Data Science vs Data Analytics: In-Depth Comparison

Data Science vs Data Analytics: In-Depth Comparison

30 August 2023 • 11 min read

Data science vs. data analytics: which will lead your company to success? It all depends on your business needs. Data science explores aspects of unstructured data, solves strategic questions, and drives innovation. Data analysis uses structured data to find the answers to specific questions about connections and trends.

In this article, we will help you understand the main differences between these fields, including the main components, skill sets, education requirements, responsibilities, and industries for implementation. Softermii has over nine years of experience in developing advanced software using data analysis, as well as cyber security services. We crystallized our experience to highlight the difference between a data analyst and a data scientist.

Data Science and Data Analytics: Similarities 

Before diving into the similarities between these concepts, let's start with definitions.

Data science is a multidisciplinary field that uses mathematical, statistical, and computer science techniques to extract insights from large amounts of data. It is crucial for strategic purposes and allows businesses to address potential issues.

Data analytics involves the statistical analysis of ordered data to find patterns and uncover new insights. Such experts identify connections and optimize conclusions, which helps quickly improve the company's real situation.

Despite the difference between data science and data analytics, these fields have something in common:

  • Both areas rely on data management, analysis, and interpretation to understand the situation and make informed decisions.
  • They use statistical techniques to analyze data and identify relationships and patterns.
  • Data science and analytics involve examining large data sets to find anomalies, trends, and patterns.
  • They use data visualization techniques to show findings and insights in an easy-to-understand format.
  • Both fields implement machine learning methods and algorithms for data understanding or predictions.

Data Science vs. Analytics: Methodologies and Techniques

What methodologies do Data Science and Data Analytics use? Let's figure it out.

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Key Components of Data Science

To extract valuable conclusions from data, data science specialists operate with the following key components:

Statistics

Statistics examines data and discovers important trends, patterns, and relationships. Descriptive statistics help summarize and describe data; inferential statistics allows experts to make inferences or predictions, and probability distributions describe the probability of different outcomes.

Machine Learning

Machine learning is all about algorithms that learn and make predictions or decisions based on data. It includes supervised (with labeled training data), unsupervised (unlabeled data), reinforcement (through interaction with the environment), and deep learning (complex data processing).

Predictive Modeling

Predictive modeling is the creation of models that predict future outcomes based on historical data. This involves data preparation, feature and model selection, training, evaluation, and tuning to optimize performance.

Predictive modeling can be used to assess risks, forecast sales, create a system of recommendations, etc.

Data Mining

Data mining helps experts extract useful information and patterns from large data sets. To reveal hidden data, specialists use such methods as analysis of association rules (identification of relationships between variables), anomaly detection, and intellectual text analysis (sentiment analysis and topic modeling).

Feature Engineering

Feature development is creating new features or transforming existing ones for better performance of ML models. For example, you can scale functions so that one does not dominate others or combine existing functions to create new ones. 

Key Components of Data Analytics

What helps data analytics experts extract essential information and visualize it? Components of this field include:

Data Visualization

Data visualization helps present information in a simple and accessible way. This can be done using charts and graphs representing different data relationships and distributions. Dashboards consolidate multiple visualizations to form a complete view of data trends.

Descriptive Analytics

Descriptive analytics involves analyzing historical data to understand past events and trends. It helps to better understand what has already happened. Descriptive analytics techniques and actions include statistics, aggregation, and summarizing data in charts, tables, or reports.

Data Querying

Data analytics involves querying databases and datasets to obtain specific information. Structured query language (SQL) is often used for this. Key components include writing and executing SQL queries, combining data from multiple tables or sources, and data transformation. 

Exploratory Data Analysis (EDA)

Exploratory Data Analysis (EDA) lets you understand and explore data structures, patterns, and anomalies. It includes visualization to identify patterns and relationships, data distribution analysis to understand trends and variations, and correlation analysis.

