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Data Science and Analytics

Relevant Coursework:

  • CSCE 1030 - Computer Science I

  • CSCE 1040 - Computer Science II

  • CSCE 2100 - Foundations of Computing

  • CSCE 2110 - Foundations of Data Structures

  • CSCE 2610 - Assembly Language and Computer Organization (important for understanding low-level data handling)

  • CSCE 3110 - Data Structures and Algorithms

  • CSCE 3444 - Software Engineering

  • CSCE 3600 - Principles of Systems Programming

  • CSCE 3550 - Foundations of Cybersecurity (relevant for data privacy and protection)

  • MATH 1780 - Probability Models or MATH 3680 - Applied Statistics (essential for data science roles)

Recommended Electives:

  • Database Systems: Learn advanced SQL and data warehousing concepts.

  • Artificial Intelligence: Explore machine learning, deep learning, and AI tools.

  • Big Data Technologies: Understand frameworks like Hadoop, Spark, and cloud computing platforms.

Median Total Comp: (will be updated with resources)

  • Data Scientist: $120,000 - $130,000 annually

  • Data Analyst: $65,000 - $75,000 annually

  • Business Intelligence Analyst: $75,000 - $85,000 annually

  • Machine Learning Engineer: $110,000 - $130,000 annually

  • Big Data Engineer: $120,000 - $130,000 annually

  • Data Engineer: $100,000 - $120,000 annually

Top Tech Companies:
Google, Amazon, Facebook (Meta), Microsoft, IBM, Apple, Netflix, LinkedIn, Uber, Airbnb

Data Scientist 

Programming Languages:

  • Proficiency in programming languages commonly used in data science: Python (with libraries like NumPy, Pandas, Matplotlib, Seaborn), R.

Data Manipulation and Cleaning:

  • Ability to clean and preprocess raw data for analysis.

  • Handling missing data, outliers, and data transformations.

Data Exploration and Visualization:

  • Exploratory Data Analysis (EDA) techniques.

  • Visualization tools and libraries (e.g., Matplotlib, Seaborn, Plotly).

Statistics and Mathematics:

  • Solid understanding of statistical concepts and methods.

  • Knowledge of probability theory.

  • Hypothesis testing and statistical inference.

Machine Learning:

  • Understanding of machine learning algorithms and models.

  • Supervised learning, unsupervised learning, and reinforcement learning.

  • Implementing models using scikit-learn, TensorFlow, or PyTorch.

Feature Engineering:

  • Creating relevant features from raw data.

  • Dimensionality reduction techniques (e.g., PCA).

Data Modeling and Evaluation:

  • Model selection and evaluation metrics.

  • Cross-validation techniques.

  • Hyperparameter tuning.

Big Data Technologies:

  • Familiarity with big data tools and frameworks: Apache Hadoop, Apache Spark.

Database and SQL:

  • Working with databases and writing SQL queries.

  • Understanding relational database concepts.

Data Warehousing:

  • Knowledge of data warehousing concepts and technologies.

  • Familiarity with tools like Amazon Redshift, Google BigQuery.

Data Ethics and Privacy:

  • Awareness of ethical considerations in data science.

  • Complying with privacy regulations and best practices.

Domain Knowledge:

  • Understanding the domain or industry-specific context.

  • Collaborating with domain experts for meaningful insights.

Communication and Visualization:

  • Effective communication of data findings to non-technical stakeholders.

  • Creating dashboards and reports for visualization.

Version Control/Git:

  • Proficiency in using version control systems (e.g., Git) for collaborative work.

Cloud Platforms:

  • Familiarity with cloud platforms (e.g., AWS, Azure, Google Cloud) for scalable and distributed computing.

Natural Language Processing (NLP):

  • Understanding of NLP for text data analysis.

  • Implementing NLP techniques using libraries like NLTK or spaCy.

Time Series Analysis:

  • Analyzing and modeling time-series data.

  • Forecasting techniques.

Continuous Learning:

  • Staying updated with the latest developments in data science.

