Data Science Course in Pune

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Data Science Training in Pune

Data Science Training and courses for beginners at Ethans Tech Pune is the stupendous program containing a variety of Data Analytics and Data Science Training techniques. Our Data Science Training in Pune is truly outstanding, both in terms of content and the delivery provided by our world-class faculty. Data Science classes masters important like Data Science concepts such as Data Preprocessing, Exploratory Data Analytics, Data handling Techniques, Statistics, Algebra, maths, Machine Learning algorithms include regression, classification, and clustering. The Data Science course in Pune Assists individuals to get ready by working on real-time-case studies and equipping them to work independently on relevant projects.

Lectures: 100 | Duration: 175+ hours | Mode of Training: Classroom & Online

Register to confirm your seat. Limited seats are available.

About the Course

Learn from Industry Certified Professionals

Since an inauguration day of Ethans Tech, we helped thousands of individuals to become the job-ready on a highly demanded skill in the IT industry i.e. Data Science. Our data science course is crisp and contains many projects which really help the attendees to get sufficient knowledge to crack any interview they applied for. Our adept Trainers are providing expert training on either Weekends or Weekdays. Ethans Tech is a leading Coaching center in Pune having a great track record providing a solid grip on Data Science from scratch.

Data Science Course is designed by industry mentors from various MNC’s, after many rounds of discussion we came up with a comprehensive data science syllabus which completely focused on practical and project-based learning. Data Science training in Pune provides an end to end understanding of technology and helps students to build a great foundation on the subject. Attendees will be prepared with interview questions from day1 and it will help to crack Data Science interviews and possess advanced knowledge of data science concepts.

What is Data Science? Who is a Data Scientist?

Let’s start discussing the Data science course in Pune with “What is Data Science”?

Data Science is the process of analyzing and interpreting the hidden patterns, insights, and trends which are encrypted inside the data.  Data Science can be interpreted as the study of data which is generated from a variety of sources, and how this information can be turned into a piece of valuable information that can fuel the decision-making process in business.

Who is Data Scientist? A Data Scientist is a professional who works extensively with raw, structured, and unstructured data to derive valuable business insights from it. A data scientist uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from many structural and unstructured data.

A Data Scientist enhances business decision making by introducing greater speed and better direction to the entire process with the help of their data visualization capabilities. Compared to data analysts, data scientists are much more technical and possess expertise in at least one programming language – R/Python, data extraction, data wrangling, data transformation, and loading capabilities.

Top Data Science Jobs for the year 2020 compiles of Data Scientist, Machine Learning Engineer, Machine Learning Scientist, Artificial Intelligence, Data Architect, Data Engineer, Data Analyst with an average of INR 700,000 salary.

Why one should take Data Science Course?

“The job of a data scientist has only grown sexier,” said Andrew Flowers, an economist at Indeed, based in Austin, Texas. “More employers than ever are looking to hire data scientists.”

With the surge in data (Calling Big Data) and its correlated fields, the job of a Data Scientist has become the most sought after job. To handle vast amounts of data produced every day, enterprises need professionals who can treat, analyze, and organize this data to provide valuable business insights, for intelligent actions. Data Science has emerged as the most promising field in recent times.

 The demand for data scientists is only increasing and will continue to increase in the future. According to IBM, an increment between 200,000 to 600,000 openings will be generated in the year 2020. This demand will only grow further to an astonishing 700,000 openings.

According to Glassdoor, Data Scientist is the number one job on its website. This position will remain unchanged in the future. The requirement for the number of data scientists is growing at an exponential rate due to the increase in data and its various types. The number of roles and data scientists will only increase in the future. Some of the positions in data science such as data engineer, data science manager & big data architect. Moreover, the financial, telecom, retail, and insurance industries are becoming major players for recruiting data scientists.

The January report from Indeed, one of the top job sites, showed a 29% increase in demand for data scientists year over year and a 344% increase since 2013 — a dramatic upswing. But while demand — in the form of job postings — continues to rise sharply, searches by job seekers skilled in data science grew at a slower pace (14%), suggesting a gap between supply and demand and the site considers data science a “high-demand skill.”.

Accessibility of the data today can help organizations to reap multiple benefits from it. Because of this, companies are not shying away from offering increased data scientist salary in India. Companies are offering huge salaries at those having skills to take on the positions of Data Analysts, Scientists, Engineers, etc. India is the second-highest country to recruit employees in the field of data science or data analytics, etc. with 50,000 positions available – second only to the United States.

