✅ *Data Science Fundamentals You Should Know* 📊📚

 

✅ *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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