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PG Program in Data Science, Machine Learning & Neural Networks in collaboration with IBM In Pune

Become an industry-ready Certified Data Science professional through immersive learning of Data Analysis and Visualization, ML models, Forecasting & Predicting Models, NLP, Deep Learning and more with this Job-Assured Program

Program Overview

Best online data science program in Pune and across the country as well. Get trained with highly in demand tools, techniques, & technologies for Data Science.

Key Highlights

  • 6 Months Internship Part of the Data Science Program6 Months Internship Part of the Program
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PG Program in Data Science, Machine Learning & Neural Networks in collaboration with IBM In Pune

  1. 140000
    • Best online Data science course
    • Best online Data science course
    • Best online Data science course
    • Best online Data science course
    8500+ learners
Features
  • 300+ hours of learning
  • Practice Test Included
  • Certificate of completion
  • 6 Domain Specializations

Languages and Tools covered

  • Excel in Data Science program - online excel course
  • python in Data Science Program - online python course
  • tableau in Data Science program - online tableau course
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11 Months PG Program in Data Science, Machine Learning & Artificial Intelligence in collaboration with IBM

Get eligible for 4 world-class certifications thus adding that extra edge to your resume.

  • Learning paths and certification from IBM
  • Course completion certificate from DataTrained Education
  • Project completion certificate from DataTrained Education
  • Internship Certificate from Partner Companies

What’s the focus of this course?

Choose from 6 specializations,receive industry mentorship,dedicated
career support, learn 14+ programming tools & languages & much more

6 Unique Specializations- data science programs near me

6 Unique Specializations

Choose from 6 specializations as per your background & career aspirations. Get an Executive Certification In Data Science, Machine Learning & Neural Networks In Collaboration With IBM

Dedicated Career Assistance- data science program institute

Dedicated Career Assistance

Receive 1:1 career counseling sessions & mock interviews with hiring managers. Exhilarate your career with our 400+ hiring partners.

Student Support -  data science online training

Student Support

Chat support for Quick Doubt Resolution is available from 06 AM to 11 PM IST. Program Managers are available on call, chat and ticket during business hours.

Instructors

Join DataTrained – IBM- certified curriculum and learn every skill from the industry’s best thought leaders.

Dr. Deepika Sharma - Training Head, DataTrained
Dr. Deepika Sharma
Training Head, DataTrained

Dr. Deepika Sharma has been associated with academics /corporate education for more than 10 years. She has a deep passion in the field of Artificial Intelligence, Data Science, and Machine Learning.

Shankargouda Tegginmani - Data Scientist, Accenture
Shankargouda Tegginmani
Data Scientist, Accenture

Shankar is a data Scientist with 14 Years of Experience. His current employment is with Accenture and has experience in telecom, healthcare, finance and banking products.

Sanket Maheshwari - Data Scientist, Faasos
Sanket Maheshwari
Data Scientist, Faasos

Experienced Data Scientist with a demonstrated history of working in the information technology and services industry.

Polong Lin - Business Analyst, IBM
Polong Lin
Business Analyst, IBM

Polong Lin is a Data Scientist at IBM in Canada. Under the Emerging Technologies division, Polong is responsible for educating the next generation of data scientists through BDU.

Jay Rajasekharan- Data Scientist, IBM
Jay Rajasekharan
Data Scientist, IBM

Currently, he is driving several productivity programs - using data analytics to drive insights from business operations and implementing optimizations such as streamlining workflows, improving service levels, and ultimately reducing cost.

Mahdi Noorian- Data Scientist, IBM
Mahdi Noorian
Data Scientist, IBM

Mahdi Noorian is a Postdoctoral Fellow at the Laboratory for Systems, Software and Semantics (LS3) of the Ryerson University. He holds a Ph.D degree in Computer Science from University of New Brunswick.

SYLLABUS

Best-in-class content by leading faculty and industry leaders in the form of live sessions, pre-recorded videos, projects, case studies, industry webinars, and assignments.

best data science courses online

Syllabus

Module 1 Foundations

The Foundations bundle comprises 2 courses where you will learn to tackle Statistics and Coding head-on. These 2 courses create a strong base for us to go through the rest of the tour with ease.