What is the scope of Job Opportunities With Data Science Training?

Career opportunities in data have exponentially grown in the recent few years. India is the second-highest country to recruit employees in the field of data science or data analytics, etc. with 50,000 positions available.

If you have completed the certification course in data science from EthansTech, your career as a data scientist is expected to grow onwards and upwards.

The different job roles in Data Science which you can apply after the completion of your Data Science training certification are

  • Data Scientist– Gathering vast amounts of structured and unstructured data and converting them into actionable insights. An encouraging data-driven approach to solving complex business problems.

Average salary:  Rs 7,00,000 per annum

  • Data Engineer- The primary job of a Data Engineer is to design and engineer a reliable infrastructure for transforming data into such formats as can be used by Data Scientists. In an organization, the position of a Data Engineer is as vital as that of a Data Scientist.

Average Salary:  Rs 1,000,000 per annum

  • Data Analyst- Data Analysts are professionals who translate numbers, statistics, figures, into plain English for everyone to understand.

Average Salary:  Rs 6,00,000 per annum

By completing the Certification Course with EthansTech you may likely receive an annual bump up of around 15% in your compensation. This will further increase with an increase in the years of work experience and the number of skills you’ve mastered.

Why Choose Ethans for Data Science ?

This Data Science Training In Pune is an ideal choice for all the analytics career enthusiasts who are planning to secure their careers in Data Science. As the course complements the present industry requirements, both fresher’s & working professionals who are looking towards a career shift from their existing technologies to Data Science can get enrolled for our Data Science Course In Pune. If you are new to programming & stats then there’s nothing to worry about, we have got you covered. Our Data Science training will cover the concepts right from the scratch. You will learn the basics of Statistics, Maths, SQL, EDA, Statistical analysis, Python programming to advanced AI, Machine Learning, Business Analytics & Predictive Analytics, Text Analytics and more.

Our Data Science training is the best fit for

  • Managers
  • Data analysts
  • Business analysts
  • Database Administrators
  • Networking Operators
  • Professional whats to change their career path
  • Legacy Technologies Professional
  • IT Developers & Software Professionals
  • Job Seekers
  • Freshers/Graduates
  • End users

With the increasing demand for big data analytics, Data Science has become the key technology & the major buzz word across the IT & Corporate domain. So, this is the right time to step into this technology. Throughout this Data Science Training In Pune program, our expert trainers & mentors will be extending their full support to the participants. With a coordinated effort in our Data Science Training, we will be working towards transforming our students into complete career ready Data Scientists


Data Science Training Content

Introduction to Computer Programming (Python)

  • Programming Foundation Concepts: Basic Terminology, Data Types and Variables, Operators and Expressions, Control Structures, Functions and Procedures, Recursion, Basic Algorithms
  • Object-Oriented Concepts: Classes and Objects, Inheritance, Polymorphism, Encapsulation, Abstraction, Method Overriding, Constructor and Destructor
  • Getting Started with Python: History of Python, Python Features, Python 2 vs Python 3, Installing Python, Running Python Scripts, Python Syntax and Semantics, Python Development Environments
  • Python Installation and Setup: Python 3.X Installation Guide, Introduction to Anaconda Python, IDEs: Jupyter Notebook, PyCharm, and VS Code, Setting Up Virtual Environments, Package Management with pip and conda
  • Python Built-in Objects: Numbers (int, float, complex), Strings, Lists, Tuples, Sets, Dictionaries, Type Conversion
  • Control Flow in Python: Conditional Statements (if, elif, else), Looping Statements (for, while), Nested Loops, Loop Control Statements (break, continue, pass), Comprehensions (List, Dict, Set), Conditional Expressions
  • Functions in Python: User Defined Functions, Lambda Functions, Function Arguments (default, keyword, arbitrary), Return Statement, Recursion, Built-in Functions, Higher-Order Functions
  • String Handling in Python: String Creation and Operations, String Methods, String Formatting, Regular Expressions, String Slicing, Escape Characters, Unicode Strings
  • Data Structures in Python: List Operations and Methods, Dictionary Operations and Methods, Set Operations and Methods, Tuple Operations and Methods, Working with Stacks and Queues, Comprehensions, Collections Module
  • File Handling in Python: Reading and Writing Files, File Methods, File Modes, Handling Binary Files, Working with CSV Files, Context Managers, File Path Operations
  • Python Libraries and Modules: Standard Library Overview, Commonly Used Libraries (os, sys, math, datetime), Importing Modules, Creating User Defined Modules, User Defined Packages, Working with, Packaging and Distribution (whl Packaging)
  • Advanced Python Concepts: Object Oriented Python, Exception Handling, Debugging Techniques, Iterators and Generators, Decorators, Context Managers, Metaprogramming
  • Database Interface with Python: Introduction to Databases, SQLite in Python, CRUD Operations, Connecting to Sqlite, Database Connection Pooling, Handling Transactions
  • Working with Data Formats: Introduction to JSON Data, Parsing JSON, Creating JSON, Working with XML, Using CSV Module, pickle Module for Serialization, YAML Format
  • Web Programming with Python: Requests Module, Web Scraping with BeautifulSoup, Using Scrapy for Advanced Scraping, Working with APIs
  • Numerical Computation with Numpy: Introduction to Numpy, Numpy Arrays, Array Indexing and Slicing, Array Operations, Broadcasting, Numpy Functions, Working with Matrices
  • Data Manipulation with Pandas: Introduction to Pandas, Series and DataFrame, Reading and Writing Data, Data Cleaning, Data Transformation, Merging and Joining, Data Aggregation

