Data Types
Structured Data: Data that is organized in a tabular format, like rows and columns in a spreadsheet or database (e.g., sales records, sensor data).
Unstructured Data: Data without a predefined structure, such as text, images, and videos (e.g., social media posts, emails, audio recordings).
Semi-structured Data: A mix of structured and unstructured data, often in formats like JSON or XML (e.g., web pages, log files).
2. Statistics
Descriptive Statistics: Summarize and describe data, including measures like mean, median, mode, variance, and standard deviation.
Inferential Statistics: Making predictions or inferences about a population based on a sample, using techniques like hypothesis testing, confidence intervals, and p-values.
Probability: The foundation of data science, focusing on the likelihood of events, probability distributions (e.g., normal distribution), and conditional probability.
3. Data Cleaning and Preprocessing
Missing Data: Handling missing values by imputation, removal, or using special techniques based on the problem.
Outliers: Identifying and addressing extreme data points that can skew analysis.
Normalization/Standardization: Rescaling features to ensure consistency (e.g., Min-Max Scaling, Z-score Normalization).
Encoding Categorical Data: Converting categorical variables into numerical formats (e.g., one-hot encoding, label encoding).
Data Splitting: Dividing data into training, validation, and test sets to evaluate models effectively.
4. Exploratory Data Analysis (EDA)
Visualization: Graphically representing data to find patterns, trends, and anomalies. Common tools include histograms, scatter plots, box plots, and heatmaps.
Correlation: Understanding relationships between variables using metrics like Pearson or Spearman correlation coefficients.
Dimensionality Reduction: Techniques like Principal Component Analysis (PCA) to reduce the number of features while retaining key information.
5. Machine Learning
Supervised Learning: Training a model on labeled data to predict an outcome (e.g., regression, classification).
Unsupervised Learning: Finding hidden patterns in unlabeled data (e.g., clustering, dimensionality reduction).
Reinforcement Learning: A type of learning where agents take actions in an environment and learn from feedback (rewards or penalties).
Model Evaluation: Assessing model performance using metrics like accuracy, precision, recall, F1 score, mean squared error (MSE), or area under the curve (AUC).
6. Programming Skills
Python and R: Popular programming languages in data science. Python is known for its versatility, while R is strong in statistical analysis.
Libraries and Frameworks:
NumPy and Pandas: For data manipulation and analysis.
Matplotlib and Seaborn: For data visualization.
Scikit-learn: For machine learning algorithms and model evaluation.
TensorFlow and PyTorch: For deep learning.

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