Data science is a multidisciplinary field that covers a wide range of topics. To become proficient in data science, you should have a solid understanding of the following key areas:
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Statistics:
- Probability theory
- Descriptive statistics
- Inferential statistics
- Hypothesis testing
- Regression analysis
- Bayesian statistics
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Mathematics:
- Linear algebra
- Calculus
- Multivariate calculus (for deep learning)
- Differential equations (for time series analysis)
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Programming and Data Manipulation:
- Python or R programming languages
- Data manipulation libraries like Pandas (Python) or dplyr (R)
- Data visualization libraries like Matplotlib, Seaborn (Python), or ggplot2 (R)
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Machine Learning:
- Supervised learning (e.g., linear regression, decision trees, support vector machines)
- Unsupervised learning (e.g., clustering, dimensionality reduction)
- Deep learning (e.g., neural networks, convolutional neural networks, recurrent neural networks)
- Model evaluation and selection techniques
- Feature engineering
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Data Preprocessing:
- Data cleaning
- Missing data imputation
- Outlier detection and treatment
- Data scaling and normalization DATA SCIENCE COURSE IN PUNE
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Big Data Technologies:
- Hadoop
- Apache Spark
- Distributed computing concepts