This course will introduce you to the world of Python programming language that is widely used in Artificial Intelligence and Machine Learning. We will start with basic ideas before going on to the language's important vocabulary as search phrases, syntax, or sentence building. This course will take you from the basic principles of AI and ML to the crucial ideas with Python, among the most widely used and effective programming languages in the present market. In simple terms, Python is like the English language.

Python Basics

Python is a popular high-level programming language with a simple, easy-to-understand syntax that focuses on readability. This module will guide you through the whole foundations of Python programming, culminating in the execution of your 1st Python program.

Anaconda Installation - Jupyter notebook operation

Using Jupyter Notebook, you will learn how to use Python for Artificial Intelligence and Machine Learning. We can create and share documents with narrative prose, visualizations, mathematics, and live code using this open-source online tool.

Python functions, packages and other modules

For code reusability and software modularity, functions & packages are used. In this module, you will learn how you can comprehend and use Python functions and packages for AI.

NumPy, Pandas, Visualization tools

In this module, you will learn how to use Pandas, Matplotlib, NumPy, and Seaborn to explore data sets. These are the most frequently used Python libraries. You'll also find out how to present tons of your data in simple graphs with Python libraries as Seaborn and Matplotlib.

Working with various data structures in Python, Pandas, Numpy

Understanding Data Structures is among the core components in Data Science. Additionally, data structure assists AI and ML in voice & image processing. In this module, you will learn about data structures such as Data Frames, Tuples, Lists, and arrays, & precisely how to implement them in Python.

In this module, you will learn about the words and ideas that are important to Exploratory Data Analysis and Machine Learning. You will study a specific set of tools required to assess and extract meaningful insights from data, from a simple average to the advanced process of finding statistical evidence to support or even reject wild guesses & hypotheses.

Descriptive Statistics

Descriptive Statistics is the study of data analysis that involves describing and summarising different data sets. It can be any sample of a world's production or the salaries of employees. This module will teach you how to use Python to learn Descriptive Statistics for Machine Learning.

Inferential Statistics

In this module, you will use Python to study the core ideas of using data for estimating and evaluating hypotheses. You will also learn how you can get the insight of a large population or employees of any company which can't be achieved manually.

Probability & Conditional Probability

Probability is a quantitative tool for examining unpredictability, as the possibility of an event occurring in a random occurrence. The probability of an event occurring because of the occurrence of several other occurrences is recognized as conditional probability. You will learn Probability and Conditional Probability in Python for Machine Learning in this module.

Hypothesis Testing

With this module, you will learn how to use Python for Hypothesis Testing in Machine Learning. In Applied Statistics, hypothesis testing is among the crucial steps for conducting experiments based on the observed data.

Module 2 Machine Learning

Machine Learning is a part of artificial intelligence that allows software programs to boost their prediction accuracy without simply being expressly designed to do so. You will learn all the Machine Learning methods from fundamental to advanced, and the most frequently used Classical ML algorithms that fall into all of the categories.

With this module, you will learn supervised machine learning algorithms, the way they operate, and what applications they can be used for - Classification and Regression.

Linear Regression - Simple, Multiple regression

Linear Regression is one of the most popular Machine Learning algorithms for predictive studies, leading to the very best benefits. It is an algorithm that assumes the dependent and independent variables have a linear connection.

Logistic regression

Logistic Regression is one of the most popular machine learning algorithms. It is a fundamental classification technique that uses independent variables to predict binary data like 0 or 1, positive or negative , true or false, etc. In this module, you will learn all of the Logistic Regression concepts that are used in Machine Learning.

K-NN classification

k-Nearest Neighbours (Knn) is another widely used Classification algorithm, it is a basic machine learning algorithm for addressing regression and classification problems. With this module, you will learn how to use this algorithm. You will also understand the reason why it is known as the Lazy algorithm. Interesting Right?