Introduction to RDBMS Database (SQL)

  • Introduction of RDBMS: Definition of RDBMS, Characteristics of RDBMS, Advantages of using RDBMS, Data Models in RDBMS, Keys in RDBMS (Primary Key, Foreign Key), RDBMS vs. NoSQL, Popular RDBMS Software
  • Installing and Using MySQL: System Requirements for MySQL, Downloading and Installing MySQL, Configuring MySQL Server, MySQL Workbench Overview, Connecting to MySQL Server, Basic MySQL Commands, MySQL User Management
  • Querying Single Table: SELECT Statement Basics, Filtering Data with WHERE Clause, Sorting Data with ORDER BY Clause, Using LIMIT to Restrict Rows, Aggregation Functions (COUNT, SUM, AVG), Grouping Data with GROUP BY, Handling NULL Values
  • Modifying Data and Table Structures: Inserting Data (INSERT INTO), Updating Data (UPDATE), Deleting Data (DELETE), Modifying Table Structure (ALTER TABLE), Dropping Tables and Databases, Adding and Removing Columns, Renaming Tables and Columns
  • Querying Multiple Tables: Introduction to Joins, Inner Join, Left Join, Right Join, Full Outer Join, Cross Join, Using Aliases for Table Names
  • Constraints of SQL: Primary Key Constraints, Foreign Key Constraints, Unique Constraints, Not Null Constraints, Check Constraints, Default Constraints, Enforcing Constraints in MySQL
  • Importing and Exporting Data: Importing Data from CSV Files, Exporting Data to CSV Files, Using MySQL Workbench for Import/Export, Using LOAD DATA INFILE, Using SELECT INTO OUTFILE, Data Formats for Import/Export, Handling Errors During Import/Export
  • Working With Expressions: Arithmetic Expressions, String Expressions, Date and Time Expressions, Logical Expressions, Conditional Expressions (CASE), Using Functions in Expressions, Combining Expressions
  • Database Normalization: Introduction to Normalization, First Normal Form (1NF), Second Normal Form (2NF), Third Normal Form (3NF), Boyce-Codd Normal Form (BCNF), Advantages of Normalization, Denormalization
  • Grouping and Summarizing Data: Using GROUP BY Clause, Using HAVING Clause, Aggregate Functions (SUM, AVG, MAX, MIN), Counting Rows with COUNT, Grouping Sets and Rollup, Pivoting Data, Summarizing Data with Subqueries
  • Triggers & Cursor: Introduction to Triggers, Creating Triggers in MySQL, BEFORE and AFTER Triggers, Row-Level and Statement-Level Triggers, Managing Trigger Execution Order, Debugging Triggers, Use Cases for Triggers. Introduction to Cursors, Declaring Cursors in MySQL, Opening and Closing Cursors, Fetching Data with Cursors, Cursor Looping, Handling Exceptions in Cursors, Use Cases for Cursors
  • Functions: Built-in Functions in MySQL, String Functions (CONCAT, LENGTH), Numeric Functions (ROUND, CEIL), Date and Time Functions (NOW, DATE_ADD), Aggregate Functions (SUM, AVG), Creating User-Defined Functions, Using Functions in Queries
  • Designing and Creating a Database: Database Design Principles, Entity-Relationship (ER) Modeling, Creating ER Diagrams, Defining Tables and Relationships, Choosing Data Types, Setting Up Primary and Foreign Keys, Implementing Business Rules with Constraints
  • Exploring and Processing on Data: Data Exploration Techniques, Descriptive Statistics in SQL, Data Cleaning and Transformation, Data Integration from Multiple Sources, Using Subqueries for Data Processing, Analyzing Data Patterns, Using SQL for Data Reporting
