Skip to main content

Kacharagadla Featured Article

Why Attitude Is More Important Than IQ

When it comes to success, it’s easy to think that people blessed with brains are inevitably going to leave the rest of us in the dust. But new research from Stanford University will change your mind (and your attitude). Psychologist Carol Dweck has spent her entire career studying attitude and performance, and her latest study shows that your attitude is a better predictor of your success than your IQ. Dweck found that people’s core attitudes fall into one of two categories: a fixed mindset or a growth mindset. With a fixed mindset, you believe you are who you are and you cannot change. This creates problems when you’re challenged because anything that appears to be more than you can handle is bound to make you feel hopeless and overwhelmed. People with a growth mindset believe that they can improve with effort. They outperform those with a fixed mindset, even when they have a lower IQ, because they embrace challenges, treating them as opportunities to learn something new. Common sense wou…

It is all about Data Analytics and Data Science

“Everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.” 
This concept applies to a great deal of data terminology. While many people toss around terms like “data science,” “data analysis,” “big data,” and “data mining,” even the experts have trouble defining them. Here, we focus on one of the more important distinctions as it relates to your career: the often-muddled differences between data analytics and data science. 

Data Analytics vs. Data Science 
While data analysts and data scientists both work with data, the main difference lies in what they do with it. Data analysts examine large data sets to identify trends, develop charts, and create visual presentations to help businesses make more strategic decisions. Data scientists, on the other hand, design and construct new processes for data modeling and production using prototypes, algorithms, predictive models, and custom analysis.

people working in the tech field or other related industries probably hear these terms all the time, often interchangeably. However, although they may sound similar, the terms are often quite different and have differing implications for business. Knowing how to use the terms correctly can have a large impact on how a business is run, especially as the amount of available data grows and becomes a greater part of our everyday lives. 

Data Analytics 
What is Analytics? Analytics brings together theory and practice to identify and communicate data-driven insights that allow managers, stakeholders, and other executives in an organization to make more informed decisions. Experienced data analysts consider their work in a larger context, within their organization and in consideration of various external factors. Analysts are also able to account for the competitive environment, internal and external business interests, and the absence of certain data sets in the data-based recommendations that they make to stakeholders. 

A Master of Professional Studies in Analytics prepares students for a career as a data analyst by covering the concepts of probability theory, statistical modeling, data visualization, predictive analytics, and risk management in the context of a business environment. In addition, a master’s degree in analytics equips students with the programming languages, database languages, and software programs that are vital to the day-to-day work of a data analyst.

If data science is the house that holds the tools and methods, data analytics is a specific room in that house. It is related and similar to data science, but more specific and concentrated. Data analytics is generally more focused than data science because instead of just looking for connections between data, data analysts have a specific goal in minding that they are sorting through data to look for ways to support. Data analytics is often automated to provide insights in certain areas. 

Data analysis involves combing through data to find nuggets of greatness that can be used to help reach an organization’s goals. Essentially, analytics sorts data into things that organizations know they know or know they don’t know and can be used to measure events in the past, present, or future. Data analytics often moves data from insights to impact by connecting trends and patterns with the company’s true goals and tends to be slightly more business and strategy focused. 

Types of Data Analytics 
Four types of data analytics build on each other to bring increasing value to an organization. 
  • Descriptive analytics examines what happened in the past: Monthly revenue, quarterly sales, yearly website traffic, and so on. These types of findings allow an organization to spot trends. 
  • Diagnostic analytics considers why something happened by comparing descriptive data sets to identify dependencies and patterns. This helps an organization determine the cause of a positive or negative outcome. 
  • Predictive analytics seeks to determine likely outcomes by detecting tendencies in descriptive and diagnostic analyses. This allows an organization to take proactive action—like reaching out to a customer who is unlikely to renew a contract, for example. 
  • Prescriptive analytics attempts to identify what business action to take. While this type of analysis brings significant value in the ability to address potential problems or stay ahead of industry trends, it often requires the use of complex algorithms and advanced technology such as machine learning. 
In a 2016 survey of more than 2,000 business executives, the consultancy PwC found that organizations find descriptive analytics to be insufficient for informed, data-driven decision making. As such, diagnostic and predictive analytics are increasingly important to organizations.

