Course Content
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Accuracy Score
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Activation Function
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Algorithm
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Assignment Operator (Python)
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Artificial General Intelligence (AGI)
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Artificial Intelligence
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Artificial Narrow Intelligence (ANI)
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Artificial Neural Network (ANN)
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Backpropagation
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Bias
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Bias-Variance Tradeoff
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Big Data
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Business Analyst (BA)
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Business Analytics (BA)
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Business Intelligence (BI)
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Categorical Variable
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Clustering
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Command Line
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Computer Vision
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Continuous Variable
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Cost Function
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Cross-Validation
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Data Analysis
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Data Analyst
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Data Science
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Data Scientist
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Early Stopping
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Exploratory Data Analysis (EDA)
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False Negative
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False Positive
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Google Colaboratory
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Gradient Descent
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Hidden Layer
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Hyperparameter
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Image Recognition
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Imputation
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K-fold Cross Validation
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K-Means Clustering
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Linear Regression
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Logistic Regression
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Machine Learning Engineer (MLE)
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Mean
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Neural Network
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Notebook
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One-Hot Encoding
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Operand
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Operator (Python)
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Print Function (Python)
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Python
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Quantile
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Quartile
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Random Forest
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Recall
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Scalar
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Snake Case
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T-distribution
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T-test
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Tableau
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Target
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Tensor
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Tensor Processing Unit (TPU)
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TensorBoard
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TensorFlow
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Test Loss
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Time Series
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Time Series Data
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Test Set
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Tokenization
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69.
Train Test Split
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Training Loss
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Training Set
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Transfer Learning
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True Negative (TN)
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True Positive (TP)
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75.
Type I Error
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Type II Error
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77.
Underfitting
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78.
Undersampling
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79.
Univariate Analysis
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80.
Unstructured Data
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81.
Unsupervised Learning
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82.
Validation
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83.
Validation Loss
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1 min
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84.
Vanishing Gradient Problem
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2 min
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85.
Validation Set
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86.
Variable (Python)
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1 min
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87.
Variable Importances
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88.
Variance
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89.
Variational Autoencoder (VAE)
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2 min
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90.
Weight
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1 min
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91.
Word Embedding
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92.
X Variable
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2 min
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93.
Y Variable
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94.
Z-Score
0 min
1 min
0
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Business Intelligence (BI) refers to the set of strategies, tools, technologies, and processes used to collect, analyze, and present data in a format that helps businesses make informed decisions. BI primarily focuses on querying, reporting, data visualization, and data analytics to provide historical, current, and predictive views of business operations. Its main goal is to support operational decision-making and improve business processes. BI tools often work with structured and sometimes semi-structured data.
Key characteristics of Business Intelligence include:
- Providing historical and current data analysis.
- Focused on generating reports, dashboards, and visualizations for descriptive analytics.
- Typically deals with structured data from databases, data warehouses, or other structured sources.
- Emphasizes ease of use, accessibility, and user-friendly interfaces for business users to explore data.
How does Business Intelligence differ from Data Science?
While both Business Intelligence and Data Science involve working with data to derive insights, BI typically deals with providing descriptive analytics and reporting for operational decision-making, whereas Data Science delves deeper into predictive and prescriptive analytics, often requiring more advanced statistical and machine learning techniques to solve complex problems and make future predictions.
In essence, Business Intelligence is a subset of Data Science, focusing on simpler analytics for business operations, whereas Data Science encompasses a wider array of techniques and methodologies for extracting insights and creating predictive models from diverse and complex datasets.