Data science is a rewarding career field full of opportunities for advancement. The specialized roles (data engineer, data scientist, data analyst) are fundamental to helping organizations maximize their ability to harness data for strategic planning. Each of these data science specialties involves transforming raw datasets into actionable information.
The Data Incubator is an immersive data science bootcamp and placement company delivering the most up-to-date and hands-on data science training. You’ll leave our program more than equipped to get started in one of these data science specialties. Learn more about our Fellowship program to learn how to get started.
A data analyst gathers, organizes and analyzes information to provide business insights. Analysts work with raw information that is usually unstructured, unorganized, not managed and spread across silos within the company. Examples of this type of information are video recordings from security cameras or ATMs, audio transcripts or text messages. The analyst’s job is to transform this raw information into something that provides meaning and actionable insights.
For example, the analyst may collect raw customer information for the following use cases:
- Identify customers who are likely to churn
- Build a personalized customer experience based on past behavior
- Identify fraud incidents
Typical daily activities for a data analyst include:
- Preparing data for analysis
- Developing strategies and models to analyze the data
- Running statistical analyses on datasets to extract valuable insights that can be applied by organizations
- Presenting findings to management, peers, and end-users
Analysts rely on several tools for their work. The most common tools are:
- SQL databases, which store and retrieve information in a standardized format for easy retrieval
- Data entry software like Excel
- Visualization programs, such as Tableau, for presenting large amounts of detailed statistical information quickly and clearly
Dashboard software is an online, graphical representation of a company’s operations. It offers the ability to view everything from cash flow and inventory levels to customer loyalty scores in one place on the screen.
This streamlined information makes it easy for managers at all levels within a company to quickly make well-informed decisions about how best to allocate their time and resources towards achieving success—whether that means increasing sales or providing stellar service—to achieve organizational goals.
An analyst must be comfortable working with various tools and techniques each day. Some of these include:
- Excellent analytical skills
- Good communication skills and the ability to work well with others
- Ability to think critically, problem-solve and design effective solutions
- The ability to work well with numbers and understand the meaning of information
- The ability to analyze, interpret and report statistical information
Data engineers build databases that hold a company’s information. They create pipelines that turn raw data into formats that companies can use for decision-making. They also build the infrastructure for creating data models, machine learning, and data analytics. Typical daily activities for a data engineer include:
- Collecting and formatting information for business intelligence purposes
- Creating models
- Building pipelines to transform raw information into usable information
- Automating machine learning models
- Installing and maintaining servers and databases in a production environment
- Working with information scientists to identify, collect, and format information
- Automating tasks such as ETL (Extract, Transform, Load) to run analytics and machine learning algorithms
Engineers rely on various tools and technologies each day. The most common tools are:
- Python, SQL, Git (GitHub),
- Linux/Unix system administration
- Microsoft Azure cloud computing
- Hadoop for analytics with MapReduce jobs in Java
- Oracle DBMS to store large volumes of structured information
- Visualization tools, such as Tableau or PowerBI
- Machine Learning
- Statistical analysis packages like R and MATLAB
Data architecture is the strategy for managing information in databases. Architects build schemas (models) that describe how to store, retrieve and transform information. An architect gathers business requirements from stakeholders, investigates current information structures, and develops a blueprint for the architecture.
The architect also describes the techniques used to test and manage the database. The primary function of architects is to ensure that their organizations can access their information at all times. Architects rely on a variety of tools and technologies each day. The most common tools are:
On a typical day, an architect does the following:
- Data modeling
- Metadata management
- Quality assurance and data governance processes, such as ETL (extract-transform-load) services, data mapping, and assessments of the quality of sourcing systems
- Developing design standards for business intelligence reporting requests to meet specific needs in an organization’s strategic goals that are not already covered by existing BI tools or templates;
- Designing reports
- Identifying how best to represent the most critical metrics using graphs and dashboards
To succeed in this role, a person needs the following skills.
- Process Management
- Modeling and Analysis
- Integration Development
- Testing Techniques, Tools, and Strategies
- Data warehousing
Machine Learning Engineer
Machine learning engineers automate predictive modeling, deploy machine learning products, optimize solutions and build their own machine learning models. They design and implement solutions to create, manage and deploy predictive modeling solutions.
An average day for this role might include:
- Designing models to predict customer behaviors using analytics tools like R/Python or designing neural networks & deep learning algorithms.
- Building diverse machine learning pipelines for solving problems across different departments, such as marketing automation or fraud detection.
- Analyzing information sets to understand how well they perform for tasks such as classification or clustering.
