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Data Science Course in Pune

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Data science is about uncovering hidden data patterns that relate to statistics, activity, comprehension, and conclusions to support business choices. The professionals who carry out these tasks are data scientists or scientists. The top qualification in this field for data science is thought to be granted by 3RI. With over 400+ participants placed in major international businesses, including BMC, IBM, Accenture etc., as part of the Data Science training program, our institute is regarded as the top Data Science training institute in Pune. 

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We provide EMI facility for you 

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EMI Plan Registration Percent
EMI
With
0% Interest
On Registration
10%
1st installment - 1st month of joining
70%
2nd installment - 2nd month
20%

Key Features

Course Duration : 5 Months

Real Time Projects : 2

Project Based Learning

EMI Option Available

Certification & Job Assistance

24 x 7 Support

Take A Quantum Leap in your Career to New Heights.

Enroll Now for Data Science at 3RI Technologies

Data Science Syllabus

The detailed syllabus is designed for freshers as well as working professionals

1. Fundamentals of Data Science and Mathematical statistics
    ● Introduction to Data Science
    ● Need of Data Science
    ● BigData and Data Science
    ● Data Science and machine learning
    ● Data Science Life Cycle
    ● Data Science Platform
    ● Data Science Use Cases
    ● Skill Required for Data Science
2. Mathematics For Data Science
    ● Linear Algebra-Matrices
        o Zero
        o One
        o Identify
        o Diagonal
        o Column
        o Row
        o Operations
3. Introduction to Statistics
    ● Descriptive vs. Inferential Statistics
    ● Types of data
    ● Measures of central tendency and dispersion
    ● Hypothesis & inferences
    ● Hypothesis Testing
    ● Confidence Interval
    ● Central Limit Theorem
4. Probability and Probability Distributions
    ● Probability Theory
    ● Conditional Probability
    ● Data Distribution
    ● Distribution Functions
        o Normal Distribution
        o Binomial Distribution

1. Using a Spreadsheet
    ● What is Excel?
    ● Why Use Excel?
    ● Excel Overview
    ● Excel Ranges, Selection of Ranges
    ● Excel Fill, Fill Copies, Fill Sequences,Sequence of Dates
    ● Excel adds, move, and delete cells
    ● Excel Formulas
    ● Relative and Absolute References
2. Functions
    ● SUM
    ● AVERAGE
    ● COUNT
    ● MAX & MIN
    ● RANDBETWEEN
    ● TRIM
    ● LEN
    ● CONCATENATE
    ● TODAY & NOW
3. Advanced Functions
    ● Excel IF Function
    ● Excel If Function with Calculations
    ● How to use COUNT, COUNTIF, and
    ● COUNTIFS Function?
4. Data Visualization
    ● Excel Data Analysis – Data Visualization
    ● Visualizing Data with Charts
    ● Chart Elements and Chart Styles
    ● Data Labels
    ● Quick Layout

  • An Introduction to RDBMS & SQL
  • Data Retrieval with SQL
  • Pattern matching with wildcards
  • Basics of sorting
  • Order by clause
  • Aggregate functions
  • Group by clause
  • Having clause
  • Nested queries
  • Inner join
  • Multi join
  • Outer join
  • Adding and Deleting columns
  • Changing column name and Data Type
  • Creating Table from existing Table
  • Changing Constraints Foreign key.

