Enroll in the best Data Science Course in Pune and learn about practical problems data science applications and the methods used to create analytical models that improve business outcomes. This 100% Job Assurance Data Science Course in Pune suits recent graduates and professionals looking to start a successful data science and analytics career. You get a practical knowledge of data science and analytics applications in real-world enterprises and prepare to work as a data science expert in a rapidly growing industry.

We are focused on giving you an integrated learning experience. With our exclusive career support services, we will continue to assist you as you choose an innovative approach to your career. We have a unique method of providing training to students; applicants begin working on projects from day one, in addition to training sessions. Every batch will have mentors guiding students throughout the data science course.


  • Learn from industry experts and seasoned data scientists who bring real-world experience to the classroom. Benefit from their practical insights, guidance, and mentorship throughout your learning journey.
  • Our data science course is meticulously designed to cover all aspects of data science, from data collection and pre-processing to advanced machine learning and data visualization techniques.
  • Gain a well-rounded skill set that equips you for success in various data-related roles.
  • We believe in learning by doing. Our course emphasizes practical, hands-on projects and exercises that allow you to apply your knowledge immediately.
  • Work on real datasets and industry-relevant projects to build a strong portfolio.
  • Stay ahead in a competitive job market. Our course focuses on the most in-demand skills and tools used in the industry, such as Python, R, Power BI, Tableau, and more.
  • We understand that every learner is unique. Our data analytics course in Pune offers personalized learning paths and one-on-one support to help you reach your goals. Whether you’re a beginner or an experienced data professional, our course adapts to your needs.
  • Our data science course in Pune is a commitment to your success that doesn’t end with the course. Get career services, resume workshops, and interview preparation to help you land your dream job in data science.

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Key Features

Full stack Java developer course in Pune

20+ Modules

Best Software Testing Course Pune

Free Aptitude & Soft skill Sessions

Full stack Java developer course in Pune

100% placement calls Guaranteed till you get placed

Full stack Java developer course in Pune

100+ Hours

Full stack Java developer course in Pune

Classroom & Online Training

Full stack Java developer course in Pune

Flexible Timings

Full stack Java developer course in Pune

Corporate Trainer Having 15+ yes exp with MNC

Full stack Java developer course in Pune

Hand on Real Time Projects

Resume Building & Mock Interviews

Data science and analytics Course Fees and Duration

Advanto offers Data science course to students and professionals for very affordable fees and easy payment options. The fee structure is the best in the industry among other Data science institutes in Pune.

The duration of the data science classes is generally for a 100 Hours(weekends). The timings are suited best for students and working professionals.

Walk-in to our office for fees details, course duration and other details of the Data science course in Pune.


Any Graduate (BE/B. Tech,, MCA, M. E/ M. Tech, B.CS, BCA), candidates appeared for final year can also apply.

Course Content

  • Introduction to Programming
  • Variables & Arithmetic Expressions
  • Functions
  • Data Types
  • Conditions and Conditional statements
  • Lists
  • OOPS
  • Intro to Excel
  • Importing data
  • Formatting in Excel
  • Excel Formulae
  • Data Validation
  • Calculations
  • Lookup and Reference
  • Pivot Tables
  • Charts
  • What-if Analysis
  • Intro to Macros
  • Introduction to SQL
  • DDL Statements
  • DML Statements
  • DQL Statements
  • Aggregate Functions
  • Date functions
  • Union, Union All & Intersect Operators
  • Joins
  • Views & Indexes
  • Sub-Queries
  • Exercise on SQL

Python Programming

  • Python Introduction
  • Variables
  • Functions
  • Python Operators
  • Python Flow Controls
  • Conditional Statements
  • Loops
  • Python Collection Objects
    1. Strings
    2. List
    3. Tuple
    4. Dictionary
  • List Comprehension
  • User-defined Functions
  • Function Arguments
  • Lambda Functions
  • Introduction to NumPy
  • NumPy Array
  • Creating NumPy Array
  • Array Attributes
  • Array Methods
  • Array Indexing
  • Slicing Arrays
  • Array Operation
  • Iteration through Arrays
  • Introduction to Pandas
  • Pandas Series
  • Creating Pandas Series
  • Accessing Series Elements
  • Filtering a Series
  • Arithmetic Operations
  • Series Ranking and Sorting
  • Checking Null Values
  • Concatenate a Series
  • Pandas Data frame – Introduction
  • Data frame Creation
  • Reading Data from Various Files
  • Understanding Data
  • Accessing Data frame Elements using Indexing Data frame Sorting
  • Ranking in Data frame
  • Data frame Concatenation
  • Data frame Joins
  • Data frame Merge
  • Reshaping Dataframe
  • Pivot Tables
  • Cross Tables
  • Dataframe Operations
  • Checking Duplicates
  • Dropping Rows and Columns
  • Replacing Values
  • Grouping Dataframe
  • Missing Value Analysis & Treatment
  • Visualisation using Matplotlib
  • Plot Styles & Settings
  • Line Plot
  • Multiline Plot
  • Matplotlib Subplots
  • Histogram
  • Boxplot
  • Pie Chart
  • Scatter Plot
  • Visualisation using Seaborn
  • Strip Plot
  • Distribution Plot
  • Joint Plot
  • Violin Plot
  • Swarm Plot
  • Pair Plot
  • Count Plot
  • Heatmap
  • Summary Statistics
  • Missing Value Treatment
  • Dataframe Analysis using Groupby
  • Advanced Data Explorations

