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Data Science Course in Pune | Training | Certification | Placement

    Data Science Course in Pune

    Data Science Training in Pune | Data Science Classes 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 Development Environment
    • 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 in Python
    • 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 neighbours 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.

            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 neighbours 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 practical’s 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 TRIAL OF RECORDED LESSON
  • 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.

Best Blended Syllabus for Data Science Course in Pune from a 100% Placement-Oriented Training Institute

Technogeeks’s data science training course is for people seeking certification in Data Science, Artificial Intelligence, Machine Learning, Deep Learning, and Natural Language Processing (NLP). The course is conducted by working IT professionals in the field. This training will assist you in mapping your profile based on IT standards and project requirements from various domains.


Why we are Best Institute for Data Science in Pune?

Our software training institute’s Data Science course is one of the most comprehensive in Pune, covering all aspects of the Data Science project life cycle starting from Data Collection, Data Scrubbing, Data Exploration, Data Modelling, and culminating with Interpretation of Data.

Our Data Science certification classes provide students with one of the best hands-on exposures to essential technologies with Real-time Projects such as Python, Libraries, Machine Learning, Artificial Intelligence, Deep Learning, and Natural Language Processing through live interaction with practitioners, practical laboratories, and industry projects (NLP).

The goal of this data science course syllabus is to get you started on your data science journey to make a successful start in data science professions like data engineering, data analyst, and the most coveted job of the 21st century i.e. "data scientist".


Professionals with no prior experience in the industry can quickly begin with this Data Science certification training since you will obtain a complete understanding of the fundamental concepts.

All prerequisites are covered from the beginning of the course, including python, logic construction abilities, machine learning techniques, and essential statistics.


Tools & Techniques Covered in Data Science Course Training

  • Programming Language
    • Core Python & Advance Python
  • Python Run Environments
    • Google Colab, Jupyter Notebook
  • Python Distribution
    • Anaconda
    • IDE
      • Pycharm
      • Data Analysis
        • Numpy, Pandas
        • Data Visualization
          • Matplotlib, Seaborn
        • Machine Learning
          • Regression Techniques
          • Linear Regression
          • scikit-learn
          • KNN algorithm
          • Decision Tree and Random Forest
        • Supervised Learning Models
          • Support Vector Machine
          • Unsupervised Learning Algorithms
            • Principal Component Analysis (PCA)
            • K-means Clustering Association Rule Learning
            • Natural Language Processing
              • Natural Language Toolkit (NTLK)
            • Deep Learning
              • Keras
              • TensorFlow
              • API Development & Unit Testing
                • REST API
                • Postman
                • Flask
              • Rest API Integration With Databases for Web App Development
                • CRUD operations
                • REST Principles

KYC - Know Your (Data Science) Training Course

  • Batches Completed – 160+
  • Students - 3200+
  • Course Duration: 60 Hours
  • 30+ Assignments with 9+ Real-time Projects & POC
  • Assignment Duration: 40 hours
  • Modules: 18

What will you learn in Data Science Certification Course?

In the data science certification course, we offer the dual program in which our candidates learn about python programming language and data science modules, the complete data science syllabus details are available below:

Section-1

Python Programming Language

Section-2

API integration using flask

Section-3

Data Analytics using NumPy, Pandas

Section-4

Data Visualisation using Matplotlib, Seaborn

Section - 5

Machine learning (ML) with Regression, Classification, Clustering & Association

Section-6

Artificial Intelligence (AI)

Section-7

Deep Learning (DL) using TensorFlow & Keras

Section-8

NLP using NLTK

FAQ

Why Data Science Is Such A Hot Career Right Now?

In the 21st century, the new oil is "Data", and "Data Science" is the data refinery to get the insights from RAW data! As internet use became widespread with it data generation is exploded and obviously, there was the need to understand the data to use it for data-driven decision making. So there is a need for highly skilled "data science" professionals to get business insights before competitors cache up with your business.

How Much Mathematics Does I Need to Learn to Get Into Data Science?

Calculus, linear algebra, and statistics are three math disciplines that will regularly show up if you ask any data science professional for the data science prerequisite. The good news is that statistics is the only math that you need to master for most data science profiles.

What are core Data Science Skills and which are emerging skills that I need to look for?

The core data science skills that an aspiring data science professional needs to add to their skills resume are: Python, Machine Learning, Statistics, Data Visualisation, Scikit-learn, R, Business understanding, communication, Math, SQL, Critical Thinking, ETL - preparation, and Excel.

Hot/Emerging DS Skills you should look for: Deep Learning, TensorFlow, Apache Spark, NLP - Text Processing, Pytorch, Bigdata tools, Unstructured data, No-SQL, Hadoop, Kaggle & Scala.

What Will Data Science Jobs Look Like In The Future?

Like any other field, data science is always evolving with technology innovation and market maturity. According to experts, getting data ready for analysis takes up to 80% of a data scientist's time. But as automation progressing in data science it becoming like web designing where you don't need to write code but the tool will do that for you. As quantum computing & quantum information science are advancing data scientists must understand quantum mechanics and how to use a quantum algorithm to solve a particular problem.

What is the Best Python IDE for Data Science?

PyCharm is undoubtedly the most well-known Python IDE. It has an excellent debugger, and works smoothly with git, and works easily with the multiple Python versions with the virtual environment. Reindexing is relatively fast with an initiative interface. The community version is free and has all essential features.

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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