1. An Introduction to Python

    ● Why Python , its Unique Feature and where to use it?
    ● Python environment Setup/shell
    ● Installing Anaconda
    ● Understanding the Jupyter notebook
    ● Python Identifiers, Keywords
    ● Discussion about installed module s and packages
2. Conditional Statement ,Loops and File Handling
    ● Python Data Types and Variable
    ● Condition and Loops in Python
    ● Decorators
    ● Python Modules & Packages
    ● Python Files and Directories manipulations
    ● Use various files and directory functions for OS operations
3. Python Core Objects and Functions
    ● Built in modules (Library Functions)
    ● Numeric and Math’s Module
    ● String/List/Dictionaries/Tuple
    ● Complex Data structures in Python
    ● Python built in function
    ● Python user defined functions
4. Introduction to NumPy
    ● Array Operations
    ● Arrays Functions
    ● Array Mathematics
        ▪ Mean
        ▪ Standard Deviation
        ▪ Max
        ▪ Min
    ● Array Manipulation
        ▪ Reshaping
        ▪ Resizing
    ● Array I/O
    ● Matrix creation
    ● Data generation
    ● Random function
    ● Normalized Data Generation
    ● Indexing and Slicing
    ● Transpose
    ● Importing Files with Numpy
5. Data Manipulation with Pandas
    ● Data Frames
    ● Series
    ● I/O
    ● Creating Pandas DataFrame
    ● Selection in DFs
    ● Data Describe
    ● Data info
    ● Retrieving in DFs
    ● Applying Functions
    ● Reshaping the DFs – Pivot
    ● Combining DFs Merge Join
    ● Data Alignment
6. SciPy
    ● Hypothesis Testing using Scipy
    ● Shapiro Test
    ● Spearmaman Test
    ● T-Test of Independents
    ● Chi-Square Test
7. Visualization with Seaborn
    ● Seaborn Installation
    ● Introduction to Seaborn
    ● Basics of Plotting
    ● Plots Generation
    ● Visualizing the Distribution of a Dataset
    ● Selection color palettes
8. Visualization with Matplotlib
    ● Matplotlib Installation
    ● Matplotlib Basic Plots & it’ s Containers
    ● Matplotlib components and properties
    ● Scatter plots
    ● 2D Plots
    ● Histograms
    ● Bar Graphs
    ● Pie Charts
    ● Box Plots
    ● Customization
    ● Store Plots
9. Descriptive Statistics
    ● Data understanding
    ● Observations, variables, and data matrices
    ● Types of variables
    ● Measures of Central Tendency
    ● Arithmetic Mean / Average
        ▪ Merits & Demerits of Arithmetic Mean and Mode
        ▪ Merits & Demerits of Mode and Median
        ▪ Merits & Demerits of Median Variance
10. Probability Basics
    ● Notation and Terminology
    ● Unions and Intersections
    ● Conditional Probability and Independence
11. Probability Distributions
    ● Random Variable
    ● Parameters vs. Statistics
    ● Binomial Distribution
    ● Central Limit Theorem
12. Tests of Hypothesis
    ● Large Sample Test
    ● Small Sample Test
    ● One Sample: Testing Population Mean
    ● Hypothesis in One Sample z-test
    ● Two Sample: Testing Population Mean
    ● One Sample t-test – Two Sample t-test
    ● Paired t-test
    ● Hypothesis in Paired Samples t-test
    ● Chi-Square test
13. Data Analysis
    ● Case study- Netflix
    ● Deep analysis on Netflix data

1. Exploratory Data Analysis
    ● Data Exploration
    ● Missing Value handling
    ● Outliers Handling
    ● Feature Engineering
    ● Train-Test Split
    ● Standard Scaler
    ● Min-Max Scaler
    ● Data Preprocessing
    ● Resampling
        ▪ Up-Sampling
        ▪ Down-Sampling
2. Machine Learning: Supervised Algorithms Classification
    ● Introduction to Machine Learning
    ● Linear Regression
    ● Multiple Linear Regression
    ● Gradient Descent
    ● Logistic Regression
        ▪ Binary Classification
        ▪ Multiclass classification
    ● Naïve Bays Algorithm
    ● K-Nearest Neighbor Algorithm
    ● Decision Tress
        1. Classification
        2. Regression
    ● Support Vector Machines
    ● Random Forest
    ● Model Evaluation and performance
        ▪ Accuracy ,Precision
        ▪ Recall
        ▪ F1 Score
        ▪ Confusion Matrix
        ▪ Classification Report
        ▪ K-Fold Cross Validation
        ▪ ROC, AUC etc…
    ● Hyper parameter tuning
3. Machine Learning: Unsupervised Learning Algorithms
    ● Similarity Measures
    ● Cluster Analysis and Similarity Measures
    ● K-Means Clustering
        ▪ Elbow Method
    ● Hierarchical Clustering
        ▪ Agglomerative Clustering
    ● K-Nearest Neighbors
4. Ensemble algorithms
    ● Bagging
        ▪ Random Forest
    ● Boosting
        ▪ Adaboost
    ● Voting
    ● Stacking
    ● Principal Components Analysis
5. Recommendation Systems
    ● Collaborative filtering model
    ● Content-based filtering model.
    ● Hybrid collaborative system