Machine Learning

  • Introduction to Machine Learning
  • Machine Learning Modelling Flow
  • Parametric and Non-parametric Algorithms
  • Types of Machine Learning
  • Introduction of Linear Regression
  • Types of Linear Regression
  • OLS Model
  • Math behind Linear Regression Decomposition Variability
  • Metrics to Evaluate Model
  • Feature Scaling
  • Feature Selection
  • Regularisation Techniques
  • Project – Property Price Prediction
  • Class Assessment on Linear Regression
  • Intro to Logistic Regression
  • Maximum Likelihood Estimation
  • Performance Metrics
  • Performance Measures
  • Bias-Variance Tradeoff
  • Overfitting and Underfitting Problems
  • Cross Validation
  • Project – Vaccine Usage Prediction
  • Home Assignment on Logistic Regression
  • Introduction to Decision Tree
  • Entropy
  • Information Gain
  • Greedy Algorithm
  • Decision Tree: Regression
  • Gini Index
  • Tuning of Decision Tree-Pruning
  • Project – Heart Disease Prediction
  • Introduction to Random Forests
  • Averaging
  • Bagging
  • Random Forest – Why & How?
  • Feature Importance
  • Advantages & Disadvantages
  • Project on random forest – Taxi Fare Prediction
  • Class Assessment on Classification
  • What is Clustering?
  • Prerequisites
  • Cluster Analysis
  • K-means
  • Implementation of K-means
  • Pros and Cons of K-means
  • Application of K-means
  • Project on K-means Clustering – E-commerce Customer Segmentation
  • Introduction to Hierarchical Clustering
  • Types of Hierarchical Clustering
  • Dendrogram
  • Pros and Cons of Hierarchical Clustering
  • Project on Hierarchical Clustering – Travel Review Segmentation
  • Home Assignment on Clustering
  • Prerequisites
  • Introduction to PCA
  • Principal Component
  • Implementation of PCA
  • Case study
  • Applications of PCA
  • Project on PCA – Real Estate Data Analysis using PCA
  • Understand Time Series Data
  • Visualising Time Series Components
  • Exponential Smoothing
  • Holt’s Model
  • Holt-Winter’s Model
  • Project – Forecasting the Sales of a Furniture Store

Statistics for Data Science

  • Introduction to Statistics
  • Random Variables
  • Descriptive Statistics
  • Measure of Central Tendency
  • Measure of Dispersion
  • Skewness and Kurtosis
  • Covariance and Correlation
  • What is Probability?
  • Events and Types of Events
  • Sets in Probability
  • Probability Basics using Python
  • Conditional Probability
  • Expectation and Variance
  • Probability Distributions
  • Discrete Distributions
  • – Uniform
  • – Bernoulli
  • – Binomial
  • – Poisson
  • Continuous Distributions
  • – Uniform
  • – Normal
  • Probability Distributions using Python
  • Introduction to Hypothesis Testing
  • Terminologies used in Hypothesis Testing
  • Procedure for testing a Hypothesis
  • Test for Population Mean
  • Small Sample Tests
  • Large Sample Tests
  • One-way ANOVA
  • Assumptions
  • ANOVA Hypothesis
  • Post Hoc Test
  • Chi-Square Test
  • Chi-Square Test Steps
  • Chi-Square Example

Basics of Cloud

Machine Learning on Cloud

Deploying ML models on Cloud

Data visualisation with Tableau and Power BI

  • Introduction to Tableau
  • Data Connection
  • Tableau Interface and Basic Chart Types
  • Working with Metadata
  • Visual Analytics
  • Mapping
  • Calculations
  • Dashboard and Stories

Project – Mortgage Analysis

  • Introduction
  • Interface
  • Data Connections
  • Data Transformation
  • Advance Data Transformation
  • Project – Stock Data analysis
  • Exam – Tableau and Power BI Exam

Data analytics and ML Track

  • Ensemble Techniques
  • What is Ensembling?
  • Bootstrap Method
  • Bagging
  • Boosting
  • XGBoost
  • AdaBoost
  • Introduction to KNN
  • Working of KNN
  • Applications of KNN
  • Advantages and disadvantages of KNN

Analytics in Healthcare/Finance

Some detailed project based on Classification

Analytics in Marketing/Sales

Some detailed project based on Regression

 Some detailed project based on SQL, Tableau

Some detailed project based on Excel, Power BI

  • Introduction to Linux
  • Linux Commands
  • Resume-building
  • GitHub Project Portfolio
  • Interview Preparation
  • Mock Interviews
  • Career Mentorship

Our recent successful placements

Frequently Asked Questions

Course Duration will be 6 weekends to 8 weekends

Yes, Our trainer will help you out with any kind of issues you face in your project

You can pay the fee in 2 installments.

Yes, we provide placement calls based on your experience.

Yes, you can repeat that topic in alternate batches.

No need, you can repeat the course for any number of times in a year without paying any extra amount.

You can discuss this by visiting our office.

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