Working in Data Analytics The responsibility of data analysts can vary across industries and companies, but fundamentally, data analysts utilize data to draw meaningful insights and solve problems. They analyze well-defined sets of data using an arsenal of different tools to answer tangible business needs: e.g. why sales dropped in a certain quarter, why a marketing campaign fared better in certain regions, how internal attrition affects revenue, etc.

Data analysts have a range of fields and titles, including (but not limited to) database analyst, market research analyst, sales analyst, financial analyst, marketing analyst, advertising analyst, customer success analyst, operations analyst, pricing analyst, and international strategy analyst. The best data analysts have both technical expertise and the ability to communicate quantitative findings to non-technical colleagues or clients.

Typical Background Data analysts can have a background in mathematics and statistics, or they can supplement a non-quantitative background by learning the tools needed to make decisions with numbers.

Skills and Tools Top data analyst skills include data mining/data warehouse, data modeling, R or SAS, SQL, statistical analysis, database management & reporting, and data analysis.

Roles and Responsibilities Data analysts are often responsible for designing and maintaining data systems and databases, using statistical tools to interpret data sets, and preparing reports that effectively communicate trends, patterns, and predictions based on relevant findings.

Most Valuable Skills for Data Analysts 
Effective data analysts possess a combination of technical skills and leadership skills. 

Technical skills include knowledge of database languages such as SQL, R, or Python; spreadsheet tools such as Microsoft Excel or Google Sheets; and data visualization software such as Tableau or Qlik. Mathematical and statistical skills are also valuable to help gather, measure, organize, and analyze data.

Leadership skills prepare a data analyst to complete decision-making and problem-solving tasks. These abilities allow analysts to think strategically about the information that will help stakeholders make data-driven business decisions and to communicate the value of this information effectively. For example, project managers rely on data analysts to track the most important metrics for their projects, to diagnose problems that may be occurring, and to predict how different courses of action could address a problem.

Data Science 
Organizations today are grappling with how to make sense of an inordinate amount of disparate data. The ability to transform a sea of data into actionable insights can have a profound impact—from predicting the best new diabetes treatment to identifying and thwarting national security threats. That’s why businesses and government agencies are rushing to hire data science professionals who can help do just that. By extrapolating and sharing these insights, data scientists help organizations to solve vexing problems. Bringing together computer science, modeling, statistics, analytics, and math skills—along with sound business sense—data scientists uncover the answers to major questions that help organizations make objective decisions.

Much like science is a large term that includes a number of specialties and emphases, data science is a broad term for a variety of models and methods to get information. Under the umbrella of data science is the scientific method, math, statistics, and other tools that are used to analyze and manipulate data. If it’s a tool or process done to data to analyze it or get some sort of information out of it, it likely falls under data science.

Practicing data science boils down to connecting information and data points to find connections that can be made useful for the business. Data science delves into the world of the unknown by trying to find new patterns and insights. Instead of checking a hypothesis, like what is usually done with data analytics, data science tries to build connections and plan for the future. Data science often moves an organization from inquiry to insights by providing a new perspective into the data and how it is all connected that was previously not seen or known.

Working in Data Science Data scientists, on the other hand, estimate the unknown by asking questions, writing algorithms, and building statistical models. The main difference between a data analyst and a data scientist is heavy coding. Data scientists can arrange undefined sets of data using multiple tools at the same time, and build their own automation systems and frameworks.
Typical Background Drew Conway, data science expert and founder of Alluvium, created a ven diagram that describes a data scientist as someone who has mathematical and statistical knowledge, hacking skills, and substantive expertise. 

Skills and Tools These include machine learning, software development, Hadoop, Java, data mining/data warehouse, data analysis, python, and object-oriented programming 

Roles and Responsibilities Data scientists are typically tasked with designing data modeling processes, as well as creating algorithms and predictive models to extract the information needed by an organization to solve complex problems.

