfill the challenges of the ever-increasing demand and maintainable future. As the importance of data science is increasing day by day, the need for a data scientist is also growing. Data scientists are the future of the world.

How do I get a Data Science job?

Firstly you will need to polish your problem solving skills, If you are coming from a quantitative background, Data Science should be an easy transition. Before analyzing data with high-tech tools, you need to get to the foundation of data analysis. Try and connect with Data Scientist on LinkedIn. Share the projects that you have worked on , It will be very beneficial.

You can also discover what kind of company you’d like to work for (size, industry, culture), what projects appeal to you, and how to prepare for the job application process, Most Importantly learn two or more programming skills.

Data science is a highly interdisciplinary field, and most likely not all prior knowledge will be lost. Data scientists need to be able to connect their models to direct business impact. Although you should definitely focus on your data science experience in your resume and cover letter.

Before you even think about applying for jobs, of course, you need to be sure you’ve got the skill set employers are looking for. And thankfully, there are lot of resources in our website that you can refer to know more about Data Science.

Having the right skills is important but skills alone are never going to get you a job unless potential employers can see what you’re doing. “If you really want to stand out” Build your portfolio, GitHub, Blog, Kaggle profile, or all of these that showcase your interest, passion, and proficiency in data science.”

Do I need a Data Science certificate?
Of course, There are definitely some advantages that come along with a Data Science certification. It reflects your interest and passion in the field of data science but there’s a caveat – due to the boom of data science, there has been a massive uptake of these courses which makes them common or general.

If you want to stand out, You will need to take up a course that provides you with Industry exposure, Real-Time, High-Quality projects. We have resources in our website for you to go through it. A certification is a standard to measure a great skill set.

In simple words, certifications definitely matter but not because of the certificate itself but because of the skills you have gained as part of the certification.
Recruiters receive a lot of applications and they focus on the skillsets and projects completed.
In the end, During the interview process. The interviewer will test everything that you have mentioned in your skillset. Therefore, if you choose to go ahead with a data science certification, make sure that you keep up with your classes and gain the right skills. Certification can just arrange the Interview but it won’t get you the job, skills will.

What is the difference between Data Science and Machine Learning?

For better comprehension, Data Science is the processing and analysis of data that you generate for various insights that will serve a business purposes. It is an evolutionary extension of statistics capable of dealing with the massive amounts of with the help of computer science technologies.
Data scientists use a combination of tools, applications, principles and algorithms to make sense of random data clusters. Since almost all kinds of organizations today are generating exponential amounts of data around the world, it becomes difficult to monitor and store this data. Data science focuses on data modeling and data warehousing to track the ever-growing data set. The information extracted through data science applications are used to guide business processes and reach organizational goals.
Data science uses a wide array of data-oriented technologies including SQL, Python, R, and Hadoop, etc.
However, it also makes extensive use of statistical analysis, data visualization, distributed architecture, and more to extract meaning out of sets of data.
They can also work with machine learning equally In fact, data scientists need machine learning skills for specific requirements like:
Machine Learning for Predictive Reporting: Data scientists use machine learning algorithms to study transactional data to make valuable predictions. Also known as supervised learning, this model can be implemented to suggest the most effective courses of action for any company.

Machine Learning for Pattern Discovery: Pattern discovery is important for businesses to set parameters in various data reports and the way to do that is through machine learning. This is basically unsupervised learning where there are no pre-decided parameters. The most popular algorithm used for pattern discovery is Clustering.

Machine learning enables a computer system to make predictions or take some decisions using historical data without being explicitly programmed. Machine learning uses a massive amount of structured and semi-structured data so that a machine learning model can generate accurate result or give predictions based on that data.
Machine learning works on algorithm which is followed by its own using historical data. It works only for specific domains such as if we are creating a machine learning model to detect pictures of pets, it will only give result for pets images, Any inaccurate data will not be responsive. Machine learning is being used in various places such as for online recommendation system, for Google search algorithms, YouTube suggestions, Facebook tagging suggestion, etc.
It can be divided as follows:
o Supervised learning
o Reinforcement learning
o Unsupervised learning


  • Who can take Data Scientist Certification course
    Answer: Working Professionals, Job Hunters, IT professionals Fresher’s, Students & Students who are about to complete the degree.


Best Data Science Training institutes in Bangalore Reviews


  • Ravi: Prwatech training institute was very much helpful to clear the interview with their advanced Data Scientist Courses Programs; I got proper Hands-on Practice exposure during Certification.


  • Praveen: Trainers of Prwatech are very experienced, as well as their teaching as well, It is so good that we need not revise again and again. The best thing is that the teaching methodology they’re using is fantastic which helps to understand the technology easier.


  • Sumedha Nigudkar: I am very happy with the data scientist training and placement Program from Prwatech offering to the candidates which will definitely an ideal option to any kind of candidates who are willing to join the training institite.


