There are several colleges in Seattle, WA where you could earn a degree in Data Science and get all the technical skills required to be a Data Scientist. Colleges like City University of Seattle and Seattle University are known for their Master’s degree program in Data Science.
The top skills that are needed to become a data scientist include the following:
- Hadoop Platform
- Statistics/ Mathematics
- Machine Learning and Artificial Intelligence
- Data Cleaning
- Apache Spark
- Data Visualization
- Unstructured data
1. Programming/Software: Programming languages and software packages are top skills necessary to be possessed by the data scientists to extract, clean, analyze, and visualize data efficiently. The main programming languages that an aspiring data scientist should be familiar with are as follows:
- R: R is data analysis software and can be used for statistical analysis, data visualization, and predictive modeling. It is an object-oriented programming language used to explore, model, and visualize data.
- Python: Analyzing data with Python is easier since a number of tools have been built specifically for data science to efficiently work with Python. Packages tailored to their needs are freely available for download.
- SQL: SQL or Structured Query Language is a special-purpose programming language used for data insertion, queries, updating and deleting, schema creation and modification, and data access control of data held in relational database management systems.
2. Hadoop Platform: Hadoop is an open-source software framework and is heavily preferred in several data science projects for processing of large data sets. It can store unstructured data such as text, images, and video. Hadoop is equipped with features like flexibility, scalability, fault tolerance, and low cost which makes it a preferable choice for data scientists.
3. Statistics/ Mathematics: A concrete understanding of multivariable calculus and linear algebra is essential for a data scientist since it forms the basis of many data analysis techniques. Math is considered to be the second language for data scientists since it simplifies writing algorithms. Data interpretation requires a deep understanding of correlations, distribution, maximum likelihood estimators and so much more.
4. Machine Learning and Artificial Intelligence: Machine Learning requires a better understanding of neural networks, reinforcement learning, adversarial learning, etc. It can be considered as a subset of Artificial Intelligence but focuses on making predictions from data available from past experiences. Machine Learning connects Artificial Intelligence with Data Science. Artificial Intelligence focuses on understanding core human abilities such as speech, vision, decision making, language, and other complex tasks, and designing machines and software to emulate these processes through techniques like Computer vision, language processing, and machine learning.
5. Data Cleaning: It is important that the data is correct and accurate before data scientists analyze it. Therefore, a considerable amount of time and effort is spent to ensure this. Data cleaning also termed as data cleansing is identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing it with the correct data. Tools like Trifacta, OpenRefine, Paxata, Alteryx, Data Ladder, WinPure are used for data cleaning. Therefore, data quality should possess the features of accuracy, validity, completeness, uniformity, and consistency.
6. Apache Spark: Apache Spark is a fast and general-purpose cluster computing system designed to cover a wide range of workloads such as interactive queries, batch applications, streaming and, iterative algorithms. The top highlighted features of Apache Spark are as follows:
- Advanced Analytics
- Supports multiple languages
The important feature of Spark is its in-memory cluster computing that increases the processing speed hence provides fast computation.
7. Data visualization: Data visualization tools provide a better and accessible way to see and understand trends, outliers, and patterns in data by using visual elements like maps, graphs, and charts. A good and effective data visualization tool make large data sets coherent. The main focus of data visualization is on information presentation which is achieved through the following:
- Heat map
- Gantt chart
- Bar graph
- Scatter plot
8. Unstructured Data: Unstructured data can be defined as data that cannot fit neatly into a database and does not follow the conventional data model like Word documents, email messages, PowerPoint presentations, survey responses, transcripts of call center interactions, and posts from blogs and social media sites. Working with unstructured data provides a better insight into analyzing data.