The top tools machine learning engineers use are:
- Python is a programming language for creating machine learning models. It’s also the most utilized language in data science and analytics, meaning that it has an enormous range of libraries (over 200) developed to accommodate scientific computing needs. Python is very useful in developing codes for statistical analyses and engineering simulations. It offers beginner-friendly coding with its syntax on par with other languages like Ruby or Java, so you don’t have to be familiar with any advanced mathematics or computer sciences to get started.
- R or MATLAB for statistical modeling is a programming language and software environment for statistics, data analysis, and mathematical computation. R is an open-source package that runs on the Linux platform, which includes interfaces to many statistical packages and others such as graphics devices. MATLAB (matrix laboratory) is a high-level technical computing program used in engineering mathematics, science
- C++ is a general-purpose programming language for developing neural networks, deep belief networks, convolutional nets (CNNs), and support vector machines (SVMs).
Machine learning engineers need the following skills to be successful:
- Information analysis
- Statistics and math/probability theory
- Machine learning engineers should be comfortable with mathematics and probability theories
- The ability to use statistical methods and engineering to design efficient algorithms
- The fundamentals of machine learning, including supervised and unsupervised
- Experience in at least one programming language, such as Python or Java
- Knowledge of Scala is a plus but not required
Business Intelligence Analyst
Business intelligence analysis is a data science specialty that focuses on working with the business to transform information into insights that drive business value. A business intelligence analyst’s primary goal is to use the information to answer important questions that decision-makers need to define business strategy.
Business intelligence analysts are the bridge between business and its information. These professionals combine analytical skills with domain knowledge to provide valuable insights into a company’s information.
Data analysts and business intelligence analysts are similar roles. Analysts focus more on reporting based on analytical methods. However, business intelligence analysts focus on interpreting historical information to locate trends.
These experts use a variety of tools, including:
- Visualization Tools: These tools help professionals create visualizations, such as graphs and charts that provide insights into information.
- Reporting Tools: One of the most notable tools is Microsoft Excel, but other tools exist, such as Cognos Insight for IBM’s DB2 platform and SPSS Modeler for statistical modeling purposes.
- Business Intelligence Software Packages: Many companies offer all-inclusive BI software packages tailored to specific industries or business functions (e.g., sales).
- Dashboards/ Discovery Platforms: A dashboard aggregates vital information into one screen to make it easy to see the most important metrics at once.
- Interactive Analytics Applications: This relatively new category includes applications like Tableau Public which allow non-technical analysts to visualize information via drag-and-off interfaces.
A typical day for a business intelligence analyst includes:
- Developing a new analytic approach to extract information using Excel, SQL, or other reporting tools
- Exploring information in an interactive visualization tool such as Tableau Public
- Creating a dashboard that will help senior management make critical business decisions
- Asking questions about the data and how to best use it to support decision-making.
- Analyzing information using various tools and techniques such as SQL, Python, R, Tableau, Scala, SAS and Excel
Business intelligence analysts need the following skills to be successful:
- Business acumen and domain knowledge of the information they work with
- Strong analysis skills
- Programming and scripting language expertise (e.g., R, SAS)
- Statistical analysis
- Predictive analytics
- Knowledge of SQL, Python, R, Tableau, and other reporting tools for analysis
Marketing analysts evaluate market conditions, analyze demographic information such as income levels or purchasing habits, and make recommendations based on this research. A marketing analyst typically works with a team of marketers, statisticians, or researchers. The analysts’ goal is to build models and recommend action plans based on the information. These experts also create reports to provide insight into past marketing campaigns.
A typical day for a marketing analyst involves:
- Analyzing marketing campaigns, researching new trends in marketing, and staying up-to-date on industry news
- Analyzing research by looking at figures and graphs or reading through reports
- Performing market analysis. This could be reviewing recent purchases made by customers across different platforms to find patterns that can help identify how they respond to your company’s products or services
- Talking with clients about their information requirements and goals for using the information
A few of the most common tools marketing analysts use are:
- Data management systems
- Market research software
- Web analytics tools
- Customer Relationship Management (CRM) applications
Marketing analysts need the following skills for a successful career in this role.
- Strong quantitative reasoning skills
- A good understanding of business and marketing principles such as pricing, and marketing mix models (e.g., Pareto’s law)
- The ability to analyze information from a variety of sources (e.g., CRM systems)
- The ability to interpret statistical information effectively and accurately
- The ability to create reports to present to management
The Career For You
Data science is a rewarding career with a host of data science specialties. The field has plenty of room for growth. For those just starting, a role as a data analyst can give them a solid foundation in data science. From there, experts can move into advanced data science specialties such as machine learning, architecture, and engineering.
The Data Incubator is an immersive data science bootcamp and placement company delivering the most up-to-date training for all the latest data science specialties.