  • Advanced Querying Techniques: Subqueries and Nested Queries, Common Table Expressions (CTEs), Window Functions, Recursive Queries, Dynamic SQL, Query Optimization Techniques, Advanced Join Operations.
Mathematics & Statistics
  • Introduction to Statistics: Definition and Importance of Statistics, Types of Statistics: Descriptive and Inferential, Applications of Statistics in Data Science, Basic Terminology: Population vs. Sample, Statistical Methods and Techniques, Role of Statistics in Decision Making
  • Descriptive and Inferential Statistics: Overview of Descriptive Statistics, Data Summarization Techniques, Visualization Methods (Charts, Graphs), Measures of Central Tendency and Dispersion, Overview of Inferential Statistics, Concept of Statistical Inference, Estimation and Confidence Intervals, Hypothesis Testing Fundamentals
  • Process of Statistical Study: Defining the Research Question, Data Collection Methods, Data Cleaning and Preparation, Data Analysis Techniques, Interpretation of Results, Reporting and Communicating Findings.
  • Sampling Techniques: Probability Sampling Methods, Simple Random Sampling, Stratified Sampling, Cluster Sampling, Systematic Sampling, Non-Probability Sampling Methods, Convenience Sampling, Judgmental Sampling, Quota Sampling, Sampling Errors and Biases
  • Measure of Central Tendencies: Mean (Arithmetic, Geometric, Harmonic), Median, Mode, Weighted Mean, Applications and Interpretation of Central Tendencies, Comparison of Different Measures
  • Frequency Distribution: Types of Frequency Distributions, Ungrouped Frequency Distribution, Grouped Frequency Distribution, Constructing Frequency Tables, Histograms, Frequency Polygons, Cumulative Frequency Distribution, Relative Frequency
  • Measure of Dispersion: Range, Variance, Standard Deviation, Mean Absolute Deviation, Quartiles and Interquartile Range (IQR), Coefficient of Variation
  • Probability: Basic Concepts of Probability, Rules of Probability (Addition and Multiplication), Conditional Probability, Bayes’ Theorem, Independent and Dependent Events, Applications of Probability in Data Science
  • Probability Density Function: Definition and Properties, Continuous Random Variables, Common Continuous Distributions (Normal, Exponential), Calculation of Probabilities using PDF, Examples and Applications, Relationship with Cumulative Distribution Function (CDF)
  • Types of Data Distribution: Normal Distribution, Binomial Distribution, Poisson Distribution, Exponential Distribution, Uniform Distribution, Log-normal Distribution
  • Skewness: Definition and Interpretation, Positive Skewness, Negative Skewness, Measuring Skewness (Pearson’s, Bowley’s, Kelly’s), Impact of Skewness on Data Analysis, Real-life Examples, Definition and Interpretation, Types of Kurtosis (Leptokurtic, Mesokurtic, Platykurtic), Measuring Kurtosis, Impact of Kurtosis on Data Analysis, Real-life Examples, Relationship with Skewness
  • IQR (Interquartile Range): Definition and Calculation, Importance of IQR in Data Analysis, Detecting Outliers using IQR, Comparison with Range, Applications of IQR, Examples and Case Studies
  • Hypothesis Testing: Definition and Importance, Null and Alternative Hypotheses, Type I and Type II Errors, P-value and Significance Level, Steps in Hypothesis Testing, Examples and Applications