Support vector machines

Support Vector Machine (SVM) is another important machine learning technique for regression and classification problems. In this module, you will learn how to apply the algorithm into practice and understand several ways of classifying the data.

We explore beyond the limits of supervised standalone models in this Machine Learning online course and then discover a number of ways to address them, for example Ensemble approaches.

Decision Trees

The Decision Tree algorithm is an important part of the supervised learning algorithms family. The decision tree approach can be used to resolve regression and classification problems unlike others. By learning simple decision rules inferred from previous data, the goal of using a Decision Tree is constructing a training type that will be used to predict the class or value of the target varying.

Random Forests

Random Forest is a common supervised learning technique. It consists of multiple decision trees on the different subsets of the initial dataset. The average is then calculated to enhance the dataset's prediction accuracy.

Bagging and Boosting

When the aim is to decrease the variance of a decision tree classifier, bagging is implemented. The average of all predictions from several trees is used, that is a lot more dependable than a single decision tree classifier.

Boosting is a technique for generating a set of predictions. Learners are taught gradually in this technique, with early learners fitting basic models to the data and consequently analyzing the data for errors.

In this module, you will study what Unsupervised Learning algorithms are, how they operate, and what applications they can be used for - Clustering and Dimensionality Reduction, and so on.

K-means clustering

In Machine Learning or even Data Science, K-means clustering is a common unsupervised learning method for managing clustering problems. In this module, you will learn how the algorithm works and how you can use it.

Hierarchical clustering

Hierarchical Clustering is a machine learning algorithm for creating a bunch hierarchy or tree-like structure. It is used to group a set of unlabeled datasets into a bunch in a hierarchical framework. This module will help you to use this technique.

Principal Component Analysis

PCA is a Dimensional Reduction technique for reducing a model's complexity, like reducing the number of input variables in a predictive model to avoid overfitting. Dimension Reduction PCA is also a well-known ML approach in Python, and this module will cover all that you need to know about this.

DBSCAN

Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to identify arbitrary-shaped clusters and clusters with sound. You will learn how this algorithm will help us to identify odd ones out from the group.

Module 3 Advanced Techniques
EDA - Part1

Exploratory Data Analysis (EDA) is a procedure of analyzing the data using different tools and techniques. You will learn data standardization and represent the data through different graphs to assess and make decisions for several business use cases. You will also learn all the essential encoding techniques.

EDA - Part2

You will also get a opportunity to use null values, dealing with various data and outliers preprocessing techniques to create a machine learning model.

Feature Engineering

Feature Engineering is the process of extracting features from an organization's raw data by using domain expertise. A feature is a property shared by independent units that can be used for prediction or analysis. With this module, you will learn how this works.

Feature Selection

Feature selection is also called attribute selection, variable selection, or variable subset selection. It is the process of selecting a subset of relevant features for use in model development. You can learn many techniques to do the feature selection.

Model building techniques

Here you will learn different model-building techniques using different tools

Model Tuning techniques

In this module, you can learn how to enhance model performance using advanced techniques as GridSearch CV, Randomized Search CV, cross-validation strategies, etc.

Building Pipeline

What is Modeling Pipeline and how does it work? Well, it is a set of data preparation steps, modeling functions, and prediction transform routines organized in a logical order. It allows you to specify, evaluate, and use a series of measures as an atomic unit.

Module 4 Time Series Analysis
Introduction

A time series is a set of data points that appear in a specific order over a specific time. A time series in investing records the movement of selected data points, like the cost of security, with a set period of time, with data points collected at regular intervals.

Time Series Components

In this module, you will learn about different components that are necessary to analyze and forecast future outcomes.

Stationarity

You will learn what is stationarity and the importance of learning stationarity.

Time Series Models

In this module, you will learn common Time series models as AR, MA, ARIMA, etc.

Model Evaluation

When you build models, you will use different evaluation methods to gauge the product performance or even accuracy. Yes, In this module, you will learn model evaluation methods.

