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Best Data Science Course in Pune with Placement | Technogeeks

    Data Science Course in Pune

    Data Science Training in Pune

    MODULE 1 – INTRODUCTION TO PYTHON

    • What is Python and brief history
    • Why Python and who use Python
    • Discussion on Python 2 and 3
    • Unique features of Python
    • Discussion on various IDE’s
    • Demonstration of practical use cases
    • Python use cases using data analysis

    MODULE 2 – SETTING UP AND INSTALLATIONS

    • Installing python
    • Setting up Python environment for development
    • Installation of Jupyter Notebook
    • How to access our course material using Jupyter
    • Write your first program in python

    MODULE 3 – PYTHON OBJECT AND DATA STRUCTURES OPERATIONS

    • Introduction to Python objects
    • Python built-in functions
    • Number objects and operations
    • Variable assignment and keywords, String objects and operations
    • Print formatting with strings
    • List objects and operations
    • Tuple objects and operations
    • Dictionary objects and operations
    • Sets and Boolean
    • Object and data structures assessment test

    MODULE 4 – PYTHON STATEMENTS

  • Introduction to Python statements
  • If, elif and else statements
  • Comparison operators
  • Chained comparison operators
  • What are loops
  • For loops
  • While loops
  • Useful operator
  • List comprehensions
  • Statement assessment test
  • Game challenge

  • MODULE 5 – UDF FUNCTIONS AND METHODS

  • Methods
  • What are various types of functions
  • Creating and calling user defined functions
  • Function practice exercises
  • Lambda Expressions
  • Map and filter
  • Nested statements and scope
  • Args and kwargs
  • Functions and methods assignment

  • Milestone Project using Python

    MODULE 6 – FILE AND EXCEPTION HANDLING

  • Process files using python
  • Read/write and append file object
  • File functions
  • File pointer and operations
  • Introduction to error handling
  • Try, except and finally
  • Python standard exceptions
  • User defined exceptions
  • Unit testing
  • File and exceptions assignment

  • MODULE 7 – PYTHON MODULES AND PACKAGES PYTHON INBUILT MODULES

  • Python inbuilt modules
  • Creating UDM-User defined modules
  • Passing command line arguments
  • Writing packages
  • Define PYTHONPATH
  • __name__ and __main__

  • MODULE 8 – OBJECT ORIENTED PROGRAMMING

  • Object oriented features
  • Implement object oriented with Python
  • Creating classes and objects
  • Creating class attributes
  • Creating methods in a class
  • Inheritance
  • Polymorphism
  • Special methods for class

  • Assignment - Creating a python script to replicate deposits and withdrawals in a bank with appropriate classes and UDFs

    MODULE 9 – ADVANCED PYTHON MODULES

  • Collections module
  • Datetime
  • Python debugger
  • Timing your code
  • Regular expressions
  • StringIO
  • Python decorators
  • Python generators

  • MODULE 10 – PACKAGE INSTALLATION AND PARALLEL PROCESSING

  • Install packages on python
  • Introduction to pip, easy install
  • Multithreading
  • Multiprocessing

  • MODULE 11 – INTRODUCTION TO MACHINE LEARNING WITH PYTHON

  • Understanding Machine Learning
  • Scope of ML
  • Supervised and Unsupervised learning
  • Milestone Project – 2

  • MODULE 12 – DATA ANALYSIS WITH PYTHON

  • Introduction to data analysis
  • Why Data analysis?
  • Data analysis and Artificial Intelligence Bridge
  • Introduction to Data Analysis libraries
  • Data analysis introduction assignment challenge

  • MODULE 13 – DATA ANALYSIS USING NUMPY

  • Introduction to Numpy arrays
  • Creating and applying functions
  • Numpy Indexing and selection
  • Numpy Operations
  • Exercise and assignment challenge

  • MODULE 14 – PANDAS AND ADVANCED ANALYSIS

    Pandas series
  • Introduction to DataFrames
  • Missing data
  • Groupby
  • Merging, joining and Concatenating
  • Operations
  • Data Input and Output
  • Pandas in depth coding exercises

  • POC - Analysis of e-commerce dataset using pandas POC - Getting insights on employee salaries data using data analysis in python

