Data Science vs. Data Analysis vs. Data Engineering
Explore the distinctions between data science, data analysis, and data engineering by learning their specific roles, tools, and required skills. Understand how each contributes to managing and deriving insights from data, enabling more informed decision-making and collaboration across teams.
We'll cover the following...
Data science
Data science is about uncovering insights from data that can impact future decisions. It involves the predictive analytics using machine learning and related fields.
Required skills
- Python language, R Programming, and SQL
- Machine learning
- Deep learning
- Natural language processing
- Computer vision
- Information retrieval
- Python libraries for machine learning like scikit-learn, TensorFlow, keras, and PyTorch.
- NoSQL Databases and ElasticSearch
Data analysis
Data analysis deals with extracting the information out of data by cleaning, and transforming it. It overlaps with data science in areas such as presenting insights and creating visualizations, but it typically does not involve machine learning.
Required skills
- Any open source tool which is provided with complete documentation
- SAS
- RapidMiner
- Microsoft Excel
- Python
- Tableau
- Power BI
- OpenRefine
Data engineering
Data engineering is about assisting data scientists and data analysts in their day-to-day jobs. It involves building data pipelines, designing data architectures, storing data efficiently, and making large volumes of data accessible to data scientists and data analysts in an optimized way. It can also involve using tools to uncover hidden insights within the data.
Required skills
- Python, Scala, or Java Language
- SQL
- Sqoop
- Flume
- Hadoop
- Spark
- Hive and Drill
- Kafka and Storm
- Mahout and Spark MLlib
- Pig
- HBase
- Solr and Lucene
- KNIME, Splunk, and Neo4j
- Docker and Kubernetes
- Flask (for writing APIs)