Data is a ubiquitous resource, and with the right skills, it can be used to predict and solve complex problems. Data analysts and data scientists can be suitable career paths for individuals who are interested in working with data. If data is merely collected but no insight is provided from that data, it is a waste of time. This concept draws the line between a data scientist and a data analyst.
The world is becoming increasingly reliant on data, and that’s a great sign for anyone interested in a data-driven career. According to predictions by the U.S. Bureau of Labour Statistics, there is an expected 36% growth in data occupations between 2021 and 2031.
The World Economic Forum’s Future of Jobs Report 2023 highlighted data analysts and data scientists as highly in-demand, high-paying jobs alongside AI, machine learning, and digital transformation specialists. While both roles work with data, it appears to many that the two professions mean the same thing.
Although their differences may not always be clear, data analysts and data scientists are two distinct career paths in big data. A data scientist is not just an exaggerated term for a data analyst. In this post, you’ll find the differences between the two professions.
What is Data Science?
Data science is a multidisciplinary approach that combines principles from mathematics, statistics, artificial intelligence, and computer engineering to analyze large amounts of data. It helps data scientists understand what happened, why it happened, what will happen, and what can be done with the results. Data science is crucial as modern organizations are inundated with data, including text, audio, video, and image data. It focuses on data design, collection, and analysis, but it took another decade for it to be used outside academia.
What is Data Analysis?
Data analysis is the process of cleaning, changing, and processing raw data to extract actionable information for businesses to make informed decisions. It helps reduce risks by providing useful insights and statistics, often presented in charts, images, tables, and graphs. Data analysis is essential in evaluating past or future events and making decisions based on that analysis.
The five distinct phases of data analysis include:
- Data identificationdata you want to analyze
- Collect the data
- Clean the data in preparation for analysis
- Analyze the data
- Interpret the results of the analysis
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Who is a Data Scientist?
A data scientist is a professional who uses data to understand and explain the phenomena around them, and help organizations make better decisions. A data scientist is responsible for collecting, analyzing and interpreting data to help drive decision-making in an organization.
Where Does a Data Scientist Work?
A data scientist can work in a range of organizations, including
- tech startups
- government agencies
- large companies
- research institutions.
Job Description of a Data Scientist
Data scientists create algorithms and predictive models for data analysts, aiding in data collection, sorting, and analysis. They develop tools, automation systems, and data frameworks for organizations. Data scientists often overlap with data analysts, gaining insights from data analysis.
Responsibilities of a Data Scientist
- Mining data with APIs or ETL pipelines
- Cleaning data with programming languages such as Python and R
- Performing statistical analysis
- Creating programming and automation techniques to simplify day-to-day processes
- Developing data infrastructures
7 Essential Skills for a Data Scientist
Essential skills for data scientists include
- Programming.
- Statistics and probability
- Data wrangling and database management
- Machine learning and deep learning
- Data visualization
- Cloud computing
- Interpersonal skills.
Responsibilities of a Data Analyst
- Data querying with SQL
- Data analysis and forecasting with Excel
- Creating dashboards with business intelligence software
- Performing various types of analytics (descriptive, diagnostic, predictive or prescriptive)
Skills for Data Analysts
Essential data analyst skills include:
- Data Cleaning and Preparation: Removing errors, identifying outliers, and transforming data for analysis.
- Data Analysis and Exploration: Using statistical methods to test hypotheses, identify trends, and make predictions.
- Statistical Analysis: Understanding statistical tests and tools like mean, median, variance, standard deviation, correlation, regression, and hypothesis testing.
- Programming: Knowledge of programming languages like Python and R.
- Database Management: Ability to query databases using SQL for data extraction.
- Creating Dashboards and Reports: Using tools like Tableau, Power BI, and Microsoft Word for interactive dashboards and reports.
- Data Visualization: Using tools like Tableau, Power BI, Matplotlib, Seaborn, or ggplot2 for easy data representation.
- Machine Learning: Proficient in machine learning to automate tasks, identify patterns, and make predictions
- Excel: Widely used for data analysis and visualization.
- Critical Thinking: Approaching problems logically and making informed decisions based on data.
- Attention to Detail: Ensuring accuracy in data analysis and recognizing anomalies or errors.
- Communication: Clear conveying findings to non-technical stakeholders.
- Problem-Solving: Creating solutions to business problems using data-driven approaches.
- Teamwork: Collaborating with other departments or teams.
- Ethical Judgement: Recognizing the ethical implications of data usage, storage, and analysis.
When to Hire Data Analyst
If you have a specific question that you’re going to drive your business off of, hire a data analyst. For example, did we get more users today than yesterday? If you’re getting users, your business is growing and that informs whether you want to raise money, increase pricing, and many other decisions
When to Hire a Data Scientist
If you need in-depth analyses of market and industry trends, consider hiring a data scientist. As seasoned data wranglers, data scientists are analytical experts who use their social science and technology skills to analyze, model, transform, and manage large sets of unstructured and structured data.
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7 Best Tools for Data Scientist
- Tableau
- Python
- Hive
- Impala
- PySpark
- Excel
- Hadoop
7 Best Tools for Data Analysts
- SQL
- Microsoft Excel
- SPSS and VBA
- Jupyter Notebooks
- R
- Tableau
- SAS
Data Scientist Versus Data Analyst: Who Earns More Salary?
According to LinkedIn, Data scientists earn more because they usually have a higher education background and have more responsibilities when it comes to front-end development.
A Data Scientist in the United States earns nearly $100,000 per annum compared to Data Analysts who earn $70,000 per annum
Data Scientist Versus Data Analyst: What is the Difference?
One of the major differences between data analysts and data scientists lies in what they do with data. Whereas a data analyst typically work with structured data to solve tangible business problems using tools listed below, a data scientist with with the unknown by using more advanced data techniques to make predictions about the future.
A data analyst analyzes existing data, while data scientists create new ways of capturing and analyzing data for analysts to utilize
How to Become a Data Analyst
A data analyst’s qualifications include a bachelor’s degree in statistical and analytical fields, with a preference for students from mathematics, statistics, computer science, or economics. Postgraduate courses like Data Analytics Bootcamp can make industry-ready professionals.
How to Become a Data Scientist
The educational requirement to becoming a data scientist is at least a bachelor’s degree in fields like mathematics, statistics, computer science, or finance. In addition, a master’s or doctoral degree in data science information technology, mathematics, or statistics, is required.
Data Analyst vs. Data Scientist: Job Outlook
According to the BLS, data analysts can look forward to a 23% growth in demand, and data scientists can expect a projected 35% increase in demand from 2022 to 2032. This is much faster than the average for all occupations.
