Data science has been in the news for a while. It has applications in almost all industries, including finance. Data science helps companies make better decisions and improve products and services.
What is Data Science?
Data science is becoming an exciting and sought-after career for skilled professionals. Today’s successful data scientists know they must go beyond analyzing large amounts of data, processing data, and programming. Data scientists must understand the entire data science process and be able to maximize their returns at each stage to gain valuable insights for their companies.
Table of Contents
Data Science Lifecycle
The image shows the five life cycle phases:
- Data capture (data entry, signal reception, data extraction)
- Maintain (data storage, cleansing, staging, processing, architecture)
- Process: data mining, clustering, modeling, summarization
- Analyze (exploratory/confirmatory, predictive, regression, text mining, qualitative)
- Connect (business intelligence, decision-making, data reporting).
- “Data Scientist” was invented in 2008 when businesses realized they needed data scientists who could organize and analyze large amounts of data. 1 In a 2009 McKinsey&Company article, Hal Varian, Google’s chief economist and UC Berkeley professor of information sciences economics, business, and management, advised industries to adapt to technology and reorganize.
IBM and Facebook announced Watson, their artificial intelligence initiative, on July 1. IBM’s first large-scale venture to make AI more accessible is significant.”
Hal Varian, Google chief economist and UC Berkeley information sciences business, economics, and business professor
Effective data scientists identify relevant questions, gather information from many diverse sources, arrange the data, translate results to solutions, and communicate the results in a way that positively influences company decisions. They are needed in all fields, making skilled data scientists more important to businesses.
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Data scientists—what are they? Do?
Over the past decade, data scientists have become essential to almost every company. These experts are well-rounded and data-driven, with advanced technical skills in mathematical algorithm creation and synthesis of large amounts of data to answer questions and determine organizational strategies. This is in addition to communication and leadership skills needed to deliver tangible results to business or organization stakeholders.
Data scientists should be impressive, results-oriented, industry-savvy, and able to communicate technical concepts to non-technical colleagues. They have a quantitative background in linear algebra and statistics and programming skills in mining, data warehousing, and modeling to build and evaluate algorithms.
They must also master the most crucial technical tools and methods, such as:
- Apache Hadoop
- Apache Spark
- The NoSQL databases
- Cloud computing
- Apache Pig
- IPython notebooks
Why become a Data Scientist?
Data scientist was one of the most sought-after jobs in America in 2016, according to Glassdoor. 4 Big technology companies aren’t the only ones hiring data scientists as data volumes grow. All industries, large and small, need data science experts, but there are few qualified candidates. 5
In the coming years, data scientist demand will remain high. LinkedIn listed data scientists as top jobs for 2024. It listed the job with the most sought-after data science skills by businesses.
The numbers below show the high demand for data scientists.
Do you consider yourself a data scientist? Data Science?
Covered and vast. Mining, cleaning, analysis, interpreting, and cleaning data are often used interchangeably, but they may require different skills and data complexity.
Data scientists determine what questions need answers and where to find relevant data. They can extract, clean, and display data, demonstrating business and analytical skills. Companies hire data scientists to collect, manage, and analyze massive amounts of unstructured data. The results are synthesized and shared with key stakeholders to inform company strategy.
Programming (SAS, R, Python), math, and statistics required. Hadoop, SQL, machine learning, storytelling, data visualization
Data analysts connect business analysts and data scientists. They receive needs from an organization and organize and analyze data to produce results that match top-level business strategies. Data analysts must turn technical analysis into qualitative actions and effectively communicate their findings to stakeholders.
Skills needed: SAS, R, Python, mathematical, statistical, data wrangling, and data visualization.
Data engineers manage massive, changing data. They create, deploy, manage, and improve data pipelines and infrastructure to transform and transfer information to scientists for queries.
- Java/Scala programming languages
- NoSQL databases (MongoDB, Cassandra)
- Apache Hadoop frameworks
Job prospects and salaries in data science
The high-tech skills of data scientists earn them attractive wages and opportunities in small and large businesses across all sectors. With over 6,000 jobs on Glassdoor, data scientists with the right skills can make an impact at some of the world’s most innovative companies.
Data Science applications, Data Science lifecycle, Data Science tools, Data Science 2024, Data analytics, Machine learning, Artificial intelligence, Big data, Predictive modeling, Data mining, Statistical analysis, Data visualization, Data-driven decision making, Natural language processing, Deep learning, Data preprocessing, Feature engineering, Data exploration, Business intelligence, Data integration, Data quality, Data ethics, Data governance, Data Science in healthcare, Data Science in finance