Intro to Analytics Chapter 1.1.2

Data Analytics Projects 

• Descriptive Analytics: Within a project, this type of analytics focuses on summarizing and describing historical data to provide insights into past trends and patterns. Descriptive analytics helps to answer questions such as "What happened?" and "How many times did it happen?" Descriptive analytics is perfect for the retail industry because retailers can analyze sales data, customer behavior, and inventory levels to identify trends and patterns. Descriptive analysis can also be used in healthcare, finance, and manufacturing industries. 

• Diagnostic Analytics: Diagnostic analytics analyzes past data to identify the root causes of specific outcomes or events. Diagnostic analytics answers questions such as "Why did an event happen?" and "What caused it to happen?" In the healthcare industry, diagnostic analytics helps doctors and other medical professionals make more informed decisions about treatment options to improve patient outcomes. 

• Predictive Analytics: Using historical data to forecast future outcomes, predictive analytics helps to answer questions such as "What is likely to happen in the future?" and "How can we prepare for it?" Banks and other financial institutions can use predictive analytics to analyze customer data and identify potential risks like defaulting on loans or credit card fraud. Predictive analytics allows financial institutions to make substantial decisions about lending and improve risk management. 

• Prescriptive Analytics: This type of analytics recommends actions that can be taken to optimize or improve a situation. Prescriptive analytics considers data analysis and modeling to provide specific recommendations on what to do next. It answers questions such as "What should we do?" and "How can we improve the outcome?" This is the ideal analytics for the transportation industry because transportation companies can analyze traffic patterns, weather data, and other variables to optimize routes and reduce fuel consumption. 

• Exploratory Analytics: This type of analytics involves exploring and analyzing data to identify potential trends, patterns, and relationships. Exploratory analytics is often used when there is no clear objective or question to answer, and the goal is to uncover new insights or opportunities. Exploratory analytics can be used in the manufacturing sector to identify areas for process improvement.

Data Analyst

• Data collection: Collecting and gathering large datasets, including databases, surveys, and other data sources from various platforms

• Data cleaning, preparation, and processing: Cleaning and preparing datasets for analysis by correcting data errors, removing duplicates, and reviewing accuracy; these processes are also known as data filtering, data integration, data classification, data munging, and data summarization

• Data analysis: Utilizing statistical methods and data visualization tools to analyze large datasets, identify trends, and compile insights to assist organizations when making business decisions

• Data reporting and visualization: Creating clear, concise reports and visuals that easily communicate findings to team members and key stakeholders

• Predictive analysis: Using algorithms (sets of detailed instructions that are used to solve specific problems or calculate specific operations) to assist with predicting trends and future outcomes based on historical data 

• Data-driven decision-making: Collaborating with various team members and stakeholders to identify opportunities for improvement to make data-driven decisions; you could make a data-driven decision by analyzing the data on customer behavior and preferences to identify which products are most popular and why

• Continuous improvement: Involves professional development, continuously monitoring and evaluating the effectiveness of decision-making processes, and recommending improvements to drive better outcomes; data analysts must stay current with the latest trends and tools within the field; continuous improvement is needed because the field of data analytics is constantly evolving with new data sources, tools, and techniques, and data analysts must be able to adapt to these changes in processes over time

Business Intelligence Analyst

• Reporting: Creating reports based on certain findings, which organizations use to make decisions

• Forecasting: Utilizing historical data to forecast future trends to identify potential risks and opportunities within organizations; additionally, BI analysts can utilize forecasting beyond the structure of their organization for various services and products

• Dashboard creation: Creating dashboards to provide real-time access to key performance indicators (KPIs), which helps business leaders identify areas within the business that may require attention

• Data analysis: After data is collected, analyzing the data to identify certain trends and possible patterns

• Data collection: Responsibility for the collection of data from various sources, which can include third-party sources, external databases, and internal systems

• Data governance: Ensuring the data is accurate and secure, which includes reviewing quality data standards, reviewing data security measures, and monitoring data usage

Decision Scientist

• Statistical analysis: A decision scientist uses statistical analysis to create and apply mathematical models that optimize decision-making processes and outcomes, using statistical models to analyze data and identify patterns and relationships that inform decisions; in contrast, a BI analyst uses statistical analysis to analyze data and extract insights that can inform business decisions, such as identifying trends or opportunities for improvement

• Modeling and simulation: Building models and simulations to understand how variables (containers or storage locations that hold a value) interact and how they are used to affect decisions within an organization; these variables can be manipulated throughout the data analysis process to achieve the necessary results

• Optimization: Mathematical optimization techniques are mathematical methods used to find the optimal solutions to a problem and to identify the best course of action that maximizes benefits and minimizes risks within an organization; mathematical optimization techniques can help make better decisions by identifying the best course of action based on a set of constraints and objectives

• Risk analysis: Assessing risks associated with different decision options and developing strategies to mitigate those risks 

