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AI Data Analyst

An AI Data Analyst is a tech professional responsible for collecting, processing, and tracking down the statistical information from the existing datasets. The AI Data Analyst career path is in demand due to its bright future outlook. Once you are on the AI Data Analyst learning path, you can explore careers in AI & ML, Data Science, and related fields. If you're unsure of how to become a data analyst, check out our top-notch degree and certificate programs, which will advance your career.

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More About Ai Data Analyst Path

As an AI Data Analyst, you'll use AI tools to analyze large datasets, aiding decision-making and optimizing operations. This role is a stepping stone in the AI field, with opportunities for advancement. Explore our degree and certificate programs to advance your career as an AI Data Analyst.

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Skills Covered in this Path

  • Components of Data Science
  • Data Science Architecture
  • Skills needed to learn Python
  • NumPy and Pandas libraries
  • Python Basics
  • NumPy
  • Pandas
  • Matplotlib
  • Seaborn
  • Plotly
  • Basics of Probability
  • Marginal Probability
  • Bayes Theorem
  • Probability
  • Statistics
  • Normal Distribution
  • Sampling Distribution
  • Hypothesis
  • Central Limit Theorem
  • Tableau
  • Data Visualization
  • Data Science
  • Data Visualization
  • Power BI
  • Components of Power BI
  • Visual Analytics
  • Practical visualization walkthrough
  • IPL data analysis with Python
  • Visualising
  • Regression Analysis
  • Python
  • Statistics
  • Reinforcement learning
  • Machine learning
  • ANN
  • Tensorflow
  • Keras
  • Gradient
  • Backpropagation
  • Reinforcement Learning
  • States
  • Actions
  • State based mechanism in Reinforcement Learning
  • Perceptron
  • ANN
  • Gradient
  • Backpropagation
  • Activation Functions
  • Softmax Function
  • Data Augmentation
  • Weight Initialization
  • Regularization
  • Image processing using Neural Networks
  • Image Classification
  • Case study problems
  • Object Detection Using OpenCV and Python Converting Images to Different Forms"
  • Image processing
  • OpenCV with Python
  • TensorFlow
  • MNIST Dataset
  • Covid-19 detection
  • Tokenization
  • Stemming
  • Lemmatization
  • Removing Stopwords
  • Modeling Techniques in NLP
  • Machine Learning and Logistic Regression
  • Sentiment Analysis
  • Functionalities of Textblob
  • Textblob Sentiment Analysis
  • U-Net
  • Semantic Segmentation
  • NLP Use-cases
  • NLP Project
  • Forecasting using Python
  • Exponential Smoothing
  • ARIMA
  • Time Series in R
  • Feature Selection
  • Linear Discriminant Analysis with Python
  • Flask
  • Model Deployment
  • Model Deployment
  • Heroku
  • Version Control
  • Containerising
  • Continuous Integration
  • Docker
  • Docker Best practices
  • Optimizing Docker Files
  • Docker
  • Docker Storage
  • Docker Network
  • Docker Compose
  • Docker
  • grafana
  • prometheus
  • Docker Monitoring
  • Docker
  • Docker swarm
  • Orchestration
  • AWS ECR
  • AWS ECS
  • Docker
  • grafana
  • prometheus
  • Docker Monitoring
  • Spring boot
  • Deployment
  • GitHub
  • Committing
  • Merging
  • Branches
  • Creating Pull Requests
  • R Commands
  • R Packages
  • R Functions
  • R Datatypes
  • Operators in R
  • RStudio
  • Time series analysis
  • Model forecast theory
  • Time Series Forecasting
  • Time Series Demo
  • OpenCV
  • Face Detection
  • Face Recognition
  • Deep Learning
  • OpenCV Operations
  • Face Detection demo
  • Face Detection
  • Face Recognition
  • Applications of Face Recognition
  • Face Detection using OpenCV using Python
  • Healthcare analytics

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