Data Cleansing

Data analysis requires cleaning and pre-processing of the data to ensure its robustness. Identifying and correcting errors, inaccuracies, and inconsistencies is crucial. To do this, specialists handle missing values, work with data points that deviate from the expected pattern, and standardize data formats, units, and values. 

Data Analytics vs. Data Science: Skills and Expertise

What skills and expertise are required for each specialist? We have prepared a list of criteria highlighting data science and data analytics.

Skill Set for Data Science

Make sure your perfect data scientist has the following:

  • Master's or Ph.D. degree in an analytics-related field.
  • Expertise and experience in the industry you work in — healthcare, fintech, etc.
  • Skills in complex mathematical modeling.
  • Experience cleaning unstructured data and working with structured data.
  • Knowledge of machine learning (including unsupervised and deep learning), data analysis, and artificial intelligence.
  • Practical knowledge of big data manipulation tools, including Apache Spark, Hadoop, TensorFlow, and MySQL.
  • Ability to visualize work results.
  • Proficiency in various programming languages, including Python, R, JavaScript, C, and C++.

Skills Set for Data Analytics

For a data analyst, it is important to meet the following criteria:

  • Bachelor's degree in a data-related field.
  • Solid knowledge of statistics and probability theory.
  • Knowledge of SQL, Python, R, and MS Excel.
  • Problem-analytical approach.
  • Knowledge of relational database systems.
  • Skills in obtaining data from primary or secondary data sources.
  • Interpretation of data and analysis of results using statistical methods.
  • Knowledge of business intelligence and visualization tools, including Power BI or Tableau.
  • Data and processing skills.
  • Experience in developing strategies and solutions to improve processes.

Data Analytics vs. Data Science: Industries

The ultimate goal of data science and analytics is successful practical application in different stages of the business cycle. 

Data Science in Industries

The most famous real-life examples of Data Science applications include:

Healthcare

Doctors are actively using data science to:

  • Analyze electronic health records (EHR), medical images (MRI, CT, X-ray), and patient data to improve diagnosis and treatment planning.
  • Predict patient outcomes, disease progression, and potential complications to intervene in time and personalize treatment.
  • Analyze molecular data and predict the effectiveness of potential drugs.
  • Optimize patient care pathways, ensuring efficient allocation of resources.
  • Analyze genomic data to understand genetic predisposition and identify disease markers.

Finance

Specialists in the financial industry implement data science for:

  • Credit scoring for predicting the probability of loan default.
  • Real-time detection of fraudulent transactions and activities.
  • Customer segmentation based on spending patterns, behavior, and demographics.
  • Optimizing investment portfolios to balance risk and return.
  • Forecasting market trends, stock prices, and economic indicators.

Weather Forecasting 

Climate change affects such industries as construction, aviation, agriculture, etc. Experts use:

  • Collecting and combining days from satellites, weather stations, and climate models.
  • Modeling of atmospheric processes for forecasting temperature, humidity, and precipitation.
  • Analysis of historical data and atmospheric conditions to predict extreme weather events.
  • Modeling long-term climate trends and assessing the impact of gas emissions.

Data Analytics in Industries

Data analysis is actively used in several industries:

Advertising 

In the advertising field, data analysis allows you to:

  • Segment audience by demographic, psychographic, and behavioral factors.
  • Analyze key indicators of advertising campaigns, including conversion rate, engagement, click-through rate, etc.
  • Conduct A/B testing to test the effectiveness of different images, texts, or other factors.
  • Track social media to find brand mentions, identify new trends, and understand public sentiment.

Transportation 

For the transport industry, data analysis is useful for:

  • Fleet management by using vehicle performance data to predict maintenance needs.
  • Analyzing historic traffic patterns and real-time data to optimize routes and reduce costs.
  • Tracking the movement of goods along the supply chain to optimize logistics
  • Analyzing accident and other incident reports to identify patterns and improve safety protocols.