1. Artificial Intelligence
    ● An Introduction to Artificial Intelligence
    ● History of Artificial Intelligence
    ● Future and Market Trends in AI
    ● Intelligent Agents – Perceive-ReasonAct Loop
    ● Constraint-based Reasoning
2. Deep networks and structured knowledge
    ● Studying Neural Elements
    ● Neural Networks and Perceptions
    ● Understanding Feedforward Networks
    ● Exploring Backpropagation
    ● Working of Neural Networks
3. Natural Language Processing
    ● Natural Language Processing
    ● NLP in Python
    ● Tokenization
    ● Part of Speech Tagging (POS Tagging)
    ● Named Entity Recognition
    ● Semantic Analysis
    ● Sentiment Analysis
4. Artificial Neural Network
    ● Understanding Artificial Neural Network
    ● The Activation Function
    ● Building an ANN
    ● Building Problem Description
    ● Evaluation the ANN
    ● Improving the ANN
    ● Tuning the ANN.
5. Conventional Neural Networks
    ● CNN Intuition
    ● Convolution Operation
    ● Kernel Operation
    ● Filtering operation
    ● Padding on image
    ● Pooling Layer
    ▪ Max Pooling
    ▪ Average Pooling
    ● ReLU Layer
    ● Fully Connected Dense Layer
    ● Softmax and Cross-Entropy
    ● Building a CNN
    ● Evaluating the CNN
    ● Improving the CNN
    ● Tuning the CNN
6. Recurrent Neural Network
    ● Recurrent Neural Network
    ● RNN Intuition
    ● The Vanishing Gradient Problem
    ● LSTMs and LSTM Variations
    ● Practical Intuition
    ● Building an RNN
    ● Evaluating the RNN
    ● Improving the RNN
    ● Tuning the RNN
7. Time Series Data
    ● Introduction to Time series data
    ● Data cleaning in time series
    ● Pre-Processing Time-series Data
    ● Prediction in Time Series using LSTM
    ● Prediction in Time Series using ARIMA

1. Introduction to Git & Distributed Version Control
2. Life Cycle
3. Create clone & commit Operations
4. Push & Update Operations
5. Stash, Move, Rename & Delete Operations.

Project Work and Case Studies
    ❖ Machine Learning end to end Project blueprint
    ❖ Case study on real data after each model.
    ❖ Regression predictive modeling – E-commerce
    ❖ Classification predictive modeling – Binary Classification
    ❖ Case study on Binary Classification – Bank Marketing
    ❖ Case study on Sales Forecasting and market analysis
    ❖ Widespread coverage for each Topic
    ❖ Various Approaches to Solve Data Science Problem
    ❖ Pros and Cons of Various Algorithms and approaches
    ❖ Amazon-Recommender
    ❖ Image Classification Sentiment Analysis
Project Domain: FINANCE
    ● The insurance company wants to decide on the premium using various parameters of the client.
    ● It’ s an important problem to keep the clients and attract new ones.
By completing this Project you will learn:
    ● How to collect data?, how to justify the right features? , Which ML / DL model is best in this situation? How much data is enough?
    ● How to have CI/CD in the project?
    ● How to do Deployment of Project to cloud?
Project Domain: Image Processing in Health care
    ● A hospital wants to automate the Detection of pneumonia in X-rays using image processing.
By completing this Project you will learn:
    ● How to handle image data? How to preprocess and augment image data?
    ● How to choose the right model for the image process?
   ● How to apply transfer learning in image processing?
    ● How to do incremental learning & CI/CD in the project?
    ● How to do Deployment of Project to cloud?