• Decision support: Providing decision-makers with the tools and insights necessary to make informed decisions

• Communication: Presenting complex data and analysis clearly and understandably to stakeholders across different functions and levels of the organization; these stakeholders can range from junior to senior levels, depending on their roles

• Strategic planning: Collaborating with key stakeholders to develop long-term strategic plans that align with organizational objectives or goals

• Innovation: Identifying new opportunities for growth and innovation through data-driven insights and analysis

• Continuous improvement: Continuously monitoring and evaluating the effectiveness of decision-making processes and recommending improvements to drive better outcomes

Machine Learning Engineer

• Data preprocessing: A step in data analysis and machine learning that involves transforming and preparing raw data to make it suitable for analysis

• Model selection: Selecting the appropriate machine learning models for the specific problem and data, which includes tasks such as selecting classification or regression models, neural networks, decision trees, and other models; in machine learning, a model is a mathematical representation of a system or process trained on a dataset to make predictions or decisions on new, unseen data

• Model training: Training machine learning models on data using various algorithms

• Model evaluation: Evaluating machine learning models on test data to measure the model’s accuracy, precision, recall, and other performance metrics

• Model deployment: Deploying machine learning models into production environments and ensuring the models are scalable, reliable, and efficient

• Software engineering: Developing software applications that integrate machine learning models into products or services

• Algorithm development: Creating and developing new machine learning algorithms or adapting existing algorithms to solve specific problems

• Performance optimization: Optimizing machine learning models and algorithms for faster computation, lower memory usage, and higher accuracy

• Cloud computing: Using cloud computing platforms to deploy and scale machine learning systems

• Collaboration: Collaborating with data scientists, software developers, and business stakeholders to identify and solve machine learning problems that align with business goals and objectives

Data Engineer

• Data pipeline development: Assisting with designing and developing data pipelines that move data from source systems to data storage systems, which includes tasks such as data ingestion, transformation, and loading. Data extract, transformation, and loading (ETL) are three stages in the process of preparing data for use in a machine learning project. Data ingestion refers to the process of collecting and importing raw data from various sources, such as databases or files. This raw data may be in different formats and may require cleaning and preprocessing to remove inconsistencies, missing values, or errors. Data transformation involves converting the raw data into a format that can be used by a machine learning model. Data loading refers to the process of loading the transformed data into a database or a storage system that can be accessed by the machine learning model during training and testing.

• Data storage management: Managing the storage of data in databases, data lakes, and data warehouses, which includes tasks such as data partitioning, indexing, and replication. Data lakes and data warehouses are two common types of data storage systems used in modern data architecture. Data lakes are designed to store raw and unstructured data at scale, while data warehouses are designed to store structured data to support business intelligence and analytics. Unstructured data is not organized in a predefined manner and includes data such as videos, audio files, emails, and images, while structured data is organized in a predefined manner, such as sales and inventory data.

• Data quality control: Ensuring the quality of data by identifying and correcting errors, inconsistencies, and missing values

• Data security: Implementing data security policies and procedures to protect sensitive data from unauthorized access, theft, or corruption

• Data architecture design: Designing and implementing data architecture that meets the organization's requirements for scalability, performance, and reliability

• Extract, transform, load (ETL): Creating and developing ETL processes that transform raw data into structured data that can be used for analysis

• Data integration: Integrating data from various sources, formats, and systems to create a unified view of datasets

• Managing big data: Involves handling large volumes of data that are too complex or too large to be processed using traditional methods; managing big data requires using specialized tools and techniques to store, process, and analyze the data efficiently and effectively

• Cloud computing: Working with cloud computing platforms to deploy and manage data infrastructure

• Collaboration: Collaborating with data scientists, analysts, and stakeholders to understand data requirements and design infrastructures that support data-driven decision-making for businesses and organizations

  Data Scientist

  • Data analysis: Analyzing large and complex data sets using statistical and machine learning techniques to identify patterns, trends, and insights that inform decision-making

  • Data visualization: Developing visualizations such as charts, graphs, and dashboards to communicate insights and trends to nontechnical stakeholders

  • Data cleaning: Cleaning and preparing data for analysis, which may involve data cleaning, normalization, and feature engineering

  • A/B testing: Designing and conducting A/B tests to evaluate the effectiveness of different strategies or interventions; A/B tests are statistical methods used to compare two versions of a product or service to determine which version performs better

  • Machine learning: Building machine learning models to automate decision-making processes, such as recommendation engines, fraud detection systems, or chatbots

  • Data storytelling: Developing narratives and presentations that explain complex technical concepts and insights to nontechnical stakeholders clearly and concisely

  • Collaboration: Collaborating with data engineers, software developers, and business stakeholders to understand data requirements and design solutions that align with business goals

  • Experimentation: Designing and conducting experiments to test hypotheses and validate assumptions within the data

  • Continuous improvement: Continuously monitoring and evaluating the effectiveness of their solutions and processes to improve decision-making outcomes over time