Use Case and Assignment

You will also get a chance to work on assignments and feel at ease while working on the use case scenarios.

Projects

Also, we are providing a few more extra projects for practice, you can assemble and compare your solutions with the ones we provide.

Module 5 Recommendation Engine
Introduction

In the introduction module, you will learn why recommendation systems are used, their requirement, and their applications.

Understanding the relationship

In this module, you will learn on what basis recommendation engine works and their association rules.

Types of Data in RS

In this module, you will learn all the types of data used in the Recommendation Engine.

Ratings in RS

In this module, you will learn just how the ratings are drawn in the Recommendation Engine.

Similarity and Its Measures

Recommendation systems work on the basis of similarity between the product and the consumers who view it. There are many ways for determining how similar 2 products are. This similarity matrix is used by recommendation systems to recommend the next most comparable product to the customer.

Types of Recommendation Engine

In this module, you will learn different types of Recommendation Engines.

Evaluation Metrics in Recommendation

Once you build the models, you require metrics to evaluate how effective is your model. You will learn various evaluation tools in RE.

Use cases

You will also get an opportunity to focus on additional use cases. Later, you can compare your solution with the SME-provided solution.

Module 6 Introduction to Deep Learning

In this introduction module, we look at the different components of a neural network, starting with adopting the phrases of Neural Networking. Install and familiarize yourself using the TensorFlow library, enjoy Keras' simplicity, then use Keras to create a strong neural network model for a classification problem. Also, you will learn how to fine-tune a Deep Neural Network.

Practical case of MLP

A multi-layer perceptron is a mathematical model of a biological neuron or an artificial neuron. A neural network is a computing system based on the human brain's organic neural networking. In this module, you will learn about all of the neural network's uses and perception.

Practical case of MLP

A multi-layer perceptron is a mathematical model of a biological neuron or an artificial neuron. A neural network is a computing system based on the human brain's organic neural networking. In this module, you will learn about all of the neural network's uses and perception.

Tensor Flow & Keras for Neural Networks and Deep Learning

TensorFlow is an open-source library for numerical computing and machine learning that was introduced by Google. Keras is a robust open-source API for building & evaluating deep learning models. In this module, you will learn how to set up Keras and TensorFlow from the starting. In Python, these libraries are often used for AI & ML.

Activation and Loss functions

In this module, you will learn how the Activation Function is used in defining a neural network's paper from many inputs. The Loss Function is a technique for predicting neural community error.

Convolution neural networks

A Convolutional Neural Community (CNN) is a kind of artificial neural network. In this module, you will learn about image recognition and processing that is specially developed to process pixel data.

Practical Cases of CNN in image classification

You will get an opportunity to work on use cases of image classification and learn how CNN will work behind the scenes.

Transfer Learning

Transfer learning is a deep learning research technique that focuses on storing and transferring knowledge received while training one model to another.

Implementing Object Detection

In this module, you will learn about how object detection models are built.

Segmentation using CNNs

Each pixel in an image is labeled with a unique class in image segmentation. Dense prediction is another name for this pixel labeling problem. In this module, you will learn how image segmentation is performend.

AutoEncoders

A neural network model called autoencoder is designed to master a compressed representation of the input. A neural network that has been taught to replicate the input to its output is called as an autoencoder.

Sequence Based Model

The sequence based model accepts a sequence of objects (words, time series, characters, etc.) and develops another sequence. Model Seq2Seq. The input is a sequence of words, and the output is the translated series of words in the Neural Machine Translation.

Projects

In this module, you will also get an opportunity to work on multiple models.

Module 7 Intoduction to NLP

Natural Language Processing is a part of computational linguistics that is used to develop real-world applications that work with languages of different structures. With appropriate algorithms, you will learn how to educate the computer to learn languages and then expect it to fully understand them. This system will take you through a introduction of NLP and all of the main components.

Applications of NLP

In this module, you will learn different applications that use NLP

Text Preprocessing

You will learn how to preprocess text using NLP tools.

