Data Science - Machine Learning & AI

Data Science – Machine Learning & AI Course In Pune


data science course in pune

Data science is a specialized field that focuses on understanding and imaging specific business, financial, manufacturing and medical research and forecasting. Data science is the process of analyzing, visualizing, extracting, managing, and storing data to gain insights from process analytics. These insights and information help companies analyze their marketing strategies, make powerful data-driven decisions, and create better ads.

GOVERNMENT CERTIFICATION

  • Central Government Certification For Data Science Program.
  • BECIL – A Govt. of India Enterprise under Ministry of Information & Broadcasting
  • Certification Valid For Private And Government Jobs Also In More Than 82 Countries.
  • Applicable As Per State Vise Government Placements.

Why You Should Choose
Data Science?

INCREASING COMPUTATIONAL POWER

Computational power has been an issue. Managing how much data we have today is becoming a challenge. It is a constant effort to increase our computational power, Here this field helps us to manage the data

WORKING ACROSS VARIOUS SECTORS

With the presence and use of data in various industries and sectors, there is no substitute for data science. Therefore, people with the right skills can have a lot of opportunities.

EASY TO LEARN

Specific data science skills and concepts are relatively easy to learn with proper attention and tuition. So we're here for 1: 1 tuition so you can learn with practice, Our structure of study makes sure you understand everything.

GREAT JOB OPPORTUNITIES

This is a field that is in great demand. There are plenty of job opportunities for career aspirants. The fastest growing science on LinkedIn is currently estimating that 11.5 million jobs will be created in 2026!

HIGHLY IN DEMAND SKILL

Data science is in high demand for specialized professionals, and it is growing. Supply, however, is slow. According to IBM, digital science will employ 28% of all digital jobs.

GROWING PROGRESS

The widespread use of data science has led to growing advances in algorithms and theories developed by researchers. Unfortunately, there are vacancies for 45 days.

Who is a Data Scientist?

A data scientist is a researcher who has to develop large quantities of big data for analysis, develop complex quantitative algorithms to organize and synthesize information, and present results to senior management with a compelling concept. Is.

The data scientist enhances business decision making by introducing maximum speed and better direction throughout the process.

A data scientist must be someone who likes to play with numbers and statistics. A strong analytical mindset combined with strong industrial knowledge is the required set of skills in a data scientist.

He should have above average communication skills and be skilled in communicating technical ideas to non-technical people.

Benefits of Data Science Course

Opportunities:

opportunities for wider education.

Positions:

Abundance of different positions.

Career demand:

A high paying career &
In demand and high price

Work Convenience:

Provides the option to
work from home

Syllabus

DATA SCIENCE SYLLABUS

WHAT YOU WILL LEARN IN DATA SCIENCE

  • Data Science Mathematics – Revising School Level Math
  • Python and R Programming Languages
  • Python Data Science Libraries
  • R Programming Data Science Libraries
  • Data Science Techniques
  • Basic Of Artificial Intelligence
  • Machine Learning
  • Data Visualization Tools
  • Big Data and Hadoop

DATA SCIENCE INTRODUCTION – MODULE I

  • Data Science and It’s Concepts
  • Scope Of Data Science
  • Data Science Business and Business Intelligence (BI) Use Cases
  • Data Science Field Discussions
  • Data Science Artificial Intelligence (AI) and AI Subset Machine Learning (ML) and ML Subset Deep Learning (DL) Involvements
  • Analytics – Introduction
  • Understanding Data, Types Of Data
  • Understanding Dataset – Structured, Unstructured and Semi Structured

DATA SCIENCE MATHEMATICS – MODULE II

  • Revising School Level Mathematics For Data Science
  • Statistics and Probabilities
  • Statistics – Descriptive Statistics
  • Statistics – Inferential Statistics
  • Statistics – Hypothesis and Hypothesis Testing
  • Linear Algebra
  • Linear Algebra – Matrix Introductions
  • Linear Algebra – Matrix Types and Practical Example
  • Linear Algebra – Matrix Arithmetic Operations
  • Linear Algebra – Scalar and Vector
  • Calculus
  • Calculus – Limit
  • Calculus – Differentials Calculus: Derivatives
  • Calculus – Integral Calculus: Integrations

DATA SCIENCE PROGRAMMING LANGUAGES – MODULE III

Python Programming Language

  • Python – Introduction
  • Python – Setup and Interpreter
  • Python – Keywords, Statements and Statements Syntax
  • Python – Variables, Literals, Data Types and Data Structure
  • Python – Operators
  • Python – Functions
  • Python – Input and Output (IO)
  • Python – Errors and Exceptions
  • Python – Modules
  • Python – classes
  • Python – Threading and Multi-threading
  • Python – Batteries
  • Python – Package Management Tools: pip and conda
  • Python – Virtual Environments

R Programming Language

  • R – Introduction
  • R – Setup and R Studio
  • R – Objects
  • R – Evaluations Of Expressions
  • R – Functions
  • R – Object Oriented Programming (OOP)
  • R – Computing on The Language
  • R – System and foreign language interfaces
  • R – Exception Handling
  • R – Debugging
  • R – Parsers
  • R – Data Science Libraries: Dplyr, Ggplot2, mlr etc.
  • R – Data imports & exports

DATABASES

  • Structure Query Language (SQL)
  • SQL – Introduction
  • SQL – Data Definition Language(DDL)
  • SQL – DDL Operations – create tables or views, alter tables or views etc.
  • SQL – Data Manipulation Language(DML)
  • SQL – DML Operations – insert, update and delete etc.
  • SQL – Select
  • SQL – Constraints
  • SQL – Normalizations
  • SQL – Joins and indexes

VISUALIZATION TOOLS

  • Tableau
  • Plotly

MACHINE LEARNING (ML) – MODULE IV

  • What is Machine Learning (ML)?
  • Introducing Supervised ML
  • Introducing Unsupervised ML
  • Introducing Reinforcement Or Semi Supervised ML
  • Supervised ML Algorithms (Regression and Classification)
  • Unsupervised ML Algorithms (Association and Clustering)
  • Reinforcement ML Algorithms
  • Python Machine Learning
  • R Programming Machine Learning

BIG DATA and HADOOP – MODULE V

  • Basic Core Java Language Conceptual Guide
  • Big Data Introduction
  • Big Data Characteristics
  • Hadoop Introduction
  • Hadoop Setup and Configurations
  • Hadoop Modules Introduction
  • Hadoop HDFS
  • Hadoop MapReduce
  • Hadoop Yarn/MapReduce Version 2

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