  • Chi-Square Test: Definition and Applications, Types of Chi-Square Tests (Goodness of Fit, Independence), Assumptions and Conditions, Calculation and Interpretation, Examples and Case Studies, Limitations of Chi-Square Test
  • ANOVA (One-Way and Two-Way): Definition and Applications, Assumptions of ANOVA, One-Way ANOVA (Concept and Calculation, Interpretation of Results, Examples and Case Studies), Two-Way ANOVA (Concept and Calculation, Interaction Effects, Examples and Case Studies)
  • Multicollinearity: Definition and Impact on Regression Analysis, Detecting Multicollinearity (VIF, Tolerance), Causes of Multicollinearity,Handling Multicollinearity (Ridge Regression, Lasso), Examples and Applications, Case Studies and Real-life Scenarios
Machine Learning (ML)
  • Prerequisites to ML – Overfitting, Regularization, Feature Selection: Introduction to Overfitting and Underfitting, Techniques to Handle Overfitting, Regularization Techniques, Feature Selection Techniques, Feature Engineering Techniques, Dimensionality Reduction Techniques, Data Preprocessing
  • Linear Regression: Introduction to Linear Regression, Ordinary Least Squares (OLS) Method, Model Evaluation Metrics, Assumptions of Linear Regression, Multiple Linear Regression, Regularization in Linear Regression, Polynomial Regression
  • Logistic Regression: Introduction to Logistic Regression, Binary Classification, Model Evaluation Metrics, Multinomial Logistic Regression, Regularization in Logistic Regression, Handling Imbalanced Data, Model Interpretation
  • Decision Tree: Introduction to Decision Trees, Tree Building Algorithms, Splitting Criteria, Pruning Techniques, Handling Continuous and Categorical Variables, Advantages and Disadvantages of Decision Trees, Visualizing Decision Trees
  • Random Forest:Introduction to Random Forest, Building a Random Forest Model, Hyperparameter Tuning, Feature Importance, Out-of-Bag (OOB) Error, Advantages and Disadvantages of Random Forest, Applications of Random Forest
  • AdaBoost: Introduction to AdaBoost, Working Principle, Algorithm Steps, Types of AdaBoost, Advantages and Disadvantages of AdaBoost, Hyperparameter Tuning, Applications of AdaBoost
  • Gradient Boosting: Introduction to Gradient Boosting, Working Principle, Algorithm Steps, Types of Gradient Boosting Algorithms, Advantages and Disadvantages of Gradient Boosting, Hyperparameter Tuning, Applications of Gradient Boosting
  • XG Boost: Introduction to XGBoost, Working Principle, Algorithm Steps, Hyperparameter Tuning, Evaluation Metrics, Advantages and Disadvantages of XGBoost, Applications of XGBoost
  • LDA (Linear Discriminant Analysis): Introduction, Working Principle, Algorithm Steps, LDA for Classification, Advantages and Disadvantages, Comparison with Other Techniques, Applications of LDA
  • PCA (Principal Component Analysis): Introduction to PCA, Working Principle, Algorithm Steps, Interpreting Principal Components, Advantages and Disadvantages of PCA, Comparison with Other Techniques, Applications of PCA
  • KMeans and KNN:Introduction to KMeans Clustering, Working Principle, Algorithm Steps, Evaluation Metrics for KMeans, Advantages and Disadvantages of KMeans, Introduction to KNN (K-Nearest Neighbors), Working Principle of KNN
  • SVM: Introduction to SVM, Working Principle, Algorithm Steps, Kernel Functions, Advantages and Disadvantages of SVM, Hyperparameter Tuning, Applications of SVM

