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Data Science & AI

Course Overview

Explore Data Science online training with real-world examples taught by industry experts. Learn key concepts using Hadoop, R programming, and machine learning. Gain insights into big data, R analytical tools, and dataset analysis. Develop skills in data transformation, visualization, and exploratory analysis. This course covers the basics of Data Science for practical applications

Learn software skills with real experts, either in live classes with videos or without videos, whichever suits you best.

Description

What is Data science?

Data science is the systematic application of scientific methods and mathematical principles to efficiently process, analyze, and extract meaningful insights from data. It leverages powerful hardware, sophisticated algorithms, and software programs to transform raw data into valuable knowledge, facilitating informed decision-making in commercial and professional contexts.

Course Objectives

Upon finishing the Data Science Online Course at Naresh i Technologies, you will gain expertise and eligibility in the following:

  1. Comprehensive understanding of Data Science.
  2. Proficiency in analyzing big data.
  3. Competence in Data Mining.
  4. Skill in working with Statistics.
  5. Proficiency in utilizing various tools such as Tableau and MapReduce.
  6. Ability to create decision trees.
  7. Exploration of Big Data concepts.

This course equips you with the knowledge and skills needed to excel in the field of Data Science, covering a spectrum of topics from data analysis to the application of different tools for effective decision-making.

Prerequisites
  • This course is suitable for any IT professional with a basic understanding of:

    • Mathematics
    • Statistics
    • Any Programming Language

    No advanced knowledge is necessary, making it accessible for individuals with a foundational understanding of these concepts.

Course Curriculum

  • Introduction to Data Science
  • Python – Basics
  • Python - Data Types & Utilities
  • Set
  • Tuple
  • Dictionary and Dictionary comprehension
  • Functions
  • Packages
  • Map Reduce
  • OOPs
  • Class & Object
  • Methods
  • Python Decorator
  • Polymorphism
  • Python - Production Level
  • Pickling & Unpickling
  • Pandas
  • Numpy
  • Matplotlib
  • Seaborn
  • Scipy
  • Statsmodels

  • Set Theory
  • Combinatorics
  • Probability
  • Linear Algebra
  • Euclidean Distance & Manhattan Distance
  • Calculus
  • Indices & Logarithms

  • Introduction
  • Types of Data
  • Measures of Central Tendency
  • Descriptive statistic Measures of symmetry
  • Measurement of Spread
  • Measurement of Spread
  • Levels of Data Measurement
  • Variable
  • Frequency Distribution Table
  • Types of Variables
  • Frequency Distribution Table
  • Correlation, Regression & Collinearity
  • Others
  • Bias and Variance in ML
  • Entropy in ML
  • Information Gain
  • Surprise in ML
  • Loss Function & Cost Function
  • Inferential Statistics

  • Introduction
  • Keys
  • Constraints
  • CRUD Operations
  • SQL Languages
  • SQL Commands
  • Operators
  • Wild Cards
  • Aggregate Functions
  • SQL Joins

  • EDA
  • Data Visualisation
  • ML Introduction
  • Important Element of Machine Learning
  • Multiclass Classification
  • Data Processing
  • Regression
  • Evaluation Metrics for Regression
  • Classification
  • Clustering
  • Introduction
  • K-Means Clustering
  • Hierarchical Clustering
  • Agglomerative clustering
  • DBSCAN Clustering
  • Association Rules
  • Recommendation Engines
  • Time Series & Forecasting
  • Model Selection & Evaluation
  • Over Fitting & Under Fitting
  • Others
  • ML Pipeline
  • ML Model Deployment in Flask

  • Introduction
  • Basic Report Design
  • Visual Sync, Grouping
  • Hierarchies, Filters
  • Power Query
  • DAX Functions

  • Deep learning at Glance
  • Training MLP: Backpropagation
  • Cost Function
  • Gradient Descent Backpropagation - Vanishing and Exploding Gradient
  • Problem
  • Introduce to Py-Torch
  • Regularization
  • Optmizers
  • Hyperparameters and tuning of the same
  • TensorFlow Framework
  • ANN (Artificial Neural Network)
  • Py-Torch Library
  • RNN (Recurrent Neural Network)
  • Basics of Image Processing
  • Convolutional Neural Networks (CNN)

  • Introduction to Natural Language Processing (NLP)
  • Document Vectorization
  • Twitter Sentiment Analysis Using Textblob
  • Spacy Library

  • Human vision vs Computer vision
  • Image Processing with OpenCV
  • Video Processing with OpenCV
  • Reinforcement Learning
  • OPEN AI
  • Time Series and Forecasting
  • MakerSuite Google
  • Azure ML
Who can learn this course

  • IT professionals seeking to advance their career in Development or Data Science.
  • College students pursuing B.E, B.Tech, BSC, MCA, M.Sc Computers, M.Tech, BCA, or BCom in any stream.
  • Recent graduates keen on enhancing their skills.

This course is suitable for a broad audience, providing valuable knowledge and practical skills for both experienced IT professionals and individuals starting their career journey.

Average package of course (Data Science & AI)

90% Avg
salary hike
5L Avg
Package
Training Features
Comprehensive Course Curriculum

Elevate your career with essential soft skills training for effective communication, leadership, and professional success.

Experienced Industry Professionals

Learn from trainers with extensive experience in the industry, offering real-world insights.

24/7 Learning Access

Enjoy round-the-clock access to course materials and resources for flexible learning.

Comprehensive Placement Programs

Benefit from specialized programs focused on securing job opportunities post-training.

Hands-on Practice

Learn by doing with hands-on practice, mastering skills through real-world projects

Lab Facility with Expert Mentors

State-of-the-art lab facility, guided by experienced mentors, ensures hands-on learning excellence in every session

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