Data science and statistics are an important part of today’s growth. The combination of the two has driven the world to tremendous advancement and ease. Though they are two different fields, they are often used interchangeably among the users. The distinction between the two is crucial to understanding the right usage of each and to seek good career opportunities in the specific domain of interest. Guiding you through the differences and similarities between the two, here is a comprehensive comparison. 

Overview of Data Science

Data science concerns dealing with data to organize, extract, and analyze it. The data to be processed requires multi-step processing performed through data cleaning, integration, visualization, and statistical analysis. It handles the data by developing models to provide solutions to complicated problems. Offering a multidisciplinary approach, the information is used to interpret, analyze, and be used in decision-making. Data science experts leverage the combined power of machine learning and computer statistics to dive into the depths of data and come up with valuable insights.

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Key Tools Data Scientist Should Be Aware of 

Data scientists are required to deal with the following tools in their daily tasks, which includes: 

  • Programming languages like R and Python: They are used for data analysis, machine learning, statistics, visualization, and scripting. They are also used for exploratory data analysis. 
  • RDBMS: MySQL is the Relational Database Management System that is specifically used for data storage, retrieval, and preprocessing. 
  • Big Data tools: Apache Hadoop and Apache Spark are commonly used where the former finds applications for distributed storage and processing large datasets. The latter, spark, offers a fast and general-purpose cluster-computing framework for big data processing and analytics. 
  • Data analysis: SAS or SPSS are a few statistical software that are often used in different industries for domain-specific analysis. 
  • Data visualization: Tableau, Matplotlib, Seaborn, and ggplot2 are among the commonly used software to communicate the work and findings by Data Scientists. 
  • Data manipulation: It is achieved via libraries of programming languages such as Pandas and NumPy. 

Overview of Statistics

Statistics is more inclined toward equations and mathematical concepts. These are used for data analysis and encompass wide applications crucial in testing and interpreting the information, further driving the statisticians to make decisions. Statisticians are also capable of working with different sets of data. The prime work here is to find similarities or differences between the two groups and make predictions based on results derived from the interpretation. 

Data Science Vs. Statistics: Key Differences

Parameter

Data Science

Statistics

Disciplines 

Interdisciplinary

Multidisciplinary 

Definition

Combines different fields to solve real-life problems and for decision-making 

Uses statistical tools for analyzing data and for decision-making

Goal

Handle different and voluminous datasets and identify trends and patterns

Determines cause and effect relationship, useful for smaller sampled and quantitative data

Approach

Identifies the most accurate model through comparison

Determines data consistency of a simple model and further continues to build and improvise the model depending on data needs

Important aspects

Data mining, pre-processing, Exploratory Data Analysis (EDA), and model building and optimization 

Mean, median, mode, standard deviation, and variation 

Application

Computer vision, Search Engine, Natural Language Processing, Recommender system and Disaster Management

Areas with random variations in sampled data like information technology, marketing, accounting, medical, economics, finance, and business

Technical Skills

Degree in data science, understanding of algorithms, good analytical skills, hands-on experience in tools and programming languages 

Degree in mathematics or statistics, advanced knowledge of probability, calculus, and linear algebra with expertise in Excel, SPSS, and SAS

Soft skills

Teamwork, organization, problem-solving, and communication

Communication and planning

Practical applications

Healthcare, finance, manufacturing industry, transportation, logistics, aviation, e-commerce and retail

Weather forecasting, consumer goods, research, stock market, public administration, the insurance industry, sports, and disaster prevention

Career opportunities

Data analysts, data scientists, data engineers, and business intelligence analysts

Statisticians, public health statisticians, and econometricians

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Key Tools Statistician Should Be Aware of 

The statisticians are required to work daily on the following software: 

  • Statistical software: It is the primary and most essential need for statisticians. Performed through either SAS or SPSS, it is used for business intelligence, advanced analytics, and data management. They are also crucial for reporting. SAS is generally suited for the healthcare and finance sector, while SPSS is of more use in social science research. 
  • Mathematical and symbolic computation: These are used to tackle complex mathematical modeling and simulation, finding more usage in academics. 
  • Excel and spreadsheet tools: Microsoft Excel is a common choice due to built-in functions and tools with efficient components for data visualization. 