  • Engaging with the data science community, attending conferences, and participating in online forums.

Data Analyst 

Excel/Spreadsheet Skills:

  • Proficiency in spreadsheet tools, especially Microsoft Excel or Google Sheets.

  • Data manipulation, sorting, filtering, and basic formula usage.

SQL (Structured Query Language):

  • Ability to write SQL queries for data extraction and manipulation.

  • Understanding of relational databases and basic database concepts.

Data Cleaning and Preprocessing:

  • Cleaning and preprocessing raw data.

  • Handling missing data and outliers.

Data Visualization:

  • Creating visualizations using tools like Excel charts and graphs, data visualization libraries (e.g., Matplotlib, Seaborn, Tableau).

Statistical Analysis:

  • Understanding basic statistical concepts.

  • Descriptive statistics and summary metrics.

Exploratory Data Analysis (EDA):

  • Techniques for exploring and understanding datasets.

  • Generating insights from visualizations and summary statistics.

Data Analysis Tools:

  • Familiarity with statistical analysis tools like R or Python (using libraries like Pandas).

  • Basic scripting and automation for repetitive tasks.

Data Interpretation:

  • Drawing conclusions and making recommendations based on data analysis.

  • Storytelling with data to effectively communicate findings.

Critical Thinking:

  • Developing a critical mindset for evaluating data and drawing meaningful insights.

  • Identifying patterns and trends in data.

Business Acumen:

  • Understanding the business context and goals.

  • Aligning data analysis with business objectives.

Data Warehousing:

  • Familiarity with data warehousing concepts.

  • Understanding of data extraction, transformation, and loading (ETL) processes.

Version Control/Git:

  • Proficiency in using version control systems (e.g., Git) for collaborative work.

Microsoft Power BI or Tableau:

  • Basic knowledge of visualization tools for creating interactive dashboards.

Basic Programming Skills:

  • Familiarity with basic programming concepts (e.g., loops, conditional statements).

  • Scripting languages like Python or R for data manipulation.

Cloud Platforms:

  • Understanding of cloud platforms (e.g., AWS, Azure, Google Cloud) for data storage and analysis.

Communication Skills:

  • Effective communication of data findings to both technical and non-technical audiences.

  • Writing clear and concise reports.

Time Management:

  • Efficiently managing time and prioritizing tasks for timely delivery of analyses.

Continuous Learning:

  • Staying updated with the latest tools and techniques in data analysis.

  • Engaging with the data analysis community, participating in online courses, and attending relevant workshops.

Business Intelligence Analyst

Data Warehousing Concepts:

  • Understanding the principles of data warehousing.

  • Knowledge of star schema, snowflake schema, and ETL (Extract, Transform, Load) processes.

SQL (Structured Query Language):

  • Proficiency in SQL for querying and manipulating data.

  • Ability to write complex queries for data extraction.

Data Modeling:

  • Designing and implementing data models for reporting and analysis.

  • Dimensional modeling for business intelligence.

Business Intelligence Tools:

  • Familiarity with BI tools like Tableau, Microsoft Power BI, QlikView, Looker, SAP BusinessObjects.

Data Visualization:

  • Creating meaningful visualizations for data analysis and reporting.

  • Understanding best practices in data visualization.

Report Development:

  • Developing reports and dashboards to convey insights to stakeholders.

  • Automation of recurring reports.

Dashboard Design:

  • Designing interactive and user-friendly dashboards.

  • Understanding user experience (UI/UX) principles.

Data Analysis:

  • Analyzing data trends and patterns to provide actionable insights.

  • Identifying key performance indicators (KPIs) for business measurement.

Statistical Analysis:

  • Basic statistical knowledge for analyzing trends and making predictions.

  • Descriptive and inferential statistics.

Business Acumen:

  • Understanding business processes and objectives.

  • Aligning BI solutions with business goals.

Data Governance:

  • Knowledge of data governance principles and practices.

  • Ensuring data quality and integrity.

Data Security:

  • Understanding data security and privacy considerations.

  • Implementing access controls and encryption where necessary.