Hands on Parts of Speech (POS)

When you are dealing with languages it is normal that we will come across parts of speech. You will learn how you can tag POS in this module.

Regular Expressions Introduction

You may observe while reading an article or a newspaper that, between the words, you will find several regular expressions too. In this module, you will learn how to deal with regular expressions.

Semantic Processing

The technique of comprehending natural language the way humans communicate based on meaning and context is known as semantic analysis. Semantic technology analyses the logical structure of phrases to find the most important parts in a text and comprehend the subject at hand.

Deep Learning for Deep NLP

You can learn the introduction of deep learning for NLP. In case you are familiar with this already, it will be a refresher.

Introduction on Co- Occurrence matrix

A co-occurrence matrix will include certain entities in rows (ER) and column (CC) (EC). The aim of this matrix is to show how many times each ER and each EC occur in the very same context.

Word Embeddings

Word embedding is a term used in natural language processing (NLP) to summarize the representation of words for text analysis, which is in the form of a real-valued vector that encodes the meaning of the word and predicts the meaning of words that are close in the vector area.

LSA Introduction

Latent Semantic Analysis (LSA) is a mathematical process that is used to get insight into components. Topic Modeling is based on this method. The basic idea is to divide a matrix of what we've - terms and documents - into 2 distinct document topic and topic-term matrices.

SkipGram Intro

Skip-gram is a kind of unsupervised learning technique for finding the most similar words for a specified word. Skip-gram is a strategy for predicting the context word for a target word.

Word2Vec Intro

Word2vec is a natural language processing technique. The word2vec method learns word connections from a huge corpus of text using a neural network model. Once trained, this can recognize synonyms and propose extra words for a sentence.

Glove Hands on

GloVe (Global Vectors for Word Representation) is also a method of creating word embeddings. It is based on word context matrices and matrix factorization methods. And then, we factorize this matrix to get a lower-dimensional matrix with every row corresponding to a vector representation of every word.

Project on Text Classification

You will learn to work on text classification projects. For example, How to find if an email is spam or not.

Introduction to Sentiment Analysis

You will learn to work on a Sentiment analysis project. For example, How is the movie? Did the customer like the product? etc.

Module 8 Tableau
Getting Started

Tableau is a visual platform for business intelligence and analytics which allows users to monitor, comprehend, observe, and make choices with a range of data. It allows you to produce any kind of graph, plot, or chart without scripting.

Data handling & summaries

You can learn how to handle various types of data, plot, analyze, sort, and summarise the entire data.

Building Advanced Reports/ Maps

Tableau Reports are a built-in feature of Tableau that allows users to display data from different sources into reports to better comprehend how far they have progressed toward their goals, better understand their customers' needs, and forecast future plans.

Calculated Fields

You will use calculated fields to generate new data from existing data in your data source. When you build a calculated field in your data source, you are efficient in including a new field (or column) whose values or members are determined by a computation that you control.

Table calculations

Table computations are modifications that can be applied to the data in a display. They are a kind of calculated field that works with Tableau's local data based on what is already in view.

Parameters

Parameters let you change a reference line, band, or box dynamically. For instance, reference a parameter rather than a reference line at a defined position on the axis. The reference line could be moved using parameter control.

Building Interactive Dashboards

An interactive dashboard allows you to dive down and filter operational data, allowing you to see data from multiple angles or in more depth. Dashboards allow data-driven business choices by providing a simplified and clear overview of overall business information.

Building Stories

A narrative in Tableau is a set of visuals that work together to present data. You can use tales to convey a data story, offer context, display how steps affect results or make a strong argument.

Working with Data

In this module, you will learn to join the tables, data blending and make connections, etc.

Sharing work with others

In this module, you will learn to share Workbooks, Publish to Reader/PDF and Publish to Tableau Server and share on the web.

Module 9 Power BI
Discover Data Analysis in Power BI

Power BI is an analytics tool that enables business users to analyze data and distribute insights throughout the organization at all levels. Through interactive and easy dashboards, Power BI offers an end-to-end view of important metrics and key performance indicators, all in real-time and in one place.

