WHAT ACTUALLY IS DATA SCIENCE? WHAT ARE THE KEY AREAS TO FOCUS IN DATA SCIENCE?

Data science is the study of data. It involves developing methods of recording, storing, and analyzing data to effectively extract useful information. Data science is more closely related to the mathematics field of Statistics, which includes the collection, organization, analysis, and presentation of data.

Example:

Such as; Identification and prediction of disease, Optimizing shipping and logistics routes in real-time, detection of frauds, healthcare recommendations, automating digital ads, etc. Data Science helps these sectors in various ways.

• KEY AREAS TO FOCUS IN DATA SCIENCE

1. Data Engineering and Data warehousing in Data Science

• Data Engineering refers to transforming data into a useful format for analysis.
• This often involves managing the source, structure, quality, storage, and accessibility of the data so that it can be queried and analyzed by other analysts.

2. Data mining and Statistical Analysis

• Data Mining refers to the application of statistics in the form of exploratory data analysis and predictive models to reveal patterns and trends in data from existing data sources.
• This person will be able to look at a business problem.
• Translate it to a data question, create predictive models to answer the question and story tell about the findings.

3. Cloud and Distributed Computing

• Cloud and System Architecture refers to designing and implementing enterprise infrastructure and platforms required for cloud and distributed computing.
• The role also analyses system requirements.
• Also ensures that systems will be securely integrated with current applications and business uses.

4. Database management and Architecture

• This role is responsible for designing, deploying, and maintaining databases in support of high volume, complex data transactions for specific services or groups of services.

5. Business Intelligence and Strategy

• Some of the key responsibilities in BI include improving back-end data sources for increased accuracy and simplicity.
• Also building tailored analytics solutions, managing dashboards, reporting to stakeholders, identifying opportunities and recognizing best practices in reporting and analysis: data integrity, test design, analysis, validation, and documentation.

6. ML/ Cognitive Computing Development

• This is what most people associate with data science: “making robots”.
• This is a larger, more complex version of data mining and statistical analysis.
• These people focus more on getting all the input you need to feed the model; building data pipelines, convenient data sources, A/B testing and bench marking infrastructure.
• If this is done you might focus on building the actual algorithms.
• This part more often than not involves well known, industry standard tools and statistical techniques.
• This focus area has become a buzzword in many organizations.
• I encourage looking into sub-fields within it in order to truly identify what you like.

7. Data Visualization and Presentation

• Being able to present data in a visually appealing way has become part of almost every business analyst and data scientist role.
• When this focus area becomes an actual role in a company.
• their main responsibility includes creating BI solutions for teams and customers.
• In other instances, it can be more graphic design oriented.

8. Sector Specific Data analytics (Healthcare, finance, Insurance)

• If you studied Healthcare, Finance or something that requires domain-knowledge expertise to analyze.
• you might opt to look into simple analyst positions within organizations in these industries.
• Again, the technical expertise of these roles will depend on the expectations of the company hiring and the tools they use.