It often gets challenging for students to select a professional domain and a career path after completing graduation and professional qualifications. In most cases, the dilemma further starts impacting the overall lifestyles. Therefore, it is important to know the future scope of the specific domain you opt for in your professional career. The blog clears your doubts about data analyst vs data scientist vs data engineer, including market trends, standard salary trends, skills required, job descriptions, etc.
For better understanding, know the basic terms covered throughout this blog:
- Database: A structured collection of users’ data and information. For instance, a social media app containing your data, preferences, etc.
- Dataset: A numeric data collection in a structured format, mostly in a tabular manner. Users watch time on Instagram, for instance.
- Data Architectures: Maps the source of data and its overall flow for meeting business objectives. Say, analyzing structured & unstructured data.
- Data Warehouse: To store the data at the enterprise level, i.e., in a centralized database. Collection of big data like Amazon, about products, customers’ details, etc.
Who is a Data Engineer?
A data engineer is an IT professional who collects and manages business data to curate relevant information. Additionally, data scientists and data analysts further analyze the generated information for better productivity and efficiency.
Businesses generate data from multiple sources to smoothen the workflow and form strategic decisions. In addition, to turn structured/ unstructured data into useful information, companies need Data Engineers.
Data Engineer Job Description:
A data engineer organizes and analyzes the raw