Database Management:

  • Understanding of databases and data storage systems.

  • Familiarity with both relational and non-relational databases.

Collaboration and Communication:

  • Collaborating with various teams, including IT, business users, and executives.

  • Communicating findings and insights effectively.

Scripting/Programming Skills:

  • Basic scripting or programming skills for data manipulation (e.g., Python, R).

Project Management:

  • Managing BI projects efficiently.

  • Meeting deadlines and delivering results.

Continuous Learning:

  • Staying updated with the latest BI tools, techniques, and trends.

  • Participating in relevant courses, conferences, and industry forums.

Data Integration:

  • Integrating data from various sources for comprehensive analysis.

  • Working with APIs and data connectors.

Time Management:

  • Efficiently managing time to meet reporting deadlines and project milestones.

Machine Learning Engineer

Programming Languages:

  • Proficiency in programming languages, particularly Python (with libraries such as NumPy, Pandas, Matplotlib, and Scikit-learn), R for statistical analysis (optional).

Mathematics and Statistics:

  • Solid understanding of mathematical concepts, including linear algebra, calculus, and probability theory.

  • Statistical concepts and methods for data analysis.

Machine Learning Algorithms:

  • In-depth knowledge of various machine learning algorithms: Supervised learning algorithms (e.g., linear regression, decision trees, support vector machines), unsupervised learning algorithms (e.g., k-means clustering, hierarchical clustering, dimensionality reduction), ensemble methods (e.g., random forests, boosting).

Deep Learning:

  • Understanding of deep learning architectures and frameworks: Neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), frameworks like TensorFlow or PyTorch.

Feature Engineering:

  • Creating relevant features from raw data for model training.

  • Handling categorical variables and encoding techniques.

Model Evaluation and Hyperparameter Tuning:

  • Selecting appropriate evaluation metrics for different types of models.

  • Hyperparameter tuning to optimize model performance.

Data Preprocessing:

  • Cleaning and preprocessing raw data.

  • Dealing with missing data and outliers.

Model Deployment:

  • Deploying machine learning models into production environments.

  • Understanding containerization (e.g., Docker) and model serving frameworks.

Version Control/Git:

  • Proficiency in using version control systems (e.g., Git) for collaborative work.

Cloud Platforms:

  • Familiarity with cloud platforms (e.g., AWS, Azure, Google Cloud) for scalable and distributed computing.

Natural Language Processing (NLP):

  • Understanding of NLP techniques for text data analysis.

  • Working with libraries like NLTK or spaCy.

Reinforcement Learning (Optional):

  • Basic knowledge of reinforcement learning concepts for dynamic decision-making systems.

Big Data Technologies:

  • Familiarity with big data tools and frameworks: Apache Hadoop, Apache Spark.

Model Interpretability and Explainability:

  • Techniques to interpret and explain model predictions.

  • Addressing bias and fairness in machine learning models.

Collaborative and Communication Skills:

  • Collaborating with cross-functional teams.

  • Effectively communicating machine learning concepts and results to non-technical stakeholders.

Continuous Learning:

  • Staying updated with the latest developments in machine learning.

  • Engaging with the machine learning community, attending conferences, and participating in online forums.

Ethics in Machine Learning:

  • Awareness of ethical considerations in machine learning.

  • Addressing bias and fairness issues in models.

Big Data Engineer

Distributed Systems Concepts:

  • Understanding of distributed computing principles.

  • Knowledge of data partitioning, replication, and fault tolerance.

Programming Languages:

  • Proficiency in programming languages commonly used in big data engineering: Java, Scala, Python.

Big Data Technologies:

  • Familiarity with major big data frameworks and technologies: Apache Hadoop (HDFS, MapReduce), Apache Spark, Apache Flink, Apache Kafka.

Data Modeling:

  • Designing and implementing data models for big data systems.

  • Schema design for distributed databases.

ETL (Extract, Transform, Load) Processes:

  • Developing ETL processes for data integration.

  • Handling large-scale data transformation and cleansing.

