Today’s economy is leaning more toward analytics—companies have been collecting data for many years. According to LinkedIn, there is a huge demand for people who can mine and interpret data.

Fortunately, as data has multiplied, so has the ability to collect, organize, and analyze it. Data storage is cheaper than ever, processing power is more massive than ever, and tools are more accessible than ever to mine the zettabytes of available data for business intelligence. In recent years, data analysis has done everything from predict stock prices to prevent house fires.

Research from the International Data Corporation suggests the digital universe—the amount of data produced and copied globally—more than doubles in size every two years. By 2021, it will have grown by a factor of 10 over where it stood in 2013.

Organizations that know how to collect and analyze large datasets can use this knowledge to identify and solve problems, improve business strategies, and minimize risk. This is the current state of the art in the field of analytics. Over time, however, experts predict that the amount of data available to organizations may actually become a potential barrier to progress, as the ability to collect it outpaces the means to sort and process it. Along with organizations continued to drive to extract ever more useful information from new and old datasets, such projections have put data expertise in high demand.

To solve this problem, some operations are done on data to optimize

Trending options in the Data Science career available are Data Analysts, Data Architects, Data Scientists, and Data Engineers.

Data Analyst

Data analysts collect, process, and perform statistical analyses of data. Their skills may not be as advanced as data scientists (e.g. they may not be able to create new algorithms), but their goals are the same – to discover how data can be used to answer questions and solve problems.

Depending on their level of expertise, data analysts may:

  • Work with IT teams, management and/or data scientists to determine the organizational goal
  • Mine data from primary and secondary sources
  • Clean and prune data to discard irrelevant information
  • Analyze and interpret results using standard statistical tools and techniques
  • Pinpoint trends, correlations and patterns in complicated data sets
  • Identify new opportunities for process improvement
  • Provide concise data reports and clear data visualizations for management
  • Design, create and maintain relational databases and data systems
  • Triage code problems and data-related issues

Data analysts are sometimes called “junior data scientists” or “data scientists in training.” Instead of being free to create their own big data projects, they may be limited to tackling specific business tasks using existing tools, systems, and data sets.

However, there are plenty of companies that don’t make a clear distinction between the two roles. In some cases, a data analyst/scientist could be writing queries or addressing standard requests in the morning and building custom solutions or experimenting with relational databases, Hadoop, and NoSQL in the afternoon.

What Kind of Skills Do I need to become Data Analyst?

Technical Skills

  • Statistical methods and packages (e.g. SPSS)
  • R and/or SAS languages
  • Data warehousing and business intelligence platforms
  • SQL databases and database querying languages
  • Programming (e.g. XML, JavaScript or ETL frameworks)
  • Database design
  • Data mining
  • Data cleaning and munging
  • Data visualization and reporting techniques
  • Working knowledge of Hadoop & MapReduce
  • Machine learning techniques

Business Skills

  • Analytic Problem-Solving: Employing best practices to analyze large amounts of data while maintaining intense attention to detail.
  • Effective Communication: Using reports and presentations to explain complex technical ideas and methods to an audience of laymen.
  • Creative Thinking: Questioning established business practices and brainstorming new approaches to data analysis.
  • Industry Knowledge: Understanding what drives your chosen industry and how data can contribute to the success of a company/organization strategy.

For your reference, you can have a look at the job description of Data Analysts

Data Architect

Data Architects create blueprints for data management systems. After assessing a company’s potential data sources (internal and external), architects design a plan to integrate, centralize, protect and maintain them. This allows employees to access critical information in the right place, at the right time.

A data Architect may be required to:

  • Collaborate with IT teams and management to devise a data strategy that addresses industry requirements
  • Build an inventory of data needed to implement the architecture
  • Research new opportunities for data acquisition
  • Identify and evaluate current data management technologies
  • Create a fluid, end-to-end vision for how data will flow through an organization
  • Develop data models for database structures
  • Design, document, construct and deploy database architectures and applications (e.g. large relational databases)
  • Integrate technical functionality (e.g. scalability, security, performance, data recovery, reliability, etc.)
  • Implement measures to ensure data accuracy and accessibility
  • Constantly monitor, refine and report on the performance of data management systems
  • Meld new systems with existing warehouse structures
  • Produce and enforce database development standards
  • Maintain a corporate repository of all data architecture artifacts and procedures

Some companies need data architects who are ninjas in data modeling techniques; others may want experts in data warehousing, ETL tools, SQL databases, or data administration. Data architects are likely to be senior-level employees with plenty of years in business intelligence under their belts.

What Kind of Skills do I need to become Data Architect?

Technical Skills

  • Application server software (e.g. Oracle)
  • Database management system software (e.g. Microsoft SQL Server)
  • User interface and query software (e.g. IBM DB2)
  • Enterprise application integration software (e.g. XML)
  • Development environment software
  • Backup/archival software
  • Agile methodologies and ERP implementation
  • Predictive modeling, NLP and text analysis
  • Data modeling tools
  • Data mining
  • UML
  • ETL tools
  • Python, C/C++ Java, Perl
  • UNIX, Linux, Solaris and MS Windows
  • Hadoop and NoSQL databases
  • Machine learning
  • Data visualization

Business Skills

  • Analytical Problem-Solving: Approaching high-level data challenges with a clear eye on what is important; employing the right approach/methods to make the maximum use of time and human resources.
  • Effective Communication: Carefully listening to management, data analysts and relevant staff to come up with the best data design; explaining complex concepts to non-technical colleagues.
  • Expert Management: Effectively directing and advising a team of data modelers, data engineers, database administrators and junior architects.
  • Industry Knowledge: Understanding the way your chosen industry functions and how data are collected, analyzed and utilized; maintaining flexibility in the face of big data developments.

