Data science encompasses a variety of functions and techniques to extract insights and knowledge from data. Here are some important functions used in data science:
Data Cleaning and Preprocessing: Dealing with missing values, outliers, and ensuring data is in a format suitable for analysis. This involves tasks such as imputation, normalization, and encoding.
Exploratory Data Analysis (EDA): Analyzing and visualizing data to understand its characteristics, patterns, and relationships. This step often includes the use of statistical methods and graphical representations.
Feature Engineering: Creating new features from existing ones to improve model performance. This involves selecting, transforming, and combining variables.
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Model Development: Building and training predictive models using machine learning algorithms. This step includes tasks such as model selection, hyperparameter tuning, and cross-validation.
Model Evaluation: Assessing the performance of models using metrics like accuracy, precision, recall, F1 score, ROC-AUC, etc. This helps in choosing the best model for the given problem.
Data Visualization: Creating meaningful and insightful visual representations of data using charts, graphs, and dashboards to communicate findings effectively.
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Machine Learning Interpretability: Understanding and interpreting the decisions made by machine learning models, ensuring transparency and accountability.
Big Data Technologies: Working with technologies such as Hadoop, Spark, and distributed computing frameworks to handle and analyze large volumes of data.
Natural Language Processing (NLP): Analyzing and processing human language data, often used in applications like sentiment analysis, chatbots, and text summarization.
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