Get Started Building with Power BI

In this module, you will learn how to find Power BI services and apps that interact. Investigate how Power BI can help you to run your business more effectively, and figure out how to make eye-catching graphics and reports.

Get Data In Power BI

You will use Power BI to connect to data in text-based files, third-party directories, Microsoft Azure directories, along with web services like Google Analytics and SalesForce, among others. You will learn about how you can get the data in Power BI.

Clean, Transform and Load data in Power BI

Power Query offers a series of capabilities designed to help you in cleaning and preparing your data for analysis. For deeper analytics, you will learn ways to simplify a complicated model, alter data types, and rename items.

Design a Data Model In Power BI

It is about simplifying the chaos with regard to creating an outstanding data model. In this module, you will find out about the vocabulary and implementation of star schemas, which is one technique to simplify a data type. You will also learn why selecting the right data granularity is vital for Power BI report efficiency and readability.

Visualization in Power BI

Visualizations are representations of data insights. A Power BI report may have a single page with a single graphic or a lot of pages with multiple visuals. Visuals from reports can be connected to dashboards in the Power BI service.

Introduction to Creating Measures using DAX in Power BI

Data Analysis Expressions (DAX) is a scripting language that is used to create custom tables, measurements, or calculated columns in Microsoft Power BI. It is a set of capabilities, operators, and also constants that can be used to calculate and return one or more values using a formula, or expression.

Create dashboards in Power BI

Dashboards enable report users to make a single object of directed data that is created for particular requirements. Dashboards could be made up of different graphics pulled from various reports.

On Premises Data Gateway Management

Each service combines gateways differently and administration choices can change. This module pertains to the manage gateways page in Power BI, also, you will deal with gateways from any service.

Implement Row-Level Security

With Power BI, Row-Level Security (RLS) can be used to restrict data access for users. You can add filters inside roles to limit data access at the row level. In this module, you will learn how to set up RLS.

Work with AI Visuals in Power BI

With Power BI, row-level security (RLS) can be used to limit data access for users. You can build filters inside roles to restrict data access at the row level. With this module, you will learn how to set up RLS.

Create Paginated Reports

Paginated reports, as the name implies, can span many pages. They are formatted in a certain way and allow for exact modification. They are used to create paginated reports. The Power BI Report Server web portal, like the SQL Server Reporting Services (SSRS) website, allows you to save and control paginated reports.

Case Study

You will also get a chance to focus on multiple use cases that will enable you to improve your analytical.

Comprehensive Curriculum

The curriculum has been designed by faculty from IITs, IBM and Expert Industry Professionals.

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Electives

Strong hand-holding with dedicated support to help you master some of the complex processes of Data Science and Artificial Intelligence.

Deep Learning with Computer Vision

  • NLP with ML
  • Deep Learning
  • Computer Vision
  • Business Analytics with Tableau

Deep Learning with NLP

  • NLP with ML
  • Deep Learning
  • Deep NLP
  • Business Analytics with Tableau

Business Analytics with R

  • NLP with ML
  • Business Analytics with "R" programming and R shiny
  • Business Analytics with Tableau
  • Business Analytics with Advanced Excel

Business Analytics with Tableau

  • NLP with ML
  • Deep Learning
  • Business Analytics with Power BI
  • Business Analytics with Tableau

Business Analytics with SAS

  • NLP with ML
  • Business Analytics with Tableau
  • Business Analytics with PowerBI
  • Business Analytics with SAS

Data Engineering with Big Data

  • NLP with ML
  • Business Analytics with Tableau
  • Full stack Bigdata

Industry Projects

Learn through real-life industry projects sponsored by top companies across industries

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6 month practical internship

6 Months internship ensures you graduate as an experienced data science professional rather than a fresher. You can go for an online internship along with your current job.

Resume pepration by DataTrained

Partnered with IIMJobs wherein you get access to their paid resume preparation kit and personal feedback from the industry HR experts. An individual career profile is prepared by our experts so that it suits his/her experience and makes it relevant to a Data Scientist role.