Natural Language Processing
    ● One of the companies wants to automate applicant’ s level in English communication.
    ● Create a ML/DL model for this task.
By completing this Project you will learn:
    ● How do convert text to the right representation?
    ● How to preprocess text data? How to select the right ML/DL model for text data?
    ● How to do transfer learning in Text Analytics?
    ● How to do CI/CD in a text analytics project? How to do Deployment of Project to cloud?
Mechanical
    ● A mechanical company wants to perform predictive maintenance of engine parts.
    ● This enables the company to efficiently change parts before the machine fails.
By completing this Project you will learn:
    ● How to handle time-series data?
    ● How to preprocess time series data?
    ● How to create ML/DL model for Time-series Data?
    ● How to do CI/CD in a text analytics project?
    ● How to do Deployment of Project to cloud?
Sales / Demand Forecasting
    ● Predict the sales/demand of a product of a company.
    ● Sales / Demand forecasting of the product will help the company efficiently manage the resources.
    ● Create a ML/DL model for this problem.
By completing this Project you will learn:
    ● How to handle time-series data?
    ● How to preprocess time series data?
    ● How to create ML/DL model for Time-series Data?
    ● How to do CI/CD in a text analytics project? How to do Deployment of Project to cloud?

1. Introduction to Data Visualization & Power of Tableau
    ● Architecture of Tableau
    ● Product Components
    ● Working with Metadata and Data Blending
    ● Data Connectors
    ● Data Model
    ● File Types
    ● Data Source Filters
2. Scatter Plot
    ● Funnel Chart
    ● Waterfall Chart
    ● Working with Filters
    ● Organizing Data and Visual Analytics
    ● Working with Mapping
    ● Working with Calculations and Expressions
    ● Working with Parameters
    ● Charts and Graphs
    ● Dashboards and Stories

  • Machine Learning end to end Project blueprint
  • Case study on real data after each model.
  • Regression predictive modeling – E-commerce
  • Classification predictive modeling – Binary Classification
  • Case study on Binary Classification – Bank Marketing
  • Case study on Sales Forecasting and market analysis
  • Widespread coverage for each Topic
  • Various Approaches to Solve Data Science Problem
  • Pros and Cons of Various Algorithms and approaches
  • Amazon-Recommender
  • Image Classification
  • Sentiment Analysis

Project Domains: Finance

  • Insurance company wants to decide on the premium using various parameters of the client.
  • It’s an important problem to keep the clients and attract new ones.

By completing this project you will learn:

  • How to collect data?, how to justify right features? , Which ML / DL model is best in this situation? How much data is enough?
  • How to have CI/CD in the project?
  • How to do Deployment of Project to cloud?

Image Processing in Health care

  • A hospital wants to automate Detection of pneumonia in X-rays using image processing.

By doing this project you will understand

  • How to handle image data? How to preprocess and augment image data? How to choose right model for image process?
  • How to apply transfer learning in image processing?
  • How to do incremental learning & CI/CD in the project?
  • How to do Deployment of Project to cloud?

Natural Language Processing

  • One of the companies wants to automate applicant’s level in English communication.
  • Create a ML/DL model for this task.

By completing this project you will learn

  • How to do convert text to right representation? How to preprocess text data? How to select right ML/DL model for text data ?
  • How to do transfer learning in Text Analytics?
  • How to do CI/CD in text analytics project?
  • How to do Deployment of Project to cloud?

Mechanical

  • A mechanical company wants to perform predictive maintenance of engine parts.
  • This enables company to efficiently change parts before machine fails.

By performing this task you will learn,

  • How to handle time series data?
  • How to preprocess time series data?
  • How to create ML/DL model for Time series Data?
  • How to do CI/CD in text analytics project?
  • How to do Deployment of Project to cloud?

Sales / Demand Forecasting

  • Predict the sales / demand of a product of a company.
  • Sales / Demand forecasting of the product will help company efficiently manage the resources.
  • Create a ML/DL model for this problem.

By performing this project you will learn,

  • How to handle time series data?
  • How to preprocess time series data?
  • How to create ML/DL model for Time series Data?
  • How to do CI/CD in text analytics project?
  • How to do Deployment of Project to cloud?

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Data Science Course in Pune

Data Science, in its simpler terms, is about generating critical business value from the data through various creative ways. It can also define as a mix of data research, algorithms, and technology to solve complex analytical issues. Data is being by generated by Companies at an exponential pace. The usable Data form can be different for different sections of people working in an organization.

