Data Science Course in Aundh, Pune

The Technogeeks Data Science Course in Aundh is designed for both freshers and working professionals. We covered 6 different technologies in one data science course, such as – Core and advanced Python, Data Analytics, Data Visualization using Tableau and Power BI, Artificial Intelligence, and SQL programming. 

In our Data Science Training we mainly focus on practical training over theoretical, as we believe in “learning by doing.” In today’s competitive market, organizations look for candidates who can demonstrate practical skills and how to solve real-world problems quickly. 

The Technogeeks Data Science course in Aundh syllabus is designed by working professionals so candidates can get knowledge as per current market demand. 

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Data Science Training Syllabus​

Best Blended Syllabus for Data Science Course in Pune with Placement Oriented Training Institute​

Section 1 - Core Python & Advance Python

  1. What is Python and brief history
  2. Why Python and who use Python
  3. Discussion on Python 2 and 3
  4. Unique features of Python
  5. Discussion on various IDE’s
  6. Demonstration of practical use cases
  7. Python use cases using data analysis
  1. Installing python
  2. Setting up Python Development Environment
  3. Installation of Jupyter Notebook
  4. How to access our course material using Jupyter
  5. Write your first program in Python
  6. Deployment on local and cloud platforms using Google Colab
  1. Introduction to Python objects
  2. Python built-in functions
  3. Number objects and operations
  4. Variable assignment and keywords, String objects and operations
  5. Print formatting with strings
  6. List objects and operations
  7. Tuple objects and operations
  8. Dictionary objects and operations
  9. Sets and Boolean
    Object and data structures
  10. Assessment test
  1. Introduction to Python statements
  2. If, elif and else statements
  3. Comparison operators
  4. Chained comparison operators
  5. What are loops
  6. For loops
  7. While loops
  8. Useful operator
  9. List comprehensions
  10. Statement assessment test
  11. Game challenge
  1. Methods
  2. What are various types of functions
  3. Creating and calling user defined functions
  4. Function practice exercises
  5. Lambda Expressions
  6. Map and filter
  7. Nested statements and scope
  8. Args and kwargs in Python
  9. Functions and methods assignment

Milestone Project using Python

  1. Process files using python
  2. Read/write and append file object
  3. File functions
  4. File pointer and operations
  5. Introduction to error handling
  6. Try, except and finally
  7. Python standard exceptions
  8. User defined exceptions
  9. Unit testing
  10. File and exceptions assignment
  1. Python inbuilt modules
  2. Creating UDM-User defined modules
  3. Passing command line arguments
  4. Writing packages
  5. Define PYTHONPATH
  6. __name__ and __main__
  1. Object oriented features
  2. Implement object oriented programming with Python
  3. Creating classes and objects
  4. Creating class attributes
  5. Creating methods in a class
  6. Inheritance
  7. Polymorphism
  8. Special methods for class
  1. Collections module
  2. Datetime
  3. Python debugger
  4. Timing your code
  5. Regular expressions
  6. StringIO
  7. Python decorators
  8. Python generators
  • Python inbuilt modules
  • Install packages on python
  • Introduction to pip, easy install
  • Multithreading
  • Multiprocessing

Section 2 - Data Analytics & Data Visualization

  1. Introduction in Excel
  2. Data Cleaning & Preparation
  3. Formatting & Conditional Formatting
  4. Lookup Function
  5. Analyzing data with Pivot Tables
  6. Charts
  7. Data Visualization/Dashboarding using excel
  8. Data Analysis using statistics
  1. Introduction to data analysis
  2. Why Data analysis?
  3. Data analysis and Artificial Intelligence Bridge
  4. Introduction to Data Analysis libraries
  5. Data analysis introduction assignment challenge
  1. Introduction to Numpy arrays
  2. Creating and applying functions
  3. Numpy Indexing and selection
  4. Numpy Operations
  5. Exercise and assignment challenge
  1. Introduction to the Series
  2. Introduction to DataFrames
  3. Data manipulation with pandas
  4. Missing data
  5. Groupby
  6. Merging, Joining, and Concatenating
  7. Operations
  8. Data Input and Output
  9. Pandas’ in-depth coding exercises
  10. Text data mining and processing
  11. Data mining applications in Data engineering
  12. File system integration with Pandas
  13. Excel integration with Pandas
    1. Operations on Excel using a dataframe
    2. Data aggregation on Excel Data
    3. Data visualization using Excel Data
    4. Milestone Project – 2
  1. Plotting using Matplotlib
  2. Plotting Numpy arrays
  3. Plotting using object-oriented approach
  4. Subplots using matplotlib
  5. Matplotlib attributes and functions
  6. Matplotlib exercises
Seaborn Visualization
  1. Categorical Plot using Seaborn
  2. Distributional plots using Seaborn
  3. Matrix plots
  4. Grids
  5. Seaborn exercises

Project– Getting insights using python analysis and visualizations on finance credit score data.

