Data Science Course in Pune

python with data science course

Trained 15000+ Students | 40+ hrs course duration |
15+ hrs assignment duration | Exams conducted after topic completion


What is
Data Science?

The best technology, career for future 

Data Science is an interdisciplinary subject that uses scientific methods, algorithms, and procedures to extract knowledge and insights from structured and unstructured data.  Large-scale data sets are analyzed through techniques such as predictive modeling, data mining, analysis of statistics, and machine learning. This facilitates the discovery of latent patterns and trends in the data.

The key function of data science is gathering, analyzing, and cleansing data to make wise decisions that guide responsible behavior. 

Expertise in programming, statistics, and mathematics is used by data scientists to evaluate large volumes of complicated data and assist businesses in problem solving and strategic decision making. Numerous industries have an impact on this, including data science, marketing, technology, healthcare, and finance. Data Science enables businesses to improve productivity, streamline operations, and gain a competitive advantage in the ever-changing landscape of the digital world by translating raw data into actionable insight.

Key features

  • Live Training 
  • Course Duration : 5 Months
  • 24*7 Lifetime Support
  • Job Assistance Program
  • Projects : Real Time projects

1. Fundamentals of Data Science and Machine Learning

  • 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
  • Vectors
  • Matrices
  • Optimization
  • Theory Of optimization
  • Gradients Descent

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
  • Normal Distribution
  • Binomial Distribution

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
  • Array Manipulation
  • Array I/O
  • Importing Files with Numpy

5. Data Manipulation with Pandas

  • Data Frames
  • I/O
  • Selection in DFs
  • Retrieving in DFs
  • Applying Functions
  • Reshaping the DFs – Pivot
  • Combining DFs Merge Join
  • Data Alignment

6. SciPy

  • Matrices Operations
  • Create matrices Inverse, Transpose, Trace, Norms , Rank etc.
  • Matrices Decomposition Eigen Values & vectors SVDs

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
  • Pylab & Pyplot
  • Scatter plots
  • 2D Plots
  • Histograms
  • Bar Graphs
  • Pie Charts
  • Box Plots
  • Customization
  • Store Plots

9. SciKit Learn

  • Basics
  • Data Loading
  • Train/Test Data generation
  • Preprocessing
  • Generate Model
  • Evaluate Models

10. 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

11. Probability Basics

  • Notation and Terminology
  • Unions and Intersections
  • Conditional Probability and Independence

12. Probability Distributions

  • Random Variable
  • Probability Distributions
  • Probability Mass Function
  • Parameters vs. Statistics
  • Binomial Distribution
  • Poisson Distribution
  • Normal Distribution
  • Standard Normal Distribution
  • Central Limit Theorem
  • Cumulative Distribution function

13. 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

14. Data Analysis

  • Case study- Netflix
  • Deep analysis on Netflix data

1.  Exploratory Data Analysis

  • Data Exploration
  • Missing Value handling
  • Outliers Handling
  • Feature Engineering

2. Feature Selection

  • Importance of Feature Selection in Machine Learning
  • Filter Methods
  • Wrapper Methods
  • Embedded Methods

3. Machine Learning: Supervised Algorithms Classification

  • Introduction to Machine Learning
  • Logistic Regression
  • Naïve Bays Algorithm
  • K-Nearest Neighbor Algorithm
  • Decision Tress
    1. SingleTree
    2. Random Forest
  • Support Vector Machines
  • Model Ensemble
  • Model Evaluation and performance
    • K-Fold Cross Validation
    • ROC, AUC etc…
  • Hyper parameter tuning
    • Regression
    • classification

4. Machine Learning: Regression

  • Simple Linear Regression
  • Multiple Linear Regression
  • Decision Tree and Random Forest Regression

5. Machine Learning: Unsupervised Learning Algorithms

  • Similarity Measures
  • Cluster Analysis and Similarity Measures

6. Ensemble algorithms

  • Bagging
  • Boosting
  • Voting
  • Stacking
  • K-means Clustering
  • Hierarchical Clustering
  • Principal Components Analysis
  • Association Rules Mining & Market Basket Analysis

7. Recommendation Systems

  • Collaborative filtering model
  • Content-based filtering model.
  • Hybrid collaborative system

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
  • Dimensions & Measures
  • Data Source Filters
  • Creation of Sets

2. Scatter Plot

  • Gantt Chart
  • 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

Why choose us?

  • Appropriate Fee Structure
  • Completely satisfied students
  • 100 % Employment Assistance
  • E-Books and sessions’ Recordings.
  • Resume Preparation Session.
  • Interview Question & Answers.
  • Certified Expert Trainers.
  • Hands on Experience on Real Time Projects.
  • Course Completion Certificate.
  • We will work hand-on-hand and will try our best to provide you with the best available opportunities.


Grab an opportunity in Data Science and Machine Learning with our job-oriented curriculum.

Get guidance from IT Pundits and assured placement assistance.


At Data Science Training in Pune, our placement assistance covers resume building, interview preparation, and connecting students with potential employers. Additionally, we provide career counseling and job placement services.

Data Science Training in Pune is the best institute providing best course content and faculty expertise.

Recent graduates and experienced professionals looking to transition their careers into data science are welcome to enroll in our Data Science with Python courses at Data Science Training in Pune.

Data Analytics training is available in various modes, including classroom sessions, online courses, and blended learning (a combination of online and offline). Choose the mode that aligns with your learning preferences and schedule.

Yes, Data Science Training in Pune provides online courses in Data Science with Python, catering to learners who prefer remote study or have time constraints.

Basic programming skills, an understanding of statistics, and familiarity with Python may be prerequisites. Our course comprehensively covers these requirements, and our faculty ensures a strong foundation.

Upon course completion, our institute, offers post-course support through forums, Q&A sessions. This ensures ongoing assistance for any queries you may have even after finishing the Data Science course.


Great place for Online certification into Python with Data Science course. The  management and staff were exceptionally helpful. The teachin method and quality of instructor had a thorough practical experience. I landed up a job within 1.5 month of the completion of my certification.

Rahul Pawar