    MODULE 15 – DATA VISUALIZATION WITH PYTHON

  • Plotting using Matplotlib
  • Plotting Numpy arrays
  • Plotting using object-oriented approach
  • Subplots using matplotlib
  • Matplotlib attributes and functions
  • Matplotlib exercises
  • Categorical Plot using Seaborn
  • Distributional plots using Seaborn
  • Matrix plots
  • Grids
  • Seaborn exercises

  • Seaborn visualization

  • Categorical Plot using Seaborn
  • Distributional plots using Seaborn
  • Matrix plots
  • Grids
  • Seaborn exercises

  • Project - Getting insights using python analysis and visualizations on finance credit score data.
    Pandas built-in data visualization Data visualization assignment

    MODULE 16- MACHINE LEARNING (DS) ALGORITHMS

  • 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.
  • Exercise on K- Nearest neighbors using Scikit 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.

  • Linear Regression with Python

  • 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 Scikit 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

  • PCA introduction
  • 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..

    Natural Language processing with Deep Learning

  • Introduction to Natural Language processing
  • NLTK Python library.
  • 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 Net and Deep Learning
  • What is TensorFlow?
  • TensorFlow Installation.
  • TensorFlow basics.
  • MNIST with Multilayer perceptron
  • TensorFlow with Contrib Learn
  • TensorFlow Exercise
  • What is Keras?
  • Keras Basics.
  • Pipeline implementation using Keras.
  • MNIST implementation with Keras.

  • MODULE 17 – REST API WITH FLASK AND PYTHON

  • REST principles
  • Creating application endpoints
  • Implementing endpoints
  • Using Postman for API testing

  • MODULE 18 - REST API INTEGRATION WITH DATABASES FOR WEB APP DEVELOPMENT

  • CRUD operations on database.
  • REST principles and connectivity to databases.
  • Creating a web development API for login registers and connecting it to the database.
  • Deploy the API on a local server.

How will I execute practicals and code in Technogeeks related to Data Science with Python Certification Course?

You will do your Assignments/Case Studies using Jupyter Notebook and Pycharm that will be installed on your system and access details will be shared during the class. For any doubt, the Technogeeks support team will promptly assist you. Also, if in case you have any system configuration issues , don't worry we have Google Collab as solution available.


Benefits of Data Science with Python Certification

  • Pay only after Attending one FREE DEMO CLASS
  • No prerequisite.
  • Course designed for non-IT as well as IT professionals.
  • You can join multiple batches once enrolled.
  • 100% practical oriented approach.
  • Placement assistance in MNC.
  • Working professional as instructor.
  • Proof of concept (POC) to demonstrate or self evaluate the concept or theory taught by the instructor. 2 - Python POC, 5 - Data Science POC.

Data science course in Pune

Technogeeks provides the best combination of Python language with data analytics,Data Science , Artificial Intelligence (AI), Machine learning (ML), Deep learning , Natural language processing (NLP) and Deep Learning.

Data Science Course Description

We start with python session and help to become programmer, after that we help to work on POCs and projects with Python, SQL and data analytics and visualization libraries

How to apply for jobs and placement

Post training completion, Technogeeks help to update profile (Resume) and apply for data science jobs in Pune and other locations in India

Technogeeks Students Reviews on Google

Rated 4.8/5 (4.8/5 based on 974 student reviews)

Batch Schedule

Date Type Timings
3rd Aug Weekdays (Tue to Fri) 8 AM to 9 AM
17th Aug Weekdays (Tue to Fri) 8 AM to 9 AM
7th Aug Weekend (Sat to Sun) 11 AM to 1 PM
21st Aug Weekend (Sat to Sun) 9 AM to 11 AM

Technogeeks cover Multiple Projects in This training to make sure that candidates must be able to work in real time

  • Milestone Project Machine Learning with Python (Module
  • Getting insights using python analysis and visualizations on finance credit score data
  • Project on Linear regression using USA_HOUSING data
  • Practice project for Linear regression using advertisement data set to predict appropriate advertisements for users
  • Project on Logistic regression using Dogs and horses’ dataset
  • Practice project on KNN algorithm
  • Practice project on decision tree and random forest using social network data to predict if someone will purchase an item or not
  • SVM project with telecom dataset to predict the users portability

  • False alarm detection system