Data Science Specialization

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Data Science Specialization

A Data Science Specialist is a professional who applies advanced analytical, statistical, and computational techniques to extract meaningful insights from data. They play a crucial role in solving complex problems, predicting trends, and enabling data-driven decision-making across various industries. In this course we will give you a strong understanding of how to start with data with course revolving around working on various algorithms and libraries along with working on visulaization using Tableau.

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Why Data Science Specialization?

The career growth prospects in data science are highly promising due to the increasing demand for data-driven decision-making across industries. Here's an overview of the opportunities and growth trajectory:

web development job demand Industries Employing Data Science Specialists
The demand for data science professionals is consistently growing across sectors such as healthcare, finance, technology, retail, logistics and supply chain etc.

data science career pathaway Diverse Career Pathways
Data science offers a wide range of roles based on expertise and interests, such as: Data Scientist, Machine Learning Scientist, Applied Data Scientist, AI Specialist, Quantitative Analyst.

artificial intelligence competitive salaries High Salaries
The salary of a Data Science Specialist varies based on factors such as experience, location, industry, and the organization's size. Generally, data science specialists command high salaries due to their technical expertise and the growing demand for data-driven decision-making.

opportunity Opportunities after Specialization
Professionals can specialize in areas like: Natural Language Processing (NLP), Computer Vision, Big Data Engineering, Deep Learning.

Key Takeaways

  1. certificate Industry recognized Certificate
  2. industry Industry led Content
  3. infinity Lifetime access to LMS
  4. notes Notes, Codes & Lectures
  5. resume Value addition in Resume
  6. leadership Learn from Industry Leaders
  7. customerservice Doubt Clearing Support

Course Curriculum

Introduction to Programming & Programming Language
Introduction to Programming, Introduction to Programming Language, How to Install?, Execution steps, Interactive Shell, User Interface or IDE, Creating Your First Program

Memory Management & Datatypes
Memory Management and Garbage Collection, Object Creation and Deletion, Object Properties, Data Types and Operations, Numbers, String Operations

Data Types
List Tuple, Dictionary, Other Core Types

Statements
Statements and Syntax, Assignments, Expressions and prints, If tests and Syntax Rules, While and For Loops

Operations
File Operations, Opening a file, Using Files, Other File tools, Check If File or Directory Exists, COPY and PASTE File using shutil.copy(), Rename File and Directory using os.rename(), ZIP file with example

Functions
Function Definition and Call, Function Scope, Function Arguments

Modules and Packages
Module Creations and Usage, Types of package, Package creation, Importing packages

Exception Handling
Default Exception Handler, Except Exceptions, Raise an exception, User defined exception

Accessing Internet
Accessing internet data & manipulating XML, using Programming Language.

Important
We will be covering all concepts of programming language using Python.
Classes
Classes and Instances, Classes method calls, Class methods, Instance methods, Static methods

Object Oriented Programming
Abstraction, Encapsulation, Polymorphism, Types of Polymorphism, Inheritance, Types of inheritance

Projects
You will be able to make multiple projects using Python which will help you in understanding vital concepts cleared.
Introduction
Overview of Datascience, Starting with Data Science, Overview of this training, Overview of Data Science, Terminologies in Data Science

Machine Learning
Introduction to Machine Learning, What is Machine Learning?, Types of Machine Learning, How Machine Learning works? Usage of Machine Learning.

Revisiting Python
Introduction to Python Programming, Introduction to Python and Anaconda, Working on Jupyter IDE, Conditional statements in Python, Different types of data, List,Tuple Dictionary, Loops

Function & Packages
Function, Packages, Installing different packages of Python
Python Libraries
Working on Various Python Libraries, NumPy, Pandas, Matplotlib, Scikit-learn

NumPy
Working on NumPy, Introduction to Numpy Array, Creating arrays of different Dimensions, Indexing, Data processing using Array

Pandas
Data Analysis using Pandas, Introduction to Pandas, Data Type of Pandas, Creating Dataframe using Pandas, Importing Dataset using Pandas, Various operations on data using Pandas tools

Matplotlib
Data visualization using Matplotlib, Plotting data using Matplotlib library, Using various tools of Matplotlib, Types of Graph, Implementation of different types of Graphs

Scikit-Learn
Applying Algorithms on Dataset using Scikit-learn, Data splitting, Using different algorithms for dataset, Prediction, Score check
Machine Learning Algorithms
Regression analysis
Simple linear regression
Multi linear regression
Classification
Binary class classification
Multi class classification
Support Vector Machine
KNN algorithm
Datasets
IRIS Dataset
BMI Male Female Dataset
MNIST Dataset
DIGIT Dataset
Titanic Dataset
CIFAR Dataset

Projects
1. Area Price Prediction Project
2. Gender Classification Project
3. Iris Flower Species Classification
4. Digit Recognition Project
5. Survival Prediction on Titanic Project
6. Object Recognition Project
7. Insurance Purchase Prediction Project

Data Visualization Techniques
Working on Tableau
Tableau Projects
Classification
Binary class classification
Multi class classification
Support Vector Machine
KNN algorithm
Concepts of Statistics

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