Assignment – Pandas built-in data visualization Data visualization

Elective Module
  • Working with Elements
  • Working with Bookmarks
  • Working with Buttons
  • Working with New Tooltips
  • Conditional Formatting
  • Working with Interactions
  • Publish Power BI Dashboards
  • Power BI Service
  • Export Reports to PowerPoint, PDF
  • Implementing Gateways
  • Working with Parameters
  • Final Deployment of the Dashboard
  1. Comparison Between Tableau & Programming-based Data Visualization
  2. Need Of Tableau
  3. Types Of Data Sources Supported By Tableau For Report Development
  4. How to Build Report & Dashboard in Tableau
  5. How To Build Charts In Tableau
  6. Data Visualization Using Tableau Features
  7. Types of Graphs
  8. Multiple graph combinations
  9. Multi-file format support in Tableau
  10. Data analysis without visualization
  11. Data analysis with visualization

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Section 3 - Mathematics and Statistics for Data Science

  1. Need of Mathematics for Data Science
  2. Exploratory data analysis (EDA)
  3. Numeric Variables
  4. Qualitative and Quantitative Analysis
  5. Types of Data Formats
  6. Measuring the Central Tendency – The Model
  7. Measuring Spread – Variance and Standard Deviation
  8. Euclidean Distance
  9. Confidence Coefficient
  10. Understanding Parametric Tests

Section 4 - Artificial Intelligence

  • Introduction to Artificial Intelligence
  • Introduction to Machine Learning
  • Need for Machine Learning in Forecasting
  • Demand of forecasting analytics in current industrial trends
  • Scope of ML
  • Introduction to Machine Learning Algorithm Categories
  • Supervised and Unsupervised Learning
  • Introduction to Regression
  • Exercise on Linear Regression using Scikit Learn Library
  • Project on Linear regression using USA_HOUSING data
  • Evaluation of Linear regression using python visualizations
  • Practice project for Linear regression using advertisement data set to predict appropriate advertisements for users.
K- Nearest neighbors using Python
  • Exercise on K-Nearest neighbors using Sci-kit Learn Library
  • Project on Logistic regression using Dogs and horses’ dataset
  • Getting the correct number of clusters
  • Evaluation of model using confusion matrix and classification report
  • Standard scaling problem
  • Practice project on KNN algorithm.

Decision tree and Random forest with python

  • Intuition behind Decision trees
  • Implementation of decision tree using a real time dataset
  • Ensemble learning
  • Decision tree and random forest for regression
  • Decision tree and random forest for classification
  • Evaluation of the decision tree and random forest using different methods
  • Practice project on decision tree and random forest using social network
  • Data to predict if someone will purchase an item or not
Support Vector Machines
  • Linearly separable data
  • Non-linearly separable data
  • SVM project with telecom dataset to predict the users portability
Principal Component Analysis
  • Introduction to PCA
  • Need for PCA
  • Implementation to select a model on breast-cancer dataset
  • Model evaluation
  • Bias variance trade-off
  • Accuracy paradox
  • CAP curve and analysis
Clustering in unsupervised learning
  • K-means clustering intuition
  • Implementation of K-means with Python using mall customers data to implement clusters on the basis of spending and income
  • Hierarchical clustering intuition
  • Implementation of Hierarchical clustering with python
Association Algorithms
  • A priori theory and explanation
  • Market basket analysis
  • Implementation of Apriori
  • Evaluation of association learning

POC – To make a model to predict the relationship between frequently bought products together on the given dataset from a supermarket.

  • Introduction to Natural Language processing
  • NLTK Python library
  • Data stemming technique
  • Data Vectorization
  • Exercise on NLTK
  • POC– Apply NLP techniques to understand reviews given by customers in a dataset and predict if a review is good/bad without human intervention.
  • Neural Network and Deep Learning
  • What is TensorFlow?
  • TensorFlow Installation
  • TensorFlow basics
  • TensorFlow with Contrib Learn
  • TensorFlow Exercise
  • What is Keras?
  • Keras Basics
  • Pipeline implementation using Keras
  • MNIST implementation with Keras

Section 5 - Working with Databases with Python

  • What is SQL? 
  • Why do we need SQL integration with Python?
  • Data types in SQL
  • DDL, DML, and TCL sub-languages in SQL
  • Significance and type of Joins in SQL
  • Where clause in SQL
  • Group by clause in SQL
  • Create command in SQL
  • Insert command in SQL
  • Select command in SQL
  • Select command variants in SQL
  • Update command in SQL
  • Delete command in SQL
  • Drop command in SQL
  • Truncate command in SQL
  • Commit and rollback concepts in SQL
  • SQL integration with Python
  • Table operations in SQL using Python
  • Working on multiple tables using Python and SQL
  • CRUD operations in SQL
  • REST principles
  • Creating application endpoints
  • Implementing endpoints Using Postman for API testing
  • Python, database and front-end integration concepts and implementation
  • CRUD operations on the database
  • REST principles and connectivity to databases
  • Creating a web development API for login registers and connecting it to the database
  • Deploying the API on a local server

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Section 6 - Major Project

  • Project use cases Introduction
  • Project Scenarios
  • Project life cycle
  • What is version controlling in project management
  • What is GitHub
  • Significance of GitHub in project management
  • Code submission for testing and deployment
  • Predictive analytics tools and techniques
  • Project best practices

About Data Science Course​

At Technogeeks, we offer the best Data Science course in Aundh, covering every aspect of the 

Data Science project life cycle, including – 

  • Data collection
  • Data cleaning
  • Data exploration
  • Data modeling


Our Data Science certification classes provide students with excellent hands-on experience in essential technologies needed to master data science and machine learning.