For your reference, you can have a look at the job description of Data Archiect

Data Scientist

Some companies treat the titles of “Data Scientist” and “Data Analyst” as synonymous. But there’s really a distinction between the two in terms of skill set and experience. Though data scientists and data analysts have the same mission in an organization—to glean insights from the massive pool of data available—a data scientist’s work requires more sophisticated skills to tackle a higher volume and velocity of data.

As such, a data scientist is someone who can do undirected research and tackle open-ended problems and questions. Data scientists typically have advanced degrees in a quantitative field, like computer science, physics, statistics, or applied mathematics, and they have the knowledge to invent new algorithms to solve data problems.

“A data scientist is someone who is better at statistics than any software engineer and better at software engineering than any statistician” – Josh Wills

On any given day, a data scientist’s responsibilities may include:

  • Conduct undirected research and frame open-ended industry questions
  • Extract huge volumes of data from multiple internal and external sources
  • Employ sophisticated analytics programs, machine learning and statistical methods to prepare data for use in predictive and prescriptive modeling
  • Thoroughly clean and prune data to discard irrelevant information
  • Explore and examine data from a variety of angles to determine hidden weaknesses, trends and/or opportunities
  • Devise data-driven solutions to the most pressing challenges
  • Invent new algorithms to solve problems and build new tools to automate work
  • Communicate predictions and findings to management and IT departments through effective data visualizations and reports
  • Recommend cost-effective changes to existing procedures and strategies

Every company will have a different take on job tasks. Some treat their data scientists as data analysts or combine their duties with data engineers, others need top-level analytics experts skilled in intense machine learning and data visualizations.

As data scientists achieve new levels of experience or change jobs, their responsibilities invariably change. For example, a person working alone in a mid-size company may spend a good portion of the day in data cleaning and munging. A high-level employee in a business that offers data-based services may be asked to structure big data projects or create new products.

What Kind of Skills Do I need to become Data Scientist?

Technical Skills

  • Math (e.g. linear algebra, calculus and probability)
  • Statistics (e.g. hypothesis testing and summary statistics)
  • Machine learning tools and techniques (e.g. k-nearest neighbors, random forests, ensemble methods, etc.)
  • Software engineering skills (e.g. distributed computing, algorithms and data structures)
  • Data mining
  • Data cleaning and munging
  • Data visualization (e.g. ggplot and d3.js) and reporting techniques
  • Unstructured data techniques
  • R and/or SAS languages
  • SQL databases and database querying languages
  • Python (most common), C/C++ Java, Perl
  • Big data platforms like Hadoop, Hive & Pig
  • Cloud tools like Amazon S3

Business Skills

  • Analytic Problem-Solving: Approaching high-level challenges with a clear eye on what is important; employing the right approach/methods to make the maximum use of time and human resources.
  • Effective Communication: Detailing your techniques and discoveries to technical and non-technical audiences in a language they can understand.
  • Intellectual Curiosity: Exploring new territories and finding creative and unusual ways to solve problems.
  • Industry Knowledge: Understanding the way your chosen industry functions and how data are collected, analyzed and utilized.

For your reference, you can have a look at the job description of Data Scientists

Data Engineer

Data engineers build massive reservoirs for big data. They develop, construct, test and maintain architectures such as databases and large-scale data processing systems. Once continuous pipelines are installed to – and from – these huge “pools” of filtered information, data scientists can pull relevant datasets for their analyses.

In his/her role as a hardcore builder, a data engineer may be required to:

  • Design, construct, install, test and maintain highly scalable data management systems
  • Ensure systems meet business requirements and industry practices
  • Build high-performance algorithms, prototypes, predictive models and proof of concepts
  • Research opportunities for data acquisition and new uses for existing data
  • Develop dataset processes for data modeling, mining and production
  • Integrate new data management technologies and software engineering tools into existing structures
  • Create custom software components (e.g. specialized UDFs) and analytics applications
  • Employ a variety of languages and tools (e.g. scripting languages) to marry systems together
  • Install and update disaster recovery procedures
  • Recommend ways to improve data reliability, efficiency and quality
  • Collaborate with data architects, modelers and IT team members on project goals

Data engineers may work closely with data architects (to determine what data management systems are appropriate) and data scientists (to determine which data are needed for analysis). They often wrestle with problems associated with database integration and messy, unstructured data sets. Their ultimate aim is to provide clean, usable data to whoever may require it.

What Kind of Skills Do I need to become Data Engineer?

Technical Skills

  • Statistical analysis and modeling
  • Database architectures
  • Hadoop-based technologies (e.g. MapReduce, Hive and Pig)
  • SQL-based technologies (e.g. PostgreSQL and MySQL)
  • NoSQL technologies (e.g. Cassandra and MongoDB)
  • Data modeling tools
  • Python, C/C++ Java, Perl
  • Mat Lab, SAS, R
  • Data warehousing solutions
  • Predictive modeling, NLP, and text analysis
  • Machine learning
  • Data mining
  • UNIX, Linux, Solaris, and MS-Windows

Business Skills

  • Creative Problem-Solving: Approaching data organization challenges with a clear eye on what is important; employing the right approach/methods to make the maximum use of time and human resources.
  • Effective Collaboration: Carefully listening to management, data scientists and data architects to establish their needs.
  • Intellectual Curiosity: Exploring new territories and finding creative and unusual ways to solve data management problems.
  • Industry Knowledge: Understanding the way your chosen industry functions and how data can be collected, analyzed, and utilized; maintaining flexibility in the face of big data developments.

For your reference, you can have a look at the job description of Data Engineers

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