Data Storage Solutions:

  • Knowledge of various data storage solutions for big data: Apache HBase, Apache Cassandra, Amazon S3.

Database Management:

  • Understanding of NoSQL databases and their use cases.

  • Proficiency in SQL for querying and managing data.

Cloud Platforms:

  • Familiarity with cloud platforms (e.g., AWS, Azure, Google Cloud) for big data processing and storage.

Data Ingestion:

  • Techniques for efficiently ingesting data into big data systems.

  • Integration with external data sources.

Stream Processing:

  • Understanding of stream processing concepts.

  • Familiarity with Apache Kafka Streams or Apache Flink for real-time data processing.

Data Security:

  • Awareness of data security considerations in big data systems.

  • Implementing access controls and encryption.

Workflow Orchestration:

  • Using tools like Apache Airflow for orchestrating data workflows.

  • Managing dependencies and scheduling tasks.

Data Quality and Governance:

  • Ensuring data quality in big data systems.

  • Implementing data governance practices.

Containerization and Orchestration:

  • Understanding containerization (e.g., Docker) for packaging applications.

  • Orchestration tools like Kubernetes for managing containerized applications.

Version Control/Git:

  • Proficiency in using version control systems (e.g., Git) for collaborative work.

Data Compression and Serialization:

  • Techniques for data compression and serialization in big data systems.

  • Optimizing data storage and transmission.

Monitoring and Logging:

  • Implementing monitoring solutions for big data clusters.

  • Logging and debugging in distributed systems.

Continuous Learning:

  • Staying updated with the latest big data technologies and best practices.

  • Engaging with the big data engineering community, attending conferences, and participating in online forums.

Data Engineer 

Relational Database Management Systems (RDBMS):

  • Proficiency in working with relational databases.

  • Understanding of SQL for querying and managing data.

NoSQL Databases:

  • Familiarity with various NoSQL databases like MongoDB, Cassandra, or Couchbase.

  • Knowledge of when to use NoSQL databases based on data requirements.

Data Modeling:

  • Designing and implementing data models for databases.

  • Understanding of normalization and denormalization.

ETL (Extract, Transform, Load) Processes:

  • Developing ETL processes for moving and transforming data.

  • Implementing data integration solutions.

Big Data Technologies:

  • Familiarity with big data frameworks and technologies: Apache Hadoop (HDFS, MapReduce), Apache Spark, Apache Kafka, HBase.

Programming Languages:

  • Proficiency in programming languages commonly used in data engineering: Python, Java, Scala.

Cloud Platforms:

  • Understanding of cloud platforms (e.g., AWS, Azure, Google Cloud) for data storage and processing.

Data Warehousing:

  • Knowledge of data warehousing concepts and technologies.

  • Familiarity with tools like Amazon Redshift, Google BigQuery.

Schema Design:

  • Designing database schemas for optimal performance.

  • Handling schema evolution in data pipelines.

Stream Processing:

  • Understanding of stream processing concepts.

  • Familiarity with Apache Kafka Streams or Apache Flink for real-time data processing.

Data Ingestion:

  • Techniques for efficiently ingesting data into data systems.

  • Integration with external data sources.

Data Quality and Governance:

  • Ensuring data quality in data pipelines.

  • Implementing data governance practices.

Workflow Orchestration:

  • Using tools like Apache Airflow for orchestrating data workflows.

  • Managing dependencies and scheduling tasks.

Version Control/Git:

  • Proficiency in using version control systems (e.g., Git) for collaborative work.

Containerization and Orchestration:

  • Understanding containerization (e.g., Docker) for packaging applications.

  • Orchestration tools like Kubernetes for managing containerized applications.

Data Compression and Serialization:

  • Techniques for data compression and serialization in data systems.

  • Optimizing data storage and transmission.

Monitoring and Logging:

  • Implementing monitoring solutions for data pipelines.

  • Logging and debugging in distributed systems.

Continuous Learning:

  • Staying updated with the latest data technologies and best practices.

  • Engaging with the data engineering community, attending conferences, and participating in online forums.

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