  • Gaurav: Prwatech has well-equipped world-class training facilities which make feel learning the technology from the world-class trainers. i can say this is one of the best training institute for data science in Bangalore




Learning Objectives – Data scientist certification course  module one starts with the basics of Business Analytics, R basic, R programming, R’s role in solving analytical problems, R’s popularity in tech giants like- Facebook, Google, Finance, etc.

Topics: Business Analytics, R, R language and programming, Ecosystem, Uses of R, Data types in R, subsetting methods, R comparison with other software’s, R installation, operations in R, useful packages, IDER, GUI, using functions like- length(), str(), ncol() etc, summarize data.

Learning Objectives – In this module, we will discuss dirty data set, data cleaning, which results in data set, and ready for analysis, exploring functions, the versatility of R, the robustness of R. This module helps you to understand various importing techniques in R

Topics: Data Cleaning, Data Inspection, Troubleshooting the problems, Function uses, grepl(), sub(), grep(), Data coerce, apply() functions use, Import data from spreadsheets into R, Importing text files into R, import data, installation of packages. RDBMS from R, using ODBC, SQL queries, Web Scraping

Learning Objectives – Data scientist certification module 3 includes information about exploratory data analysis, EDA- for observing what data tell us beyond the formal hypothesis. Typical EDA process.

Topics: Exploratory Data Analysis, Implementing EDA on datasets, Box plots, cor() in R, EDA functions, list(), summarize(), Multiple packages in R, various plots, Segment plot, HC plot in R.

Learning Objectives – In this module, you will learn about- visualization in USP of R, creating simple visualization in R, complex visualization in R.

Topics: Data Visualization, Graphical representations, Functions in R, Plotting graphs like box plot, table plot and histogram, Improvising plots, GUIs, Deducer, R Commander and Spatial Analysis.

Learning Objectives – In this module, we will discuss various Machine Learning algorithms, Machine Learning types- Unsupervised Learning and Supervised Learning, Difference between Unsupervised Learning and Supervised Learning. We will also learn about K-means Clustering and implementing them, Concepts of Decision trees and Random forest, Algorithm creation of forests and trees, LMS.

Topics: Data Mining, Machine Learning algorithms, Unsupervised and Supervised Machine, K-means clustering, Decision Trees, Random forest Algorithm creation, Entropy, features, and working of Random forest.

HQL and Hive with Analytics

Learning Objectives – Data science training in Bangalore, module 6, we will introduce- Regression Techniques, Linear and logistic regression basics, implementation in R.

Topics: Simple and Multiple Linear Regressions, Simple and Multiple Logistic Regression, Log-Linear Model.

Learning Objectives – In this module, we will discuss the Analysis of Variance, ANOVA Technique, Predictive Analysis.

Topics: Anova Technique, Sample testing, One and Two-way ANOVA, Predictive Analysis.

You will be given a problem statement and in this module, our qualified professionals will help you with that statement.

Perform Exploratory Data Analysis


Establish a hypothesis of data
Handle outliers
Troubleshooting missing data.
Validation datasets.
Logistic Regression
Confusion Matrix
Final model

In this world where data is becoming the base of every decision, Data Science is becoming the need of the hour. We has always been known for understanding the market gap and come up with courses that are in alignment with the industry need. Data Scientist course at Prwatech is a 40-hour practical based along with with 35-course classes program. After completion of which you will be able to understand business intelligence and business analytics with the power of analyzing large data junks. Along with that, you will be able to debug and understand all aspects of Big Data Technology. Some key topics that you would be covering with Prwatech are Business Analytics, R, R language and programming, Ecosystem, Uses of R, Data types in R, subsetting methods, R comparison with other software’s, and many more

Prwatech offers various modes of training, from classroom to online (self-learning and instructor-led). We understand that each student is different so does his needs. Therefore, we believe in teaching with different modes so that each student can start his learning journey on his own. With the various modes of study, a student can select which fits best to him. In case of query, our team of experts is always available for help.

At Prwatech we take each student very seriously and believe in providing 100% support throughout their learning journey. We have 24/7 contact support. Where in a student can raise in their query and within no time email support will be provided. A student can avail this support even after the completion of the course.

The only pre-requisite for this course is to have an aptitude for analytical thinking. You need not be a programmer, never the less if you have programming skills that would be an added benefit.

Our Data Science course has a nice blend of real-life cases as a part of the training curriculum. We believe in preparing students for industry. These cases will help you to test your skills at all levels. As these are a real-life problem they will help you to build a greater understanding of the problems that you might face at work.

It does not matter which mode of education you chose to take with Prwatech. We offer placements for all the modes of the course. Prwatech has tie-ups with more than 80+ organization within various industries.

INR  16000

35 Hours
Practical 40 Hours
15 Seats
Course Badge
Course Certificate

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