Natural Language Processing & Deep Learning (NLP & DL)
  • Forward Propagation: Definition and Purpose, Mathematical Formulation, Input Layer to Hidden Layer Transitions, Hidden Layer to Output Layer Transitions, Activation Functions in Forward Propagation, Computational Graphs, Example of Forward Propagation in Neural Networks
  • Back Propagation: Definition and Importance, Chain Rule of Differentiation, Error Calculation, Weight Adjustment, Gradient Descent in Back Propagation, Back Propagation in Deep Networks, Example of Back Propagation
  • Epochs: Definition of Epochs, Role of Epochs in Training, Impact on Model Performance, Epochs vs. Iterations, Monitoring Training Progress, Determining Optimal Number of Epochs, Overfitting and Underfitting Concerns
  • Weights and Bias: Definition and Role of Weights, Initialization of Weights, Role of Bias in Neural Networks, Adjusting Weights and Biases, Impact on Model Accuracy, Regularization Techniques, Visualizing Weights and Biases
  • Activation Function: Definition and Purpose, Types of Activation Functions, Sigmoid Activation Function, ReLU (Rectified Linear Unit), Tanh (Hyperbolic Tangent), Softmax Activation Function, Choosing the Right Activation Function
  • Gradient Descent: Definition and Purpose, Types of Gradient Descent (Batch, Stochastic, Mini-Batch), Learning Rate and Its Impact, Convergence Criteria, Gradient Descent Algorithm Steps, Challenges and Solutions, Examples of Gradient Descent
  • Loss Functions: Definition and Purpose, Mean Squared Error (MSE), Cross-Entropy Loss, Hinge Loss, Regularization in Loss Functions, Impact on Model Training, Selecting Appropriate Loss Function
  • Optimizers: Definition and Role, Stochastic Gradient Descent (SGD), Adam Optimizer, RMSProp, Adagrad, Momentum-Based Optimization, Comparing Different Optimizers
  • Finding patterns in text: Text Data Collection, Tokenization, Stop Words Removal, N-grams Analysis, Frequency Distribution, Part-of-Speech Tagging, Named Entity Recognition
  • Text Analytics: Definition and Importance, Text Preprocessing Techniques, Sentiment Analysis, Topic Modeling, Text Classification, Text Summarization, Applications in Various Domains
  • Applications of Social Media Analytics: Sentiment Analysis on Social Media, Trend Analysis, Social Network Analysis, Customer Feedback and Insights, Brand Monitoring, Crisis Management, Influencer Identification
  • Fine tuning the models using Hyper parameters: Definition of Hyperparameters, Hyperparameter Tuning Techniques, Grid Search, Random Search, Bayesian Optimization, Hyperparameter Impact on Model Performance, Practical Examples
  • Deep Learning for NLP: Overview of Deep Learning in NLP, RNNs for Sequence Modeling, CNNs for Text Classification, Transformer Models, Transfer Learning in NLP, Pre-trained Language Models, Applications and Case Studies
  • Word Embeddings: Definition and Importance, Word2Vec, GloVe (Global Vectors for Word Representation), FastText, Contextualized Word Embeddings, Evaluation of Word Embeddings, Applications in NLP
  • Taming big text, Unstructured vs. Semi-structured Data: Differences Between Unstructured and Semi-structured Data, Challenges with Big Text Data, Storage Solutions for Big Text, Data Cleaning and Transformation, Tools for Handling Big Text Data, Practical Examples, Case Studies
  • Wordnet: Overview of WordNet, Synsets and Semantic Relations, WordNet for Synonym Identification, Applications in NLP, Integrating WordNet with Other Tools, Practical Examples, Case Studies
  • Text Preprocessing: Tokenization, Lowercasing, Stop Words Removal, Stemming and Lemmatization, Handling Punctuation and Special Characters, Removing Duplicates, Practical Examples
  • Lemmatization and Stemming: Definition and Differences, Stemming Algorithms (Porter, Snowball), Lemmatization with WordNet, Impact on Text Data, Choosing Between Lemmatization and Stemming, Practical Examples, Case Studies
  • Feature Extraction from text:Bag of Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), Word Embeddings, N-grams, Named Entity Recognition, Part-of-Speech Tagging, Practical Examples
  • Vocabulary and Document: Building a Vocabulary, Document-Term Matrix, Term Frequency Calculation, Inverse Document Frequency Calculation, Handling Out-of-Vocabulary Words, Practical Examples, Case Studies


