It is improbable to be a domain expert in everything to know about operating with Data Science. The best way to make yourself remarkably valuable in a team is to learn everything, but be a boss of “something.”
The fact is that once you are appointed as a data “something,” the company will assign you to a team, and other people will complement the skills that you have or require to get the job accomplished.
The intention is to become a commissioned resource about a specific issue. Therefore, the focus domain you pick will probably be related to your background, experience, or interests in other fields of study.
Data Science Life Cycle
The Data Science course life cycle consists of the following segments.
- Capture – data acquisition, data entry, signal reception, data extraction
- Maintain – data warehousing, data cleansing, data staging, data processing, and data architecture
- Process – data mining, clustering/classification, data modeling, data summarization
- Analyze – exploratory/confirmatory, predictive analysis, regression, text mining, qualitative analysis
- Communicate – business intelligence, data reporting, data visualization, decision making
The term “data scientist” was neologized as recently as 2008, when companies recognized the need for data professionals to create and analyze massive amounts of data.
In 2009, Hal Varian, Google’s chief economist and UC Berkeley professor of information sciences, business, and economics, predicted the importance of adapting to technology’s influence and reconfiguring different industries.
Influential data scientists can identify relevant questions, accumulate data from various data sources, synthesize the information, translate results into solutions, and express their findings in a way that positively influences business decisions. These skills are needed in almost all industries, causing skilled data scientists to be increasingly relevant to companies.
Hold in brain that a relevant job position might challenge you to perform different focus areas. Therefore, a focus area doesn’t imply you can ONLY do that one thing, but solely that you are BEST at arranging it.
1. Data Engineering and Data Warehousing
Data Engineering leads to transforming data into a suitable format for analysis which often involves managing the source, structure, quality, storage, and accessibility of the data to be queried and analyzed by different analysts.
2. Data Mining and Statistical Analysis
Data Mining refers to applying statistics in the form of exploratory data analysis and predictive models to reveal patterns and trends in data from existing data sources. For example, this person will look at a business problem and translate it to a data question, create predictive models to answer the question, and tell about the findings.
3. Database Management and Architecture
Cloud and System Architecture commits to designing and executing enterprise infrastructure and platforms needed for cloud and distributed computing. The position also investigates system requirements and guarantees that systems will be securely united with current applications and business practices.
4. Cloud and Distributed Computing
This function is accountable for designing, deploying, and managing databases to support high volume, complex data activities for specific settings or groups of services.
5. Business Intelligence and Strategy
Some of the fundamental obligations in BI include updating back-end data sources for improved accuracy and simplicity, developing tailored analytics solutions, maintaining dashboards, reporting to stakeholders, recognizing opportunities, and recognizing best practices in reporting and analysis: data integrity, analysis, validation, test design, and documentation.
6. ML / Cognitive Computing Development
What most people associate with data science is “making robots.” It is a larger, more complex version of data mining and statistical analysis. These people focus on getting all the input you need to feed the model, build data pipelines, convenient data sources, A/B testing, and benchmarking infrastructure. When/if this is completed, you might focus on developing the actual algorithms/models, but this part more often than not includes well-known, industry-standard tools and statistical systems. This locus area has shifted a buzzword in various organizations, so work to encourage looking into its sub-fields to recognize what you desire genuinely.
7. Data Visualization and Presentation
Being able to present data visually appealing has become part of almost every business analyst and data scientist role. When this focus area becomes an essential role in a company, its primary responsibility includes building BI solutions for teams and customers based on particular business requirements and exercise illustrations. In other situations, it can be more graphic design-oriented.
8. Operations-Related Data Analytics
If you don’t consider yourself very technical yet have a passion for problem solving and processes, these might be the right path for you. These types of roles focus on leveraging the tools and data provided by the other data science team members to find opportunities for advancement within the business’s operations. These can either be concentrated on logistics, financials, technology, human resources, etc.
9. Market-Related Data Analytics
This role has different levels of technical expertise depending on the level of analysis and company. These people tend to focus on more external data related to customers, sales, and marketing, yet their purpose is similar to those in operations: track performance and finds opportunities.
10. Sector-Specific Data Analytics
Lastly, if you studied Healthcare, Finance, or something that requires domain-knowledge expertise to analyze, you might look into simple analyst positions within organizations in these industries. But, again, the technical knowledge of these roles will depend on the expectations of the company hiring and the tools they use.
Summing Up
Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills. To uncover valuable intelligence for their organizations, data scientists must master the full spectrum of the data science life cycle and maintain flexibility and understanding to maximize revenues at each phase of the process.