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Python with Data Science Training & Certification

Get Trained & Get Certified in DS

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Description

Data Science is one of the most in-demand and fastest-growing fields in today’s digital world. It combines mathematics, business intelligence, modern tools, and machine learning techniques to transform raw data into meaningful insights.

With the rapid growth of data across industries, organizations rely on Data Science to identify patterns, predict future trends, and make smart, data-driven decisions. From startups to global companies, Data Science plays a key role in innovation and business success.

At Systems and Networks Technologies (SNTI), our Data Science training focuses on industry-relevant skills, including Python, data analysis, visualization, and machine learning, ensuring students are ready for real-world challenges.

Ideal Candidates for This Course:

Curriculum

Total Hours

0 Hours

Data Science with Python

Introduction to Data Science

􀀀 What is Data Science?
􀀀 What does data science involves
􀀀 Life cycle of Data Science
􀀀 Tools of Data Science

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

Setup and Essentials

􀀀 Introduction to python
􀀀 Software installation
􀀀 Write your first program in Python

Objects & Data structures

􀀀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

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

Functions & 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

File and exceptions 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

Python inbuilt modules

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

OOPs Concepts

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

Python Modues

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

Install packages on python

􀀀 Introduction to pip, easy install
􀀀 Multi-threading

􀀀 Multiprocessing

Machine Learning with Python

􀀀 Understanding Machine Learning
􀀀 Scope of ML
􀀀 Supervised and Unsupervised learning

Mathematical Computing with Python (Numpy)

􀀀 Introduction to Numpy :
􀀀 Introduction to numpy arrays
􀀀 How to Access Array Elements?
􀀀 Indexing, Slicing, Iteration, Indexing with Boolean Arrays
􀀀 Dealing with Flat les using numpy
􀀀 Mathematical functions
􀀀 Statistical functions (mean, median, average, standard deviation)
􀀀 Operations with arrays
􀀀 Introduction to Scienti c Computing (Scipy) :
􀀀 Save a search as a report
􀀀 Editing reports 􀀀 Creating reports with visualizations charts and
tables
􀀀 Data Manipulation with Pandas
􀀀 Introduction to Pandas :
􀀀 De ning data structures
􀀀 Understanding Dataframes
􀀀 Importing Data from various sources
􀀀 Csv, txt, excel etc)
􀀀 Missing values
􀀀 Data Operations
􀀀 File read operations
􀀀 Descriptive statistics

􀀀 Data Visualization using Matplotlib
􀀀 Create plots like scatter plot, histogram, bar graph, pie chart
using Matplotlib Grids, axes, plots Markers, colour, fonts and
styling.

Data Visualization using Tableau BI tool

􀀀 Comparison Between Tableau & Programming Based
􀀀 Data Visualization
􀀀 Need Of Tableau
􀀀 Types Of Data Sources Supported By Tableau For Report
Development
􀀀 How To Build Report & Dashboard in Tableau
􀀀 How To Build Charts In Tableau
􀀀 Data Visualization Using Tableau Features

Machine learning Algorithms

􀀀 Introduction to Data Science
􀀀 Introduction to Artificial Intelligence
􀀀 Introduction to Machine Learning
􀀀 Need of Machine learning in forecasting
􀀀 Demand of forecasting analytics in current industrial trends
􀀀 Introduction to Machine Learning Algorithms Categories
􀀀 Introduction to Natural Language Processing (NLP)
􀀀 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 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.

Introduction to Deep Learning

􀀀 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 API with Flask /Django and
Python
􀀀 REST principles 􀀀 Creating application endpoints
􀀀 Implementing endpoints
􀀀 Using Postman for API testing

API Integration with Database for Web 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
􀀀 Deploying the API on a local server

Main Project

􀀀 Assignment and Live Examples
􀀀 Case studies