Data Science Classes helps us to explore the data to the granular form and find the needed insights. Data Science is about being analytical or inquisitive wherein asking new questions, doing further explorations, and keep learning is a part of the job for Data Scientists.

According to Harward Business Review, Data Scientist is the Sexiest Job of the 21st Century.

According to Forbes, IBM Predicts Demand For Data Scientists Will Soar 28% By 2020

GET FRONTLINE DATA SCIENCE TRAINING IN PUNE AT 3RI TECHNOLOGIES

Data Science is a trending niche, for it promises notable mileages for the business economy! It is rather ironic that data which was considered a burden to manage and store only about a few decades ago is now viewed as a resource; courtesy of course to data scientists. They have brought about a paradigmatic change through their skills which allow them to derive the value from raw data. It is important to mention that ‘Raw Data’ is clueless to most laymen, including the high echelons in the business management; but when processed through the Data Science Tools, it renders value which is precious and immense for the decision-makers and salesmen. They are all riding on the Professionalism of the Data Scientists and this generates the demand of the latter! 3RI Technologies is the leading institution offering Data Science Classes in Pune and fresh graduates as well as Working Professional can enroll for it.


WHAT IS DATA SCIENCE?

Today, Data Science is a much-talked subject and moreover, its significance is being deliberated among the business managers who are eager to hire a brilliant professional onboard their firm. Data Science is actually a milieu space that is shared by the distinct yet related domains of statistics & applicative mathematics, computer programming frameworks and tools, data metrics and analytics. Machine Learning & associated automation underpins all the above-listed fields, almost as a generic derivative; because it is through this channel that the good results are accrued in favor of the business clients. What are these good results? Let’s talk about them!

Trending smart services that are propelling the businesses around the world such as SEO, SMO, SMM, SEM and moreover CRM, all revolve around the ability to generate leads of authentic value for the commerce banners. The web developers have been doing well through their professional conduct for their clients but they in turn actively seek the ‘Meaningful Data’ pertaining to the existing and potential customers, the market trends and the competition figures of the biz rivals. Here, Data Science comes into play and serves to close the gaps. Data scientists offer their services as professionals and their task is to make productive use of the statistics, mathematics, analyses tools and programmed automation (like as mentioned above) to usher the leads that speak the meaning of practical worth! This meaning could be refined information on the probable customers, the dynamic trends and the like.

Data Science Classes in Pune at 3RI Technologies has been designed with the objective of developing in the candidates, the ability to master the professional techniques towards obtaining the best and desirable value for the companies.


WHY SEEK DATA SCIENCE TRAINING?

The above discussion is sufficient to throw light on the correlation that exists between the data professionals and the web developers, the business managers and all. A Data Scientist who possesses worthy skills is thus in high demand; and banners as reputed as Amazon, eBay and many more are eager to hire them at the earliest. Simply speaking, Data Scientists have become the direct determinants of business efficiency and profit optimization.

3RI Technologies is a Premier Institute offering well-structured Data Science Classes in Pune and it trains the candidates in the latest Data Science concepts apart from discussing the fundamentals and topics of core significance.

Career Growth in Data Science


THE PRACTICE AREAS OF DATA SCIENCE –

Data Science is an interrelated space of practice and warrants collective use of statistical models, data management, analytics and inference derivation for the business client (whose service niche and hence functional orientation could be really unique)! Broadly, you can delineate the following in Data Science matrix –

  • Big data This domain relates to the ability of the data professional to aggregate, through all possible sources, quality data (hence metrics) and also manage the same for productive analyses. Statistical models play a vital role here.
  • Data Analytics Here, the data scientist builds innovative programming models towards analyzing the raw data sets that could be unstructured, unrelated, dynamic, obsolete & redundant. Languages like Python are actively relied upon for designing smart data analytics models. Business Intelligence (BI) tools are embedded to ensure coherent resonances.
  • Programmed Automation The Data Scientist develops consistent algorithms (machine learning) that behave in automation to churn available data from designated sources and generate the demanded leads (like pertaining to marketing, sales, customer engagement, feedback and more). Such programmed automation helps the business client to get a regular supply of inference and insights of custom usage.

3RI Technologies conducts its Data Science Course in Pune every year and offers comprehensive Training that combines theoretical knowledge with ‘hands-on’ experience for the candidates. You learn as a team member from the real-time projects being undertaken at 3RI Technologies.

