Course Content
1.
Accuracy Score
0 min
2 min
0
2.
Activation Function
0 min
2 min
0
3.
Algorithm
0 min
2 min
0
4.
Assignment Operator (Python)
0 min
2 min
0
5.
Artificial General Intelligence (AGI)
0 min
3 min
0
6.
Artificial Intelligence
0 min
4 min
0
7.
Artificial Narrow Intelligence (ANI)
0 min
3 min
0
8.
Artificial Neural Network (ANN)
0 min
2 min
0
9.
Backpropagation
0 min
2 min
0
10.
Bias
0 min
2 min
0
11.
Bias-Variance Tradeoff
0 min
2 min
0
12.
Big Data
0 min
2 min
0
13.
Business Analyst (BA)
0 min
2 min
0
14.
Business Analytics (BA)
0 min
2 min
0
15.
Business Intelligence (BI)
0 min
1 min
0
16.
Categorical Variable
0 min
1 min
0
17.
Clustering
0 min
2 min
0
18.
Command Line
0 min
1 min
0
19.
Computer Vision
0 min
2 min
0
20.
Continuous Variable
0 min
1 min
0
21.
Cost Function
0 min
2 min
0
22.
Cross-Validation
0 min
2 min
0
23.
Data Analysis
0 min
7 min
0
24.
Data Analyst
0 min
4 min
0
25.
Data Science
0 min
1 min
0
26.
Data Scientist
0 min
6 min
0
27.
Early Stopping
0 min
2 min
0
28.
Exploratory Data Analysis (EDA)
0 min
2 min
0
29.
False Negative
0 min
1 min
0
30.
False Positive
0 min
1 min
0
31.
Google Colaboratory
0 min
2 min
0
32.
Gradient Descent
0 min
2 min
0
33.
Hidden Layer
0 min
2 min
0
34.
Hyperparameter
0 min
2 min
0
35.
Image Recognition
0 min
2 min
0
36.
Imputation
0 min
2 min
0
37.
K-fold Cross Validation
0 min
2 min
0
38.
K-Means Clustering
0 min
2 min
0
39.
Linear Regression
0 min
2 min
0
40.
Logistic Regression
0 min
1 min
0
41.
Machine Learning Engineer (MLE)
0 min
5 min
0
42.
Mean
0 min
2 min
0
43.
Neural Network
0 min
2 min
0
44.
Notebook
0 min
3 min
0
45.
One-Hot Encoding
0 min
2 min
0
46.
Operand
0 min
1 min
0
47.
Operator (Python)
0 min
1 min
0
48.
Print Function (Python)
0 min
1 min
0
49.
Python
0 min
5 min
0
50.
Quantile
0 min
1 min
0
51.
Quartile
0 min
1 min
0
52.
Random Forest
0 min
2 min
0
53.
Recall
0 min
2 min
0
54.
Scalar
0 min
2 min
0
55.
Snake Case
0 min
1 min
0
56.
T-distribution
0 min
2 min
0
57.
T-test
0 min
2 min
0
58.
Tableau
0 min
2 min
0
59.
Target
0 min
1 min
0
60.
Tensor
0 min
2 min
0
61.
Tensor Processing Unit (TPU)
0 min
2 min
0
62.
TensorBoard
0 min
2 min
0
63.
TensorFlow
0 min
2 min
0
64.
Test Loss
0 min
2 min
0
65.
Time Series
0 min
2 min
0
66.
Time Series Data
0 min
2 min
0
67.
Test Set
0 min
2 min
0
68.
Tokenization
0 min
2 min
0
69.
Train Test Split
0 min
2 min
0
70.
Training Loss
0 min
2 min
0
71.
Training Set
0 min
2 min
0
72.
Transfer Learning
0 min
2 min
0
73.
True Negative (TN)
0 min
1 min
0
74.
True Positive (TP)
0 min
1 min
0
75.
Type I Error
0 min
2 min
0
76.
Type II Error
0 min
2 min
0
77.
Underfitting
0 min
2 min
0
78.
Undersampling
0 min
2 min
0
79.
Univariate Analysis
0 min
2 min
0
80.
Unstructured Data
0 min
2 min
0
81.
Unsupervised Learning
0 min
2 min
0
82.
Validation
0 min
2 min
0
83.
Validation Loss
0 min
1 min
0
84.
Vanishing Gradient Problem
0 min
2 min
0
85.
Validation Set
0 min
2 min
0
86.
Variable (Python)
0 min
1 min
0
87.
Variable Importances
0 min
2 min
0
88.
Variance
0 min
2 min
0
89.
Variational Autoencoder (VAE)
0 min
2 min
0
90.
Weight
0 min
1 min
0
91.
Word Embedding
0 min
2 min
0
92.
X Variable
0 min
2 min
0
93.
Y Variable
0 min
2 min
0
94.
Z-Score
0 min
1 min
0
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What is Data Science?
Data Science is an interdisciplinary field that involves the use of statistical, computational, and machine learning techniques to extract insights and knowledge from data. It combines elements from various fields such as statistics, mathematics, computer science, and domain expertise.
In essence, data science involves the process of collecting, cleaning, processing, analyzing, and visualizing large amounts of data to uncover patterns, relationships, and insights that can inform decision-making and drive innovation. The ultimate goal of data science is to use data to solve complex problems and drive better business decisions.
Some of the key tools and techniques used in data science include statistical analysis, machine learning, data visualization, data mining, and predictive modeling. These techniques can be applied to a wide range of domains, including finance, healthcare, marketing, social media, and more.