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Regular mock HR and Technical interviews by mentors with personal guidance and support. The industry mentor helps students to take projects on Kaggle and move on to the status bar so that their resume looks competitive to the recruiters.

100% granted placement

We generate the Ability Score of every individual which is then sent to our more than 400+ recruitment partner organizations. At last, we organize campus placements every three months in Noida, Gurgaon, Ahmedabad, Bangalore, and Chennai to place our students.

Career Impact

DataTrained in collaboration with IBM presents the best online Data Science Program in Pune. Over 5000 Careers Transformed.

DataTrained has helped me with the vital knowledge and skills that are needed for a data scientist role. The trainer starts with an example to make us comprehend the concept and then help us build the Algorithms with the real industry datasets.DataTrained brings the power of online learning along with dedicated Mentorship, Counselling, Live Sessions and 6 months Internship.

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6 LPA
Indium Software

After my graduation, I didn't want to pursue MBA since everyone is doing it I wanted to do something different but I was confused. I opted for the PG Program in Data Science by Data Trained Education and I had an amazing journey with them, the trainers were top-notch, the course content was perfect.

Hemant Patar Placed at Maganti IT services--- DataTrained Placement
Hemant Patar
Maganti IT services

I can certainly say the content they are offering is really good. Assignments are relatable. Completing the assignments helps in a better understanding of the module. In a nutshell, I would recommend this course to anyone interested in Data Science.

Amandeep Placed at Analytics Vidya-- DataTrained Placement
Amandeep
Analytics Vidya
Kumar Vaibhav Placed at HCL -- DataTrained Placement
Kumar Vaibhav

HCL

12 LPA
HCL
Abhinav Placed at MSMEx ---DataTrained Placement
Abhinav

MSMEx

6 LPA
MSMEx
Prathamesh Mishtry Placed at Deqode Solutions-- DataTrained Placement
Prathamesh Mishtry

Deqode Solutions

5.35LPA
Deqode Solutions
Abhistha Chatterjee Placed at Quantiphi --DataTrained Placement
Abhistha Chatterjee

Quantiphi

5 LPA
Quantiphi

Admission Process

There are 3 simple steps in the Admission Process which is detailed below

Step 1: Fill in a Query Form

Fill up the Query Form and one of our counselors will call you & understand your eligibility.

Step 2: Get Shortlisted & Receive a Call

Our Admissions Committee will review your profile. Upon qualifying, an Email will be sent to you confirming your admission to the Program.

Step 3: Block your Seat & Begin the Prep Course

Block your seat with a payment of INR 10,000 to enroll in the program. Begin with your Prep course and start your Data Science journey!

Program Fee

₹ 140000 ($2,200) + 18% GST

No Cost EMI options are also available. *

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What's Included in the Price

Placements

Access to real-life 40 industry projects

6 Months online Internship part of the core curriculum

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Data Science Trends in Pune

The latest trends in data science suggest that a new viewpoint is taking shape. Data is no longer a science reserved for a select group of specialists but has instead become an invaluable opportunity for every professional within a business to improve and refine their practice. The demand for data scientists in Pune is at an all-time high, which is resulting in an upward career arc and generous pay from companies. 2021 and the next few years are going to be very exciting for the businesses and teams adopting data science

Jobs/Vacancies in the field of Data Science

India is witnessing the rapid digitization of businesses and services, making it the second-largest hub for data science in the world. Around 93,000 jobs in Data Science were vacant at the end of August 2021 in India. 70% of these vacancies were for positions with less than five years of experience. Transitioning to data science is a smart move as it fetches far higher comparative returns.

Salary in Pune of Data Science Professionals

The average salary for a data scientist is 7 lakh per year. When you earn Your PG certificate, you can expect anywhere from 13 to 25% boost in your annual salary. Thus, investing just 4-5 hours weekly of one’s time to gain specialisation in Data Science and Machine Learning gives compounded returns, in the long run, allowing professionals to expand their career horizons.