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Headline: Accelerate Your Career in Full Stack Data Science: 6-Month Intensive Program

Introduction

Transform your career with our comprehensive 6-month Full Stack Data Science course. Master the in-demand skills that drive innovation across industries and unlock exciting new opportunities.

Why Choose This Course?

• Streamlined Learning: Acquire crucial data science skills efficiently within a condensed timeframe.
• Emphasis on Practicality: Hone real-world competencies through hands-on projects and applications.
• Mentorship by Industry Experts: Receive guidance from seasoned professionals in the field of data science.
• Comprehensive Career Assistance: Access the resources and support necessary to secure your desired position in the data science industry.

Full Stack Data Scientist Curriculum:

• Foundations in Excel

• • Introduction to Excel Environment
  • Excel Formulas and Functions
  • Data Wrangling with Real-world Datasets
  • Excel Pivot Tables and Visualization
  • Excel Dashboarding and VBA Macros
• Data Visualization using Tableau  

• • Fundamentals of Data Visualization
  • Introduction to Tableau and Data Connection
  • Data Management and Enhancement in Tableau
  • Interactive Graphing and Charts
  • Storytelling and Dashboarding in Tableau
• Introduction to SQL   

• • Database Fundamentals and SQL Basics
  • Filtering and Displaying Data
  • Functions and Joins
  • Grouping and Aggregating Data
  • Subqueries and Nested Queries
• Statistics, Probability, and Linear Algebra
  
  • Statistical Fundamentals and Hypothesis Testing
  • Probability Theory and Distributions
  • Linear Algebra Concepts
  • Analysis of Variance and Covariance
• Python Basics
  
  • Introduction to Data Science Tools
  • Python Fundamentals and Data Structures
  • Python Conditionals, Loops, and Operators
  • Functions, Classes, and Basic Problem-solving
• Advanced Python for Data Scientists

• • Numpy and Pandas for Data Engineering
  • Optimizing DataFrames and Memory Footprint
  • Processing Data in Chunks and Augmenting with SQLite
• Machine Learning   
  • Introduction to Machine Learning and its Types
  • Supervised Learning Algorithms
  • Unsupervised Learning Algorithms
  • Ensemble Methods and Proximity-based Models
• Deep Learning with TensorFlow
  • Introduction to Deep Learning and Neural Networks
  • Gradient Descent and Backpropagation
  • Introduction to TensorFlow and Keras
  • Building Artificial Neural Networks
• Natural Language Processing (NLP)

• • Introduction to NLP and Basic Techniques
  • NLP with Deep Learning: RNNs, LSTMs, Word2vec
  • Advanced NLP: Transformer Architecture, Transfer Learning, BERT
• Model Deployment on Cloud

• • Introduction to Heroku and Streamlit
  • Deploying Models on Heroku for Sentiment Analysis
• Big Data with Hadoop & Spark

• • Fundamentals of Big Data and Hadoop
  • Apache Spark for Distributed Processing
  • Big Data Processing with PySpark

Who Should Apply

• Professionals aiming for a transition into the field of data science
• Recent graduates possessing a technical background
• Individuals enthusiastic about leveraging data and AI for problem-solving
• Working professionals seeking to enhance their skill set in data science

Outcomes

By the end of this course, you will be able to:

• Confidently tackle data analysis and visualization tasks
• Build and evaluate various machine learning models
• Understand the principles of deep learning and its applications
• Design and implement impactful AI-powered solutions

What Sets Us Apart

• Industry-Aligned Curriculum: Curriculum designed with feedback from leading tech companies.
• Experienced Instructors: Learn from practitioners actively working in AI and data science
• Flexible Learning Options: Choose in-person, online, or blended formats to fit your schedule.

Enrol Now

Invest in your future! Limited seats available. To learn more and apply, visit [website link].

Let`s Make it Even Better - Additional Considerations

• Testimonials: Feature success stories of former students.
• Pricing and Scholarships: Provide transparent information about costs and eligibility for financial aid.
• Showcase projects: Highlight examples of student projects on your website to demonstrate the course`s value.

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