Classroom Sessions

Ethans Pune offers highly professional and advanced training as per the industry need with a special focus on 100% practical and project based program, interactive sessions with Individual focus, online doubt clearing sessions, backup online recorded session, forum access for doubts and questions

LMS (Learning Management System)

Free courses to supplement your learning and understand cross functional demands of the industry. Recorded sessions of ongoing courses from your classes. Along with study materials in the form of assignments, projects and POCs to guide you through modules in your ongoing class.


Hands-on assignments on each topic and modules, trainers provide these assignments according to the student's skill set and project requirements. Assignment duration will be approximately an hour a day. Assignment focuses on real time companies projects. Complementary Study Material with every course.


We are ISO 9001:2015 certified institution. Our certificate is recognized by many top companies. The certificate is provided on an assessment by our industry experts which makes your curriculum vitae, a holistic one and marks an edge on your profile. Increase the weightage of your resume by obtaining a course completion certificate on successful completion of the course and projects.

Real-life Case Studies

Ethans provides multiple use cases on real time projects. It helps students to understand the business requirements, analysis of requirements and challenges on real time implementation. We make them industry ready.

Job Assistance Program

On successfully completing a course with Ethans, you could be eligible for a job assistance program. Under this program we help students to build a perfect resume and optimize online profiles for job calls. Alumni group of 5000+ students help share the opportunities. 3000+ freshers/professionals received jobs in top companies in India or across India with good salary packages.

Data Science Classes in Pune FAQs

What are the prerequisites for Data Science Training in Pune?

Ethans Tech data Science program doesn’t require any prerequisites to start off.

Our program contains all the basic concepts needed to grab the data science technique which includes logical building, programming techniques, data warehousing concepts, SQL, statistics and linear algebra.

Who should go for Data Science training in Pune?

Data Science Training is open to all students. As per our teaching experience teaching students from non-IT backgrounds can also learn this technology. Students in a class are mixed from 1 years of experience to 24 years of working experience. Even CA, CS, BCom, Share Brokers, learning this skill for implementing the same into day to day activities. It’s the finest kick off for the freshers and icing on the cake, for others.

What if I have queries after I complete this course?

You will have access to the technical discussion forum lifetime, which will help you in resolving most of your doubts and queries. Eventually, you can connect with your respective faculty directly, else reach out to the nearest Ethans help desk for further assistance.

Who are the instructors?

Our faculties are all extensively experienced IT Industry experts and real time corporate trainers from renowned MNC’s with marvelous knowledge in the subject matter. In addition to their standard duties of imparting knowledge, faculties also undertake consulting assignments for leading Indian and Multinational Companies that gives practical exposure based on real time scenarios from their world of experience. Ethanians that upskill are able to vividly recall classes even after years. Such is the impact of our remarkable trainers on the students.

Will Ethans be providing any study materials?

Yes, Ofcourse. You will be provided with both, hard copies of the study material upon enrollment and soft copies will be shared by the respective faculties over the cloud during the program.

What types of courses are available at Ethans?

Ethans is a Market leader in Cloud, Automation and Analytics (such as Python, Data Science – Machine Learning, Artificial Intelligence, AWS, Azure, GCP, DevOps, Hadoop, Selenium, Robotics Process Automation, etc.) helps working professionals and freshers in enhancing skills in multiple technologies as per the market demand.

Does Ethans provide Job Assistance?

Yes. Ethans being one of the top notch institutes, has a wide network in the market. We do have collaboration with multiple companies that helped 3000+ freshers to get place and working professionals to switch their domains in top companies across India. On course completion, one becomes eligible for the placement assistance.

Does Ethans provide weekend classes for professionals?

There are multiple institutes which are providing software training courses, but only Ethans offers professional training on various demanding technologies with flexible timings over the weekdays as well as on weekends depending on the requirements of working professionals.

What are the profiles and experiences of trainers at Ethans?

We have a big pool with a strong and renowned team of professionals having extensive expertise in the technology they deliver. Our trainers are quite supportive and render an amicable learning environment which invigorates the student’s growth in an optimistic way. They are all with huge industry and teaching experiences. Some of them hold more than two decades of experience of the same industry.

Do I need to pay the complete fee lump sum or I can have an installment facility too?

We are open for both, lump sum and installments. Best to pay it in one go, but we avail maximum two installments. First installment is to be cleared on the very first day of the class and the second one, within 21 days from the batch commencement date.

The fee which I will pay is refundable or transferable?

No. Fees once paid is non-refundable and non-transferable at any case, as we assure best training quality at Ethans Tech. We do not let go of any of our valuable students and ensure to deliver the quality that meets one’s requirements and is worth it. Each one of you is important to us.

What are the facilities and infrastructure at Ethans?

As of now, Ethans has presence at three locations in Pune (Wakad Saudagar/Kharadi/Hinjewadi) and in Noida as well. Looking forward to expanding gradually across India with all your support. Ethans has state-of-the-art-infrastructure that accelerates a pleasant and academically vigorous and stimulating environment for the students and the faculty.

Does Ethans provide Online Training?

