1. Beginner
Skills:
- Basic understanding of statistics and data analysis.
- Familiarity with spreadsheets or basic data manipulation tools (e.g., Excel).
- Ability to use simple data visualization tools (e.g., Excel, Google Sheets, or Python’s
matplotlib and seaborn). - Introductory knowledge of programming (Python or R) and basic libraries (e.g.,
Pandas, NumPy). - Basic knowledge of data types (structured, semi-structured, and unstructured data).
Example Tasks:
- Plotting simple graphs (bar charts, line graphs) to visualize data.
- Calculating mean, median, mode, variance, and other basic statistical metrics.
- Loading and cleaning small datasets.
Skills:
- Proficient in data wrangling: loading, cleaning, and transforming data using libraries like
Pandas, NumPy, or R’s dplyr. - Good understanding of probability, statistical testing (e.g., hypothesis testing, confidence intervals), and distributions.
- Basic knowledge of machine learning algorithms (e.g., linear regression, decision trees) and their applications.
- Experience with data visualization libraries (e.g.,
matplotlib, seaborn, or ggplot2). - Ability to perform exploratory data analysis (EDA) and extract insights from datasets.
- Familiarity with supervised and unsupervised learning concepts.
Example Tasks:
- Cleaning and transforming large datasets using
Pandas or NumPy. - Creating visualizations for data distributions and relationships (scatter plots, histograms).
- Implementing and evaluating simple machine learning models like linear regression or K-means clustering.
- Performing A/B testing or statistical analysis on datasets.
3. Advanced
Skills:
- Proficient in implementing complex machine learning algorithms (e.g., random forests, gradient boosting, neural networks) using libraries like
scikit-learn, TensorFlow, or PyTorch. - Strong understanding of feature engineering, hyperparameter tuning, model evaluation, and optimization techniques.
- Experience working with large-scale datasets and using cloud platforms for data storage and computation (e.g., AWS, GCP, Azure).
- Familiarity with big data tools and frameworks (e.g., Hadoop, Spark).
- Ability to work with databases (SQL) and unstructured data (e.g., text data with NLP).
- Knowledge of deep learning and more advanced topics like natural language processing (NLP), reinforcement learning, or computer vision.
Example Tasks:
- Building and fine-tuning machine learning models for production.
- Performing sentiment analysis on text data using NLP techniques.
- Creating predictive models using time series analysis or deep learning methods.
- Implementing machine learning pipelines for automated model training and deployment.
4. Expert
Skills:
- Mastery of complex algorithms and advanced techniques, such as deep learning architectures (e.g., CNNs, RNNs, Transformers) or reinforcement learning.
- Deep understanding of data science workflows, MLOps (machine learning operations), and the deployment of machine learning models in production environments.
- Expertise in using cloud platforms, distributed computing, and handling real-time data streams.
- Strong ability to create custom machine learning models, handle imbalanced data, and apply transfer learning.
- Leadership experience in designing large-scale data science projects, mentoring teams, and making data-driven business decisions.
Example Tasks:
- Designing and implementing custom deep learning architectures for complex problems (e.g., image recognition, natural language understanding).
- Leading a team of data scientists in building scalable and efficient data pipelines.
- Managing and deploying machine learning models at scale for real-time or high-impact applications.
- Developing and deploying end-to-end AI systems and integrating them with business operations.