After taking this Data Science course, what kinds of jobs are there in Data Science?

Following completion of the training required for Data Science certification, the following are just a few of the numerous career choices in Data Science: 

Career Path

Description

Skills

Data Scientist

Evaluating and analyzing intricate datasets.

Data visualization abilities, machine learning, statistical analysis, and fluency in Python and R are all required.

Machine Learning Engineer

Constructing and implementing models for machine learning.

Algorithms for machine learning, model development and implementation, Python and related ML libraries mastery, and AWS SageMaker

Big Data Analyst

Large-scale dataset management and analysis.

Proficiency with SQL, AWS Glue, Athena, Redshift, and big data tools (Hadoop, Spark) are requirements for this role.

Cloud Data Engineer

Creating and putting into practice data storage solutions.

Creation of data pipelines, proficiency with database management, cloud storage (AWS S3), and familiarity with AWS services (Redshift).

Data Visualization Specialist

Making intelligent and interactive visualizations.

AWS Quicksight, design concepts, communication skills, and data visualization tools like Tableau and Power BI.

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Batch Schedule

Schedule Your Batch at your convenient time.

25-Feb-24 | SAT-SUN 8:00 AM to 10:00 AM

11-Mar-24 | MON-FRI 8:00 AM to 10:00 AM

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Will I Get Certified?

Upon successfully completing this program, you’ll earn a certificate.

The 3RI certification is accepted and respected by every significant multinational company across the nation. Fresh graduates and corporate trainees are eligible for the assistance. We offer certificate once the academic and practical courses have been finished. The certification that we offer here at 3RI is recognized across the country. The value of your resume will grow as a result. With the assistance of this qualification, you will be able to obtain prominent employment posts in the most successful multinational corporations in the country. The completion of our course as well as the projects that are based on practical application, are prerequisites for receiving the certificate.

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FAQs

Most frequent questions and answers

After the course completion, an exam will be conducted to judge your knowledge along with the live project work completion check, and you will be awarded a certificate from 3RI Technologies.

Yes, we provide placement assistance to our students. We have a dedicated team for Placement and tie ups with 300+ MNC’s and SME companies.

Yes, we conduct demo classes every weekend. Please contact us for more details.

We conduct comprehensive Case Studies for each Topic and algorithms. The trainer will suggest various approaches to Solve Data Science Problem. In this course, we would be conducting the live projects on domains like Financial Analytics, Logistics Analytics, Text Analytics.

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Data Science Course in Pune

Recent years have seen Pune overtake Mumbai as Maharashtra’s most populous city. The population density is 6,400 people per square kilometer, and 7.4 million people live there. Situated on the fringes of the Sahyadri Hills, Pune  is 560 meters above sea level and is flanked by the Deccan Plateau and the Western Ghats. Temperature extremes in the tropics are rare in Pune’s mild semiarid environment. Pune’s booming economy is largely attributed to the city’s thriving information technology sector and its world-class educational institute. The GDP of Pune is predicted to reach $69 billion by 2021. In addition to its rich history, the city also features numerous museums, zoos, and temples that visitors will find fascinating.

Some of Pune’s most visited landmarks are listed here.

3RI Technologies provide training for software courses like AWS, DevOps, AWS with DevOps, PowerBI, Tableau, Salesforce, Selenium, Java Full Stack, Oracle SQL/PLSQL, Data Science, Machine Learning, Artificial Intellegence.

Data Science Certification Training locations in Pune: Pune City, Aundh (411007), Gokhalenagar (411016), Kothrud (411029), Baner (411004), Shivajinagar (411005), Parvati (411009), Kondhwa (411048), Navsahyadri (411052), Chatursringi (411053), Pimpri Chinchwad (411078), Pimple Gurav (411061), Pimple Nilakh (411027), Pimple Saudagar (411027), Pimple Khed (411017), Pimple Jagtap (411061), Rahatani (411017), Wakad (411057), Balewadi (411045), Vishal Nagar (411027), Thergaon (411033), Shivaji Nagar (Pimpri-Chinchwad) (411017), Sangvi (411027), Jagtap Dairy (411027)

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