Common Data Scientist Job Titles 
The most common careers in data science include the following roles. 
  • Data scientists: Design data modeling processes to create algorithms and predictive models and perform custom analysis. 
  • Data analysts: Manipulate large data sets and use them to identify trends and reach meaningful conclusions to inform strategic business decisions. 
  • Data engineers: Clean, aggregate, and organize data from disparate sources and transfer it to data warehouses. 
  • Business intelligence specialists: Identify trends in data sets. 
  • Data architects: Design, create and manage an organization’s data architecture.
Starting a Career in Data Science 
Most employers look for data science professionals with advanced degrees. Candidates for data science roles usually begin with a foundation in computer science or math and build on this with a master’s degree in data analytics. In these graduate-level programs, professionals gain core competencies in skills such as predictive analytics, statistical modeling, big data, data mining applications, enterprise analytics, data-driven decision making, data visualization, and data storytelling.

Essential Data Science Skills 
Most data scientists use the following core skills in their daily work: 
  • Statistical analysis: Identify patterns in data. This includes having a keen sense of pattern detection and anomaly detection. 
  • Machine learning: Implement algorithms and statistical models to enable a computer to automatically learn from data. 
  • Computer science: Apply the principles of artificial intelligence, database systems, human/computer interaction, numerical analysis, and software engineering. 
  • Programming: Write computer programs and analyze large datasets to uncover answers to complex problems. Data scientists need to be comfortable writing code working in a variety of languages such as Java, R, Python, and SQL. 
  • Data storytelling: Communicate actionable insights using data, often for a non-technical audience. 
Data scientists play a key role in helping organizations make sound decisions. As such, they need “soft skills” in the following areas. 
  • Business intuition: Connect with stakeholders to gain a full understanding of the problems they’re looking to solve. 
  • Analytical thinking: Find analytical solutions to abstract business issues. 
  • Critical thinking: Apply objective analysis of facts before coming to a conclusion. 
  • Inquisitiveness: Look beyond what’s on the surface to discover patterns and solutions within the data. 
  • Interpersonal skills: Communicate across a diverse audience across all levels of an organization.
Choosing Between a Data Analytics and Data Science Career 
Once you have a firm understanding of the differences between data analytics and data science—and can identify what each career entails—you can start evaluating which path is the right fit for you. To determine which path is best aligned with your personal and professional goals, you should consider three key factors: 
  1. Your educational and professional background 
  2. Your personal interests 
  3. Your desired career trajectory
Data Analysis vs. Data Science vs. Business Analysis 
The difference in what a data analyst does as compared to a business analyst or a data scientist comes down to how the three roles use data. 
  • The data analyst serves as a gatekeeper for an organization’s data so stakeholders can understand data and use it to make strategic business decisions. It is a technical role that requires an undergraduate degree or master’s degree in analytics, computer modeling, science, or math. 
  • The business analyst serves in a strategic role focused on using the information that a data analyst uncovers to identify problems and propose solutions. These analysts typically earn a degree in a major such as business administration, economics, or finance. 
  • The data scientist takes the data visualizations created by data analysts a step further, sifting through the data to identify weaknesses, trends, or opportunities for an organization. This role also requires a background in math or computer science, along with some study or insight into human behavior to help make informed predictions. At startups and other small organizations, it is not uncommon for a data analyst to take on some of the predictive modeling or decision-making responsibilities that may otherwise be assigned to a data scientist.
Why it Matters?
The seemingly nuanced differences between data science and data analytics can actually have a big impact on a company. To start, data scientists and data analysts perform different duties and often have differing backgrounds, so being able to use the terms correctly helps companies hire the right people for the tasks they have in mind. Data analytics and data science can be used to find different things, and while both are useful to companies, they both won’t be used in every situation. Data analytics is often used in industries like healthcare, gaming, and travel, while data science is common in internet searches and digital advertising. 

Data science is also playing a growing and very important role in the development of artificial intelligence and machine learning. Many companies are turning to systems that allow them to use computers to sift through large amounts of data, like on enterprise flash systems, using algorithms to find the connections that will most help their organizations reach their goals. Machine learning has immense potential across a number of industries and will undoubtedly play a huge role in how businesses are run in the future. Because of that, it is vital that organizations and employees know the difference between data science and data analytics and the role each discipline plays. 

Although the differences exist, both data science and data analytics are important parts of the future of work and data. Both terms should be embraced by companies that want to lead the way to technological change and successfully understanding the data that makes their organizations run.

Comments