Full stack data science with AI

  • ✅ 7 Months Online Program
  • ✅ Multiple Live Projects
  • ✅ Online-live sessions with Industry Experts
  • ✅ Industry-graded and Market-Ready Skills
  • ✅ 100% Job Assistance
  • ✅ Personalized progressive Learning.
  • ✅ Expert and Industry-experienced Trainers
  • ✅ Earn certification without a halt in your job

Full stack data science with AI

Gain Advanced Industry Skills

The Online Full Stack Data Science & AI Training course offers a thorough and immersive program meticulously crafted to empower participants with the essential knowledge and competencies crucial for success in the dynamic realm of data science and artificial intelligence (AI). Developed with precision, this course spans across a spectrum of critical subjects, encompassing data collection methodologies, preprocessing techniques, advanced analysis methodologies, machine learning principles, deep learning advancements, and practical applications of AI. Delivered in an online training format, participants have the flexibility to engage with course materials from anywhere, at any time, allowing for seamless integration into their schedules and lifestyles. Through a blend of interactive modules, virtual labs, and instructor-led sessions, participants will delve into the intricacies of handling diverse real-world datasets and honing their proficiency in implementing cutting-edge AI algorithms. Whether pursuing a career shift, seeking professional advancement, or simply expanding one's expertise, this online training course provides a comprehensive platform for individuals to embark on their journey towards mastery in data science and AI. Learn software skills with real experts, either in live classes with videos or without videos, whichever suits you best.

Prerequisites

Who can learn? (Targeted Audience)

Who can Learn: Anyone can learn data science, regardless of their background, by gaining proficiency in relevant mathematical and statistical concepts. Here are some ways to learn data science. Anyone committed to learning and developing the necessary skills can become a data scientist.

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Our Full Stack Data Science with AI program offers a comprehensive online learning experience, blending virtual sessions with hands-on support. This program will enhance your skills in data science, machine learning, and artificial intelligence, equipping you with the expertise to tackle complex data challenges. Dive into the latest tools, methods, and real-world applications to become a data science expert.

Full Stack Data Science with AI

Eligibility : Bachelor's degree or relevant industry experience.

Duration : 7 Months

Enrollment : Fully online – Start anytime throughout the year

What Data Science & AI covered ?

The primary necessity of the Full Stack Data Science & AI course are as follows:

⦁ Introduction to Data Science & AI: Provide an overview of data science and AI concepts, methodologies, and applications.

⦁ Data Collection and Preprocessing: Gain skills in collecting data from various sources and preprocessing it for analysis.

⦁ Exploratory Data Analysis (EDA): Learn how to perform EDA to understand the structure and characteristics of datasets.

⦁ Statistical Analysis: Understand basic and advanced statistical techniques for analyzing data and deriving insights.

⦁ Machine Learning: Explore supervised, unsupervised, and reinforcement learning algorithms for predictive modeling and pattern recognition.

⦁ Deep Learning: Develop an understanding of neural networks, deep learning architectures, and techniques for training and evaluating deep learning models.

⦁ AI Applications: Learn about real-world applications of AI, including natural language processing (NLP), computer vision, and recommendation systems.

⦁ Model Deployment: Explore techniques for deploying machine learning and deep learning models into production environments.

⦁ Ethical and Legal Considerations: Understand ethical and legal issues surrounding data science and AI, including privacy, bias, and fairness.

⦁ Project Development: Work on hands-on projects and case studies to apply learned concepts and techniques in real-world scenarios.

Why This Course?

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How the program will be conducted

LMS Technologies with its start-of- art class rooms and Lab infrastructure at Noida offer the best and most conducive learning environment, with a team of highly skilled trainers having years of industry experience. Classroom trainings will be conducted on a daily basis. Practical exercises are provided for the topics conducted on daily basis to be worked upon during the lab session. Online session conducted through the virtual classroom also have the same program flow with theory and practical sessions. Our Labs can be accessed online from across the world allowing our online training student to make the best use of the infrastructure from the comfort of their home.

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Full stack data science with AI

Our dedicated team of AI and Data Science experts brings a wealth of industry experience and academic knowledge to guide you through every aspect of the program. With a focus on real-world applications, our mentors offer personalized support, practical insights, and cutting-edge knowledge in machine learning, deep learning, and data analytics.