Through hands-on projects based on Python and its libraries, Machine Learning, artificial intelligence, Deep Learning, and NLP, learners will engage in live interactions with instructors, gaining the opportunity to become proficient data science professionals.

The course starts with all the prerequisites, including core Python concepts, OOPs concepts in Python, machine learning techniques, mathematical statistics, and more.

Additionally, our data science course includes mentorship from experienced instructors to guide you throughout your learning journey.

  • Core and Advanced Python Programming
  • Data Analysis with Advance Excel
  • Data Analysis with Python
  • Learn Advanced libraries like NumPy, Pandas, Matplotlib, Seaborn Visualization
  • Explore data visualization tools like Power BI and Tableau
  • Introduction to Artificial Intelligence and Machine Learning
  • Natural Language processing (NLP)
  • Deep Learning with TensorFlow and Keras
  • SQL Programming
  • Rest API with and Flask

Anyone can enroll in a data science course in Aundh, Pune, who is interested in learning data science and wants to start a career in data science.

  • Any graduate or undergraduate from any field
  • IT professionals who want to upskill their knowledge
  • Working professionals who want to switch careers in data science

At Technogeeks, Data Science Course duration is 4 Months. We cover everything from the basics to advanced topics with daily assignments and include two hands-on projects. Additionally, after completing the course, you will go through multiple mock interviews, CV preparation, and profile enhancement. 

Course Benefits​

  • Comprehensive course covers all aspects of the data science project life cycle, like data collection, data cleaning, data exploration, and data modelling, and culminates with the interpretation of data.
  • Pay only after attending one FREE TRIAL OF RECORDED LESSON.
  • No prerequisite.
  • Tips from working Data scientist, Data Analyst on how to write clean and reusable code, data analysis & machine learning models.
  • Carefully selected data science, machine learning questions to provide you with all the practice you need during training.
  • Course designed for non-IT & IT professionals.
  • Classroom & Online Training – Can switch from online training to classroom training.
  • 100% placement calls guaranteed till you get placed.
  • Working professional as instructor.
  • Hands-on Experience with Real-Time Projects.
  • Proof of concept (POC) to demonstrate or self-evaluate the concept or theory taught by the instructor. 2 – Python POC, 5 – Data Science POC.
  • Hands-on Experience with Real-Time Projects.
  • Resume Building & Mock Interviews.
  • Evaluation after each Topic completion.
  • Interview Preparation Support.

Training Projects

Technogeeks cover multiple projects in this training to make sure that candidates must be able to work in real-time.

Credit Score Data

Getting insights using python analysis and visualizations on finance credit score data.

USA Housing Data

Practice project for Linear regression using advertisement data set to predict appropriate advertisements for users.

Predicting Purchase Behaviors

Practice project on decision tree and random forest using social network data to predict if someone will purchase an item or not.

Instructor-led Data Science Live Online/Classroom Training

Checkout Latest Batch Schedule

Data Science Certification From Technogeeks

Data Science Training Completion Certificate From Technogeeks Will Help You With

  • Career Opportunities in Data Science
  • Improving Reputation as skilled professional
  • Competitive Advantage among the cohort
  • Proof of Learning
  • Establishing Professional Credibility

Batches Completed

Industry Oriented Syllabus

Designed By Expert


Happy Students

Self Assessments

Quizzes, POC


8+ Years Of Experience

Recorded Sessions

1 Year Of Access

Data Science Certification Training

Trainer’s Profile

Our trainers are experts in their fields. They simplify complex concepts for the students and make them easy to understand. They solve each and every type of student’s query. Their teaching method is more focused on real-time examples, preparing the students for industry interviews. Students will have one-on-one coaching sessions with them so that they will be able to ask questions at any time.

Key Highlights of Our Trainers:

  • Certified Professionals with Over 8 Years of In-Depth Experience
  • Imparted Knowledge to Over 2,000 Students Annually
  • Demonstrated Strong Theoretical and Practical Expertise in Their Respective Domains
  • Possess Expert-Level Subject Knowledge and Stay Current with Real-World Industry Applications

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Aundh, Pune offers a conducive environment for learning with access to various resources and opportunities.

Technogeeks provides best Data Science training with hands-on projects, ensuring practical skill development.

You can reach out to the career counselors at Technogeeks for clarification and assistance with any doubts or questions you may have during the course.

Contact Us For more information. 

Yes, Technogeeks offers online training options for those who prefer remote learning or cannot attend in-person sessions. 

Yes, upon successful completion of the Data Science course at Technogeeks, you will be provided with a Course Completion Certificate.

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