 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
 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
 Introduction to Python objects
 Python builtin 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
 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
 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
 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
 Python inbuilt modules
 Creating UDMUser defined modules
 Passing command line arguments
 Writing packages
 Define PYTHONPATH
 __name__ and __main__
 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
 Collections module
 Datetime
 Python debugger
 Timing your code
 Regular expressions
 StringIO
 Python decorators
 Python generators
 Install packages on python
 Introduction to pip, easy install
 Multithreading
 Multiprocessing
 Understanding Machine Learning
 Scope of ML
 Supervised and Unsupervised learning
 Milestone Project – 2
 Introduction to data analysis
 Why Data analysis?
 Data analysis and Artificial Intelligence Bridge
 Introduction to Data Analysis libraries
 Data analysis introduction assignment challenge
 Introduction to Numpy arrays
 Creating and applying functions
 Numpy Indexing and selection
 Numpy Operations
 Exercise and assignment challenge
 Introduction to DataFrames
 Missing data
 Groupby
 Merging, joining and Concatenating
 Operations
 Data Input and Output
 Pandas in depth coding exercises
 Plotting using Matplotlib
 Plotting Numpy arrays
 Plotting using objectoriented approach
 Subplots using matplotlib
 Matplotlib attributes and functions
 Matplotlib exercises
 Categorical Plot using Seaborn
 Distributional plots using Seaborn
 Matrix plots
 Grids
 Seaborn exercises
 Categorical Plot using Seaborn
 Distributional plots using Seaborn
 Matrix plots
 Grids
 Seaborn exercises
 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 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.
 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.
 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.
 Linearly separable data
 Nonlinearly separable data
 SVM project with telecom dataset to predict the users portability
 PCA introduction
 Need for PCA
 Implementation to select a model on breastcancer dataset
 Kmeans clustering intuition
 Implementation of Kmeans 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
 A priori theory and explanation
 Market basket analysis.
 Implementation of Apriori
 Evaluation of association learning
 Introduction to Natural Language processing
 NLTK Python library.
 Exercise on NLTK
 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.
 REST principles
 Creating application endpoints
 Implementing endpoints
 Using Postman for API testing
 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.
Data Science Course in Pune
Data Science Training in Pune  Data Science Classes in Pune
MODULE 1 – INTRODUCTION TO PYTHON
MODULE 2 – SETTING UP AND INSTALLATIONS
MODULE 3 – PYTHON OBJECT AND DATA STRUCTURES OPERATIONS
MODULE 4 – PYTHON STATEMENTS
MODULE 5 – UDF FUNCTIONS AND METHODS
Milestone Project using Python
MODULE 6 – FILE AND EXCEPTION HANDLING
MODULE 7 – PYTHON MODULES AND PACKAGES PYTHON INBUILT MODULES
MODULE 8 – OBJECT ORIENTED PROGRAMMING
Assignment  Creating a python script to replicate deposits and withdrawals in a bank with appropriate classes and UDFs
MODULE 9 – ADVANCED PYTHON MODULES
MODULE 10 – PACKAGE INSTALLATION AND PARALLEL PROCESSING
MODULE 11 – INTRODUCTION TO MACHINE LEARNING WITH PYTHON
MODULE 12 – DATA ANALYSIS WITH PYTHON
MODULE 13 – DATA ANALYSIS USING NUMPY
MODULE 14 – PANDAS AND ADVANCED ANALYSIS
Pandas series
POC  Analysis of ecommerce dataset using pandas POC  Getting insights on employee salaries data using data analysis in python
MODULE 15 – DATA VISUALIZATION WITH PYTHON
Seaborn visualization
Project  Getting insights using python analysis and visualizations on finance credit score data.
Pandas builtin data visualization Data visualization assignment
MODULE 16 MACHINE LEARNING (DS) ALGORITHMS
Linear Regression with Python
K Nearest neighbours using Python
Decision tree and Random forest with python
Support vector machines
Principal component analysis
Model evaluation
Bias variance tradeoff
Accuracy paradox
CAP curve and analysis
Clustering in unsupervised learning
Association algorithms
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
POC  Apply NLP techniques to understand reviews given by customers in a dataset and predict if a review is good/bad without human intervention.
MODULE 17 – REST API WITH FLASK AND PYTHON
MODULE 18  REST API INTEGRATION WITH DATABASES FOR WEB APP DEVELOPMENT
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 nonIT 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% PlacementOriented 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 handson exposures to essential technologies with Realtime 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
 scikitlearn
 KNN algorithm
 Decision Tree and Random Forest

Supervised Learning Models
 Support Vector Machine

Unsupervised Learning Algorithms
 Principal Component Analysis (PCA) Kmeans 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+ Realtime 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:
Section1
Python Programming Language
Section2
API integration using flask
Section3
Data Analytics using NumPy, Pandas
Section4
Data Visualisation using Matplotlib, Seaborn
Section  5
Machine learning (ML) with Regression, Classification, Clustering & Association
Section6
Artificial Intelligence (AI)
Section7
Deep Learning (DL) using TensorFlow & Keras
Section8
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 datadriven 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, Scikitlearn, 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, NoSQL, 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 wellknown 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.
Batch Schedule
Type  Book Your Seat Now! 

Weekdays (Tue to Fri)  
Weekend (Sat to Sun) 
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