Yes. Ethans renders Online training too. It’s a virtual and absolutely interactive training by the identical faculties that educate at our different branches with the help of a specific software which is best to intercommunicate online. You will be obtaining uniformly the same study material and assessment in online training as in the classroom. Educating yourself from Ethans Tech either by classroom or online means is the same, as the instructors don’t change. Now, it depends on the students, which mode to prefer based on their comfort levels.

Does Ethans provide facility to repeat the batch?

On enrollment at Ethan’s Tech, you are provided with a flexi pass that avails one year re-joining option within the same branch and under the same trainer from the date of enrollment with an administrative charges of only 5000/-. We also provide online recorded sessions as backups. Options available for branch change with nominal charges as per the norms and regulations of the institute.

Does Ethans provide Institutional Certification after the course?

Yes. Certificate is issued to the respective student, after the course completion and assessment which is valid in multiple companies and apparently, it becomes an edge in your curriculum vitae in order to showcase your skill set.

Does Ethan's conduct training at Corporates?

Yes, we are frequently engaged in corporate training being the market leader with a big pool of corporate trainers having a wide network with collaboration with several top MNC’S that ultimately becomes an add-on for placing our students with such references.

What is the admission procedure at Ethans?

Basically, there are two ways of registering with Ethans Tech, Online and Offline. In terms of Online, you just need to visit the enrollment tab on your website – which will redirect you to the enrollment form, wherein you need to fill all your essential details and proceed further by paying Rs. 5000/- as the registration fees (inclusive of your standard course fees) For Offline admission, you can can contact and visit any of your nearest Ethans branch and carry out the further formalities under the guidance of the team at Ethans Desk.


Our Industry Expert Trainer

We are a team of 10+ Years of Industry Experienced Trainers, who conduct the training with real-time scenarios. The Global Certified Trainers are Excellent in knowledge and highly professionals. The Trainers follow the Project-Based Learning Method in the Interactive sessions.

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Lectures: 18-20 Duration: 50-60 hours

Artificial Intelligence (AI) training at Ethans Tech, Mumbai, is a program that introduces students to the extended knowledge…

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Our Testimonials

Hi, it was a great and quality experience to get upskilled in Python from Ethans Tech. The program is well designed and the trainer was highly experienced and good at presenting the concepts clearly by presenting real-time scenarios. The Admin team is very helpful and they are always available whenever you need any help regarding coding or the software. Therefore, I would highly recommend Ethans Tech.

    Renuka Gudela Palteru
    Renuka Gudela Palteru

    Sr. Team Manager at Gallagher

    The courses are premium and great commandments. Instructors are always there to clear the queries and prompt you when you open an issue if you are having any trouble. Added bonus - you get the software, study material, and certification of the course you opt for! I have enrolled in so many courses and they always offer the best course each and every time I enrolled. Love these guys. Salute to their service !!

      Upender Negi
      Upender Negi

      Network Design Specialist at BT, UK

      The training is really marvelous and we are about to start a POC that would really help us in implementing the real-time scenarios. Overall the session is good and interactive. Thanks for all the classes organized. I am immensely satisfied with the course content, they offered pre-recorded classes and 8 months of post-class assistance from experts. I exceptionally recommend Ethans Tech, as it’s the best for corporate leanings and all cutting-edge technologies/tools/languages.

        Priyanka Tomar
        Priyanka Tomar

        Consultant at Credit Suisse

        I am a last-year College student and my keen Interest is in Cloud Technology, In Feb’20 - Joined the Amazon courses training program from Ethans which gave me 100% Practical Hand-on training along with covered the Programming languages, Big Data Hadoop, AWS, Azure, SQL DataBase which help me to get prepared for Internship and Job. One of the best Institutes!

          Gayatri Soni
          Gayatri Soni

          Intern at Ethans Tech

          Classroom sessions were delightful. Don't have a command of Linux and Python, and because of that feeling of lagging somewhere, would have more wonderful learning, if I had a command of those languages. Overall experience is superior for learning.

            Md Ashraf Khan
            Md Ashraf Khan

            Technical Service Engineer Expert, Fujitsu

            It was a great learning experience from Ethan's teaching Skills are pitched perfectly. The trainers were well Experienced and motivational. They always make sure that each and every student should be crystal clear with every topic, and they are always ready to help, this is what helps them offer the best service to their students. Thanks, a bunch of Ethans for developing my interest in coding.

              Anhadpreet Singh
              Anhadpreet Singh

              Student at Guru Nanak Dev Engineering College, Ludhiana

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