✅ Data Science Fundamentals You Should Know 📊📚

1️⃣ Statistics & Probability

– Descriptive Statistics:
Understand measures like mean (average), median, mode, variance, and standard deviation to summarize data.

– Probability:
Learn about probability rules, conditional probability, Bayes’ theorem, and distributions (normal, binomial, Poisson).

– Inferential Statistics:
Making predictions or inferences about a population from sample data using hypothesis testing, confidence intervals, and p-values.

2️⃣ Mathematics

– Linear Algebra:
Vectors, matrices, matrix multiplication — key for understanding data representation and algorithms like PCA (Principal Component Analysis).

– Calculus:
Concepts like derivatives and gradients help understand optimization in machine learning models, especially in training neural networks.

– Discrete Math & Logic:
Useful for algorithms, reasoning, and problem-solving in data science.

3️⃣ Programming

– Python / R:
Learn syntax, data types, loops, conditionals, functions, and libraries like Pandas, NumPy (Python) or dplyr, ggplot2 (R) for data manipulation and visualization.

– Data Structures:
Understand lists, arrays, dictionaries, sets for efficient data handling.

– Version Control:
Basics of Git to track code changes and collaborate.

4️⃣ Data Handling & Wrangling

– Data Cleaning:
Handling missing values, duplicates, inconsistent data, and outliers to prepare clean datasets.

– Data Transformation:
Normalization, scaling, encoding categorical variables for better model performance.

– Exploratory Data Analysis (EDA):
Using summary statistics and visualization (histograms, boxplots, scatterplots) to understand data patterns and relationships.

5️⃣ Data Visualization

– Tools like Matplotlib, Seaborn (Python) or ggplot2 (R) help in creating insightful charts and graphs to communicate findings clearly.

6️⃣ Basic Machine Learning

– Supervised Learning:
Algorithms like Linear Regression, Logistic Regression, Decision Trees where models learn from labeled data.

– Unsupervised Learning:
Techniques like K-means clustering, PCA for pattern detection without labels.

– Model Evaluation:
Metrics such as accuracy, precision, recall, F1-score, ROC-AUC to measure model performance.

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