1. Knowledge Varieties and Constructions:
• Categorical: Nominal (unordered, e.g., colours) and Ordinal (ordered, e.g., schooling ranges)
• Numerical: Discrete (countable, e.g., variety of kids) and Steady (measurable, e.g., top)
• Knowledge Constructions: Arrays, Lists, Dictionaries, DataFrames (for organizing and manipulating information)
2. Descriptive Statistics:
• Measures of Central Tendency: Imply, Median, Mode (describing the everyday worth)
• Measures of Dispersion: Variance, Customary Deviation, Vary (describing the unfold of information)
• Visualizations: Histograms, Boxplots, Scatterplots (for understanding information distribution)
3. Likelihood and Statistics:
• Likelihood Distributions: Regular, Binomial, Poisson (modeling information patterns)
• Speculation Testing: Formulating and testing claims about information (e.g., A/B testing)
• Confidence Intervals: Estimating the vary of believable values for a inhabitants parameter
4. Machine Studying:
• Supervised Studying: Regression (predicting steady values) and Classification (predicting classes)
• Unsupervised Studying: Clustering (grouping related information factors) and Dimensionality Discount (simplifying information)
• Mannequin Analysis: Accuracy, Precision, Recall, F1-score (assessing mannequin efficiency)
5. Knowledge Cleansing and Preprocessing:
• Lacking Worth Dealing with: Imputation, Deletion (coping with incomplete information)
• Outlier Detection and Removing: Figuring out and addressing excessive values
• Characteristic Engineering: Creating new options from present ones (e.g., combining variables)
6. Knowledge Visualization:
• Forms of Charts: Bar charts, Line charts, Pie charts, Heatmaps (for speaking insights visually)
• Ideas of Efficient Visualization: Readability, Accuracy, Aesthetics (for conveying data successfully)
7. Moral Issues in Knowledge Science:
• Knowledge Privateness and Safety: Defending delicate data
• Bias and Equity: Making certain algorithms are unbiased and truthful
8. Programming Languages and Instruments:
• Python: Common for information science with libraries like NumPy, Pandas, Scikit-learn
• R: Statistical programming language with sturdy visualization capabilities
• SQL: For querying and manipulating information in databases
9. Massive Knowledge and Cloud Computing:
• Hadoop and Spark: Frameworks for processing large datasets
• Cloud Platforms: AWS, Azure, Google Cloud (for storing and analyzing information)
10. Area Experience:
• Understanding the Knowledge: Figuring out the context and which means of information is essential for efficient evaluation
• Drawback Framing: Defining the correct questions and targets for data-driven resolution making
Bonus:
• Knowledge Storytelling: Speaking insights and findings in a transparent and fascinating method
Greatest Knowledge Science & Machine Studying Assets: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