Data is everywhere. Data is being created by every device on the planet. There are trillions of devices (phones, cars, smart homes, smart watches, appliances, machines, robots, TVs, and IoTs) and billions of people are creating and consuming data every second. The data not only travels through various channels but also being stored in many servers. The amount and the size of data is actually becoming a major problem. Companies also use data to make sense out of it to learn about customers, predict future actions, and much more.
While data is growing, so is the demand of skilled workers who can manage data. To manage and make sense of data, there two most popular jobs that are in high-demand today, i.e., data scientist and data analyst. In this article, we will see the difference between these two job positions and what they do, and what skills are required to become a data analyst or a data scientist.
![difference between Data Scientist and Data Analyst]()
Data Analyst vs Data Scientist
The job of a Data Analyst is to analyze data so business can make sense of it. The data analyst job includes creating reports, charts, and business intelligence solutions to present different stakeholders, business managers, sales and marketing teams and other decision makers. The job includes delivering results with visualization such as tables, charts, and other form of visual UI elements. To build these reports and UI, a data analyst uses one or more visualization tools or BI tools. Some of these popular tools include Power BI, Tableau, Oracle BI, Qlik and many others. Most of the public cloud service providers also offer one or more BI products.
A data scientist however works in a complex solution. Data can't make sense without data models and data cleanup. Once data is cleaned, there needs to be some algorithms applied on the data to make some sense out of it. The data model creation with algorithm collaboration to predict results with building machine learning algorithm is mainly the responsibility of the Data Scientist.
A Data Analyst works mainly with statistical tools, visualization software, and to solve tangible problems from data gleaned from both primary and secondary sources. They spot patterns and trends which may help the organization understand future actions that need to be taken. Data Scientists deal with more advanced and complex data techniques and have the optimized ability to predict certain results from the data. This may include building visuals, visual tools, or dashboards, and even reports.
Both the roles are meant to serve the data in such a way that leads to satisfactory delivery of understanding of data to decide business actions.
How to become a Data Analyst?
To become a Data Analyst, one should be familiar with data and data query language such as SQL. There are several Business Intelligence Tools and solutions are available. One should have hands-on experience with BI tools such as Power BI, Qlik, or online SaaS BI solutions. Understanding of SQL, Excel, basic SQL, R, and Python are also good. Both, R and Python programming languages have libraries and APIs that are used to work with data and machine learning. Having programming experience with R and Python may give an edge. A good understanding of mathematics specifically statistics is also required. Statistics and Linear Algebra are used in modeling and data analysis.
How to become a Data Scientist?
To become a Data Scientist, one must not only know Data Modelling, Machine Learning techniques and algorithms, Hadoop, MySQL, TensorFlow, but also should have advanced Statistics with predictive analysis and the knowledge of object-oriented programming and programming languages such as R or Python. The work may include building Dashboards to deliver results in a structured and visualized way to both organization and clients so that data can be understood very easily.
What skills are required to become a Data Analyst?
Let’s look at a typical job posting of a data analyst position in SQL Server and Power BI field.
- The data analyst is responsible for managing the use of data and provide what is required to help business decisions and business strategy.
- Understand data capture and data analysis
- Data cleanup and formatting, storage
- Create reports and dashboards
- Data definitions, data models, data gathering
- Provide custom data blocks to businesses as needed
- What kind of experience and skills are required to become a data analyst?
- Bachelor's Degree or relatable work experience
- Minimum 5 years database management experience with good hands on experience with SQL and other query languages
- Knowledge of SQL, SQL Server, and SQL Server Management Studio (SSMS).
- Experience working with reporting software Power BI, SQL Server Reporting Services (SSRS), and SQL Server Business Intelligence Development Studio.
- Detailed understanding and experience with PowerPivot and PowerTable
- Hands-on with Microsoft Excel and creating reports and charts
- Strong problem solving and communication skills
- Good learner and a team member
What skills are required to become a Data Scientist?
A data scientist is a senior role with good experience with statistics, mathematics, and data modeling.
Let’s look at a typical job posting of a data analyst position.
Responsibilities:
- Work closely with the Product Manager and Product Owner to translate Business Value needs
- Define data and BI strategies
- Define best practices and standards, working with other Technical Anchors for the Product as well as COE/Operations for the ML tools used.
- Work specifically on the integration team to drive forward Platform integration with other internal and using external systems.
- Grow technical capabilities / expertise and provide guidance to other software engineers on the team.
- Work with architects to make technical decision on tools, integration and other issues.
- Help innovate and iterate on agile processes and share our learnings.
- Work hand to hand with Data Scientists to shape the future vision of our Data Science platform.
Skills required:
- Bachelor's or Master’s Degree in Computer Science, Computer Engineering, or related field
- 4+ years of experience with Machine Learning / Deep Learning Models / Python / Natural Language Processing and MLOps for production environments.
- 3+ years of technical leadership experience within a Machine Learning/Deep Learning function.
- Prior experience in delivering machine learning software products using iterative approach
- Prior experience in supporting continuous improvement by investigating development alternatives
- Prior experience using Machine Learning tools (pytorch, tensorflow, xgboost etc)
- Understanding or desire to learn end to end Machine Learning technology stacks
- Ability to work collaboratively with others and navigate complex decision making.
- Ability to collaborate and communicate well with engineers, designers, and Business Partners.