Criminal Justice

The use of data analysis in criminal justice contributes to:

  • Identification of crime centers and models for effective allocation of law enforcement resources.
  • Developing models to predict the likelihood of reoffending and inform rehabilitation programs.
  • Evaluating investigative data to identify factors contributing to case closures and successful outcomes.
  • Identification of witnesses and victims using surveillance footage, social media, and communication records.
  • Offenders’ profiles based on their criminal behavior to understand motives and patterns.

Data Analyst vs. Data Scientist: Job Roles and Responsibilities

Data scientists and data analysts use data differently.

Responsibilities of a Data Scientist

The data scientist has the following technical areas of responsibility:

  • Finding reliable data sources and collecting data.
  • Preparation of groups of structured and unstructured data, their cleaning, and integrity check.
  • Intelligent data analysis by creating ETL pipelines and finding trends and patterns.
  • Development and use of many ML techniques and predictive models.
  • Writing code to automate and create ML resource libraries.
  • Preparation of answers to complex business problems.
  • Identify new trends in data for business forecasting.
  • Visualization of data in an accessible way.

Responsibilities of a Data Analyst

A data analyst also has a complex and important role:

  • Collection of data from reliable primary and secondary sources.
  • Data cleaning and filtering.
  • Database management.
  • Monitoring and administration of data systems.
  • Using statistical methods to identify relevant patterns, regularities, and hidden trends.
  • Development of methods and techniques for strengthening business processes.
  • Data visualization using Tableau, Power BI, etc.

Data Science vs Data Analytics: Salary 

Data science or data analytics, which is better paid? According to Glassdoor, in the US, a data analyst earns an average of $80,102 per year, while their data scientist counterpart gets around $152,253. Let's review how the situation looks in different countries.

Country

Data Analyst

Data Scientist

USA

$80,102

$152,253

Canada

$52,281

$75,887

Australia

$62,749

$78,437

Germany

$66,591

$75,759

UK

$48,459

$70,573

Czech Republic

$61,512

$97,788

Poland

$37,608

$58,248

Ukraine

$25,332

$37,356

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But should you hire such an employee on staff or use outsourcing? Well, outsourcing offers you some great pros:

  • You can save on recruiting, hiring, compensation (vacation, sick, insurance), and equipment costs.
  • You will get access to niche experts who specialize in your industry and are familiar with the core processes.
  • You can cooperate with specialists from any country and find more cost-effective options.
  • Such experts already have refined processes; therefore, they quickly adapt and show great results.
  • Hiring a data analyst or scientist from a trusted vendor minimizes the budget and timeline risks.

Conclusion

Is data science and data analytics the same? No, they both work with data, but it happens in different ways. A data analyst focuses on solving current tasks and identifying relationships and anomalies. A data scientist creates data-crunching algorithms that help organizations solve strategic business problems, predict trends, and push innovation.

If you need one of these specialists but don't know how to find them yourself, we can help. Softermii’s team includes professionals with deep expertise in both data analytics and data science. We work with businesses in healthcare, real estate, fintech, e-commerce, and other industries.

Contact us to discuss your project with our team, who can start working on it in just one week!

Frequently Asked Questions

Can someone work as both a data scientist and a data analyst?

Yes, a specialist can work in both fields at the same time. Data scientists typically deal with more complex tasks, such as predictive modeling and algorithm development, while data analysts focus on descriptive analysis and reporting. However, these experts both work with data, so if a data analyst wants to and has a strong background, they can move into data science, developing their skills in statistics, programming, and machine learning.

How does machine learning fit into both data science and data analytics?

Machine learning is a critical component of both fields. In data science, ML algorithms are used to develop predictive models, and data analysts use them to discover patterns, perform clustering, and build complex models.

Is programming knowledge necessary for both data science and data analytics roles?

Yes, in data science, programming skills allow you to process, clean, and transform data, as well as implement complex algorithms. Data analysts can perform data queries, automate repetitive tasks, and create custom scenarios for visualization and reporting.

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