We are proud to collaborate with professionals and researchers who have made significant contributions to the field of AI and Data Science. Our mentors are committed to equipping students with the skills needed to excel in a rapidly evolving technological landscape.

Program Highlights

See which benefits you can derive from joining this program.

Online Program

  • 7-month online program
  • Online Lab Sessions
  • Highly Experienced Faculties

Collaborations

  • LMS has collaborated with many eminent Universities and Organizations across the Globe to exchange the knowledge.

Dedicated Support Team for your Academic Journey

  • Industry Experts Live Sessions.
  • Grievance Redressal System
  • Dedicated Tech & Academic Support on how to leverage the platform features.

Become Job-ready

  • Real-world case studies to build practical skills
  • Hands-on exposure to analytics tools & techniques
  • Learn industry insights through multiple industry knowledge sessions

Program Curriculum

An overview of what you will learn from this program.

  • Introduction to Jupyter Notebook
  • Getting Started with Data Science
  • Unit Introduction

  • Python Introduction
  • Python Data Structures: Lists and Arrays
  • Comprehension and Branching
  • Python: Conditions and Methods
  • Functions in Python
  • Introduction to Numpy
  • Operations in Numpy
  • Introduction to Pandas
  • Functions in Pandas
  • Operations in Pandas
  • Project 1: Satellite Image Data Analysis using NumPy
  • Functions

  • Introduction to Probability Theory
  • Permutations and Combinations
  • Quantitative Descriptors
  • Bayesian Programming in Python
  • Continuous Probability Distributions (Gaussian, Exponential)
  • Discrete Probability Distributions (Binomial, Poisson)
  • Testing the Sample Means
  • Central Limit Theorem
  • Standard Deviation and Variance
  • Probability Distributions [Histogram, BoxPlot]
  • Z-Test & T-Test Analysis

  • Data Acquisition
  • Data Wrangling
  • Data Statistical Analysis, Grouping and Correlation
  • Model Development
  • Model Evaluation and Refinement
  • Getting Started with Machine Learning: The famous iris dataset
  • Training a Machine Learning Model with scikit-learn
  • Cross-Validation: Partitioning Training and Test Sets
  • Data Science Pipeline: Passes, Scores, and Skill Matrix
  • Cross-Validation for Parameter Tuning, Model Selection and Feature Selection
  • Hyperparameter Tuning for Optimal Tuning Parameters
  • Evaluating a Classification Model: Confusion Matrix and ROC

  • Basic Plotting for Data Visualization
  • Data Manipulation for Visualizations
  • Types of Visualizations: Boxplots and Violin Plots
  • Project 2: A Visual Analysis of World GDP and carbon dioxide emission
  • Project 3: Using Folium Library for Geographic Overlays

  • Simple Linear Regression
  • Multiple Linear Regression
  • Ridge & Lasso Regression
  • Polynomial Regression
  • Multicollinearity and VIF
  • Elastic Net Regression and Lasso Regression
  • Testing for Nonlinear Relationships
  • Decision Trees in Scikit-learn
  • Random Forest Regression Analysis
  • Logistic Regression Analysis using Logistic Regression
  • Classification Analysis using Decision Tree Classification
  • Decision Tree using Scikit-learn
  • Random Forest Classification
  • Boosting Algorithms
  • K-Nearest Neighbors Classification

  • Project 6: Neural Network for Handwritten Digit Recognition
  • Convolutional Neural Networks
  • K-Means Clustering
  • Hierarchical Cluster with Data Clustering using K-Means Clustering
  • DBSCAN Clustering
  • Clustering Algorithms and Applications (Introduction)
  • Project 7: Analyzing Customer Segmentation with K-Means Clustering
  • Project 8: Customer Segmentation using Density-Based Clustering (DB-SCAN)
  • Applications of Neural Networks
  • Support Vector Machine: Hyperplanes & Support Vector Machines
  • Metrics for Accuracy
  • Project 9: Neural Network Analysis on Handwritten Digits Dataset
  • Model Based Analysis

  • Stemming, Phrase Identification, Word Sense Disambiguation
  • POS Tagging
  • TF-IDF
  • N-gram models of Language
  • Word2Vec Model: Doc to Vector
  • Working AI Algorithms

  • ALGORITHM DESIGN AND ANALYSIS
  • Evaluate the speed, runtime, and memory dependencies of algorithmic models
  • Parallel computing systems such as SIMD (Single Instruction Single Data Stream), MIMD (Multiple Instructions Single Data Stream)
  • SIMD (Multiple Instructions Multiple Data Streams)
  • How to use random tools
  • Control, flow analysis, and unit test cases
  • Measure and Optimize performance of algorithm
  • Deployment of the Models

  • ALGORITHM DESIGN AND ANALYSIS
  • Evaluate the speed, runtime, and memory dependencies of algorithmic models
  • Parallel computing systems such as SIMD (Single Instruction Single Data Stream), MIMD (Multiple Instructions Single Data Stream)
  • SIMD (Multiple Instructions Multiple Data Streams)
  • How to use random tools
  • Control, flow analysis, and unit test cases
  • Measure and Optimize performance of algorithm
  • Deployment of the Models

  • RDBMS Principles
  • Install a DB Engine
  • SQL Queries and Data Types
  • SQL Syntax, Expressions, Comments
  • Data Definition Language (DDL)
  • Data Manipulation Language (DML)
  • Grant and Revoke
  • SQL Live Practice Session
  • SQL Functions (Sum, Count, Avg etc.)
  • Joins (Left join, Right join, Full outer)
  • Queries and Sub Queries
  • SQL Clauses
  • SQL Window functions
  • SQL Real time examples

  • Tableau Desktop
  • Tableau Products
  • Tableau Terminology
  • Connecting with Data
  • Visualizing Data
  • Modifying Data
  • Tableau Calculations
  • Dashboards
  • Sharing the Visuals

Discover the Full Course Content

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Capstone Projects

Test your skills and mettle with a capstone project.

Comprehensive Curriculum

Master web development with a full-stack curriculum covering front-end, back-end, databases, and more.

Hands-On Projects

Apply skills to real-world projects for practical experience and enhanced learning.

Expert Instructors

Learn from industry experts for insights and guidance in full-stack development.

Job Placement Assistance

Access job placement assistance for career support and employer connections.

Certification upon Completion

Receive a recognized certification validating your full-stack development skills.

24/7 Support

Access round-the-clock support for immediate assistance, ensuring a seamless learning journey.

Flexible Learning Schedule

Enjoy the freedom to learn at your own pace with flexible schedules, allowing you to balance studies with other commitments.

Community Support

Join a community of fellow learners and professionals to share knowledge, exchange ideas, and grow together in your development journey.

Why Lift My Skills

Enroll with leading global online educational course provider.

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USERS

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TOP RANKED PROGRAMS

10

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INDUSTRY EXPERTS

500+

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EXPERT FACULTIES

1000+

Benefits

Learn from leading academicians and several experienced industry practitioners from top organizations.

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Personalised workshops based on your proficiency level to help you get on par.

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Mix of Live Classes & Recorded lectures for your convenience.

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24×7 Student Support, Quick doubt resolution by industry experts.

Alumni Highlights

200+

Global Companies

$122K PA

Average CTC

$250K PA

Highest CTC

87%

Average Salary Hike

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+1
FAQ
Find answers to all your queries and doubts here.
Q1: What is the closing date for issue of applications?

Answer: The closing date for applications varies each year. Please check the university's official website for the latest deadlines.

Q2: Can I visit the campus to know more about the program and University?

Answer: Yes, campus visits are encouraged. Please schedule a visit through the admissions office.

Q3: Can I pursue the Data Science master's degree part-time while I am working?

Answer: Yes, the program offers a part-time option for working professionals.

Q4: Is there financial aid available?

Answer: Financial aid is available for eligible students. Please contact the financial aid office for details.

Q5: When and how can I apply? Does the program have rolling admissions?

Answer: Applications are open year-round for rolling admissions. Check the official website for specific dates.

Q6: What are the skills required to start a job in the field of Data Science?

Answer: Essential skills include programming, statistical analysis, and data visualization.

Q7: Who is eligible for taking the Masters in Data Science course from Birchwood University?

Answer: Eligibility criteria include a bachelor's degree in a relevant field. Please refer to the admission guidelines for more details.

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