Data Analyst vs Data Scientist: Understanding the Key Differences! 

The 21st century has witnessed a boom in the Digital revolution, leading to the increasing prominence of Big Data in the world of business. The increased importance and usage of big data in businesses and organizations has created new job possibilities, including the analysis of enormous data sets and data pools to unearth valuable business information that can aid an organization in succeeding. The two popular professional pathways that have emerged from Big Data are data analysis and data science. Although they may appear to be identical in terms of the tasks they perform and job roles, they are frequently used interchangeably. Although both data scientists and analysts primarily work with data, the methodology and skill set they use to do so are very different.

However, if you are interested in Big Data and dabbling with statistics and want to pursue a career in this arena, you should consider two job paths: data analyst and data scientist. As a result, it is critical to grasp the fundamental differences that distinguish each of these vocations so that you can select the employment role that best matches your technical ability and qualifications.

In the battle between Data Analyst vs. Data Scientist, let us strive to obtain a full grasp of the distinctions each career path brings in terms of job roles, abilities, educational requirements, and more.

Data analyst vs data scientist: Definition

While both data analysts and data scientists  deal with Big data, the key difference lies in how they utilize the data. While data analysts analyze the data to derive meaningful insights, data scientists are tasked with developing algorithms and predictive models that can collect the data for analysis.

What is Data analysis?

Data analytics enables the integration of data science and business analytics. It can be defined as the systematic collection and examination of raw data set to extract valuable information that can be used for business purposes.

What is Data Science?

Heralded as the sexiest job of the 21st century by Harvard Business Review, data science has gained significant traction due to its application in mining, gathering, and cleaning data to prepare it for practical analysis. The outcome of which can impact business success.

Data science can be defined as the process of devising strategies for collecting and processing data for analysis. It encompasses the processing and designing of data models with a combination of statistics, math, programming languages like R, Python, etc., along with artificial intelligence and machine learning, integrating those models into functionalities.

What does a data analyst do?

Data analysts are specialists who extract raw information from unstructured or raw data and evaluate data by identifying trends and patterns using statistical and logical techniques to unearth hidden actionable insights that may be used to address present issues. They will use the data held by the company to create reports and visualizations that will make the data simpler for others to comprehend and apply. A data analyst may also clean up the data by eliminating superfluous or pointless information and figuring out what to do with missing data.

Thus, it can be said that data analysts analyze data to infer answers to questions for which there aren’t yet solutions.

What does a data scientist do?

A Data scientist can be considered as a more advanced version of a data analyst. A data scientist is responsible for inventing new tools and strategies, such as algorithms and predictive modeling approaches, to collect the information an organization needs to tackle difficult challenges and make forecasts about future trends. As a result, data scientists may devote more time to developing tools, automation systems, data frameworks, and data modeling workflows.

To find answers to problems that haven’t yet been conceived of, data scientists deal with vast amounts of raw and organized data using cutting-edge analytical techniques that integrate computer science, predictive analytics, statistics, and machine learning.

Data Analyst Vs. Data Scientist: skills

While there is some overlap in skills in terms of business acumen and skills pertaining to data cleansing and data analysis, each discipline requires a specific set of hard and soft skills to tackle the data. If you are interested in pursuing a career in either of the disciplines, the following are the skill requirements that you need to acquire to embark on a successful career.

The skill requirement for a data analyst includes the following:

• Knowledge of Data querying tools like Excel, SQL, and Python.
• Soft skills –  Written and verbal communication prowess, critical thinking, organizational and analytical skills, business aptitude, etc., are needed for a data analyst.
• Technical skills- Data analysts must have a practical understanding of data frameworks, data mining, and machine learning methods.
• Data visualization tools such as D3, Tableau, and Power BI
• Proficiency in programming languages- It is highly desirable to have a solid understanding of Python, SQL, SAS, CQL, R, and JavaScript in order to analyze and handle data.
• Knowledge of statistics
• Database management systems

To create future forecasts, a data scientist must have highly advanced technical skills to examine data from every feasible viewpoint. A data scientist must have the following skills:

• Advanced level understanding of data mining, machine learning algorithms, AI, statistical modeling techniques, etc.
• Strong background in advanced mathematics, calculus, linear algebra, and statistics and computer science.
• Programming- A data scientist should be proficient in programming languages such as Python, R, Java, SAS, and SQL.
• Cloud computing knowledge- AWS/Microsoft Azure/Google Cloud
• Familiarity with web services and Big Data technologies like Hadoop, Spark, or NoSQL databases

Data analyst vs data scientist: Educational requirements

A data analyst must have a bachelor’s or master’s degree in an area relevant to data analysis, such as mathematics or statistics.

Whereas, a Master’s or PhD in data science, computer science, mathematics, or statistics is required for a data scientist.

However, there are several online certification courses or bootcamps that provide bachelor’s or master’s programs in data analytics or data science for those just getting started in the IT industry. In a short span of time, graduates of these programs can find entry-level employment as data analysts thanks to their intensive and comprehensive training curricula that are in line with industry norms.

Data analyst vs data scientist: Job roles

A data analyst’s or data scientist’s function and responsibilities may differ depending on the industry and sector in which they work. A data analyst’s day may include figuring out how or why something happened. Data scientists, on the other hand, are more concerned with what could or might not happen, and they employ data modeling approaches and big data frameworks like Spark to do this.

Data Analysts:

•   Data querying using SQL.
•   Developing analytical systems to facilitate ad-hoc analysis.
• Interpret data and forecast outcomes by identifying trends and patterns in data.
•   Using business intelligence software to create dashboards.
•   Monitoring business key performance indicators  (KPI)s.
•   Performing quantitative analysis.

Data Scientists:

•   Using APIs for data mining
•   Predictive modeling and algorithm development
•   Utilizing machine learning methods for statistical analysis
• Developing and training machine learning models with tools such as Tensor Flow.
• Creating big data infrastructures with Hadoop and Spark, as well as tools like Pig and Hive.

Data analyst vs data scientist: Conclusion

Data scientist and data analyst are both in-demand job options that you may want to pursue if you are fascinated with data and are skilled in numbers. Your technical aptitude, educational background, professional aspirations, and other considerations might all influence your decision. Finally, it is not wise to state that one is superior to the other because each of the roles serves a distinct purpose and drives corporate growth by giving significant insights that aid in good strategy formulation. Being a data analyst may be enjoyable for you if you are curious about developing your analytical abilities and interpreting data to produce results. However, if you enjoy constructing predictive models and dabbling with machine learning techniques and AI, becoming a data scientist may be a good fit for you.