Common Similarities Between Data Science And Statistics

There are some significant similarities between the two domains, which are as follows:

Data Collection

The data collection involves similar steps, which are accessing the database, conducting experiments and surveys, and utilizing APIs. It follows data aggregation, which involves techniques like data mining, data recording, and web scraping through devices and sensors. Further, the process also performs validation and verification not to allow compromise with the quality. 

Data Pre-Processing

It includes cleaning the previously obtained data. The process incorporates the removal of inconsistencies, noise, or errors to handle the outliers and missing values to prohibit compromise with reliability and integrity. 

Data Analysis

Both fields work together to analyze data to derive insights and meaningful conclusions. The data obtained through various means requires processing, regardless of the stated two domains. They need to gather, clean, and organize the data. Both fields further use quantitative methods for predictions and to understand phenomena. The Data Scientists and Statisticians also work with statistical concepts and apply the same to data. 

Model Building

Both fields are concerned with creating and utilizing models for data analysis and information extraction. They develop models of different types, which include machine learning models, regression models, time series models, or clustering algorithms. The models serve the purpose of capturing and representing the dependencies or relationships in data. 

Measure of Uncertainty

They both consider the measure of uncertainty. It indicates the fields keep room for the unknown. 

Presentation of Results

Data science and statistics allow understanding and presenting the result in clear, concise, and summary form. They allow presentation intriguingly for both technical and non-technical audiences. 

Which is Better: Data Science or Statistics?

The ‘better’ among both can be stated based on the context of usage, the specific need of work, and goals. Data science is an interdisciplinary field that primarily concerns big data handling and predictive modeling and focuses on real-world problems. Statistics, on the other hand, offers a combination of mathematics and statistics for inference and testing. Therefore, understanding the following considerations before judging the right choice is crucial. 

Scope of Analysis: Data science is an appropriate choice when it comes to analyzing and extracting insights from large and complex datasets. They allow the usage of advanced computational techniques. Statistics is an appropriate choice if the focus is on experimental design, hypothesis testing, and understanding the relationships within data using statistical methods. 

Industry applications: The industries like healthcare, finance, and technology that deal with predictive modeling and machine learning leverage Data Science, while academics, traditional research disciplines, and social sciences require statistics. 

Skill set: Data scientists require a skill set to deal with big data technologies, programming, and machine learning. Statisticians further focus on statistical theory, mathematical rigor, and experimental design. 

Wrapping Up!

Data science and statistics are important fields that are rapidly evolving. Offering new tools and technologies with user-friendly interfaces to allow easy data handling and interpretation, a career in these domains holds a promising future. Candidates willing to enter the world of computer science and related fields must be clear about the differences and similarities between the two to emphasize their requirements, actions, and results rightly. 

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Frequently Asked Questions 

Q1. Why is it that data science and statistics are frequently confused?

The shared methodology between the two refers to them as a single entity. However, both serve different purposes and industries. 

Q2. Should I be a statistician or a data scientist?

The choice must be taken according to one’s goals, passion, clarity about previous skill set, and the amount of time the candidate is willing to dedicate. Statistics comes laced with a focus on mathematics, while data science is associated with computer-related detailed studies. 

Q3. How are the objectives different between the two fields?

The objective of Data Science is data exploration, pattern recognition, predictive modeling, and extracting actionable insights. Statistics has the objective to draw meaningful conclusions from the data. 

Q4. Can a statistician become a data scientist, and how?

Both fields require each other, and hence, a transition from one career choice to another is possible. Even it gives the candidate an upper hand in such scenarios. 

Q5. Is statistics a subset of data science or vice versa?

Data science incorporates statistics to get results. However, it also requires multiple other disciplines to achieve the goals. Hence, statistics can be considered a subset of data science but not vice versa. 

Q6. Is statistics enough for data science?

No, Data Science expands to a wide spectrum. It is not limited to statistics as big data technology, proper processing and data handling, programming, and other fields are an important part of Data Science. 

Q7. Who earns more: a statistician or a data scientist?

The earnings between the two vary depending on multiple factors. It ranges widely among different industries, experience levels, qualifications, skill requirements, location, and multiple other aspects. 

Data Science & Business Analytics Courses Duration and Fees

Data Science & Business Analytics programs typically range from a few weeks to several months, with fees varying based on program and institution.

Program NameDurationFees
Applied AI & Data Science

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Post Graduate Program in Data Analytics

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Caltech Post Graduate Program in Data Science

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Data Analytics Bootcamp

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Data Scientist11 Months$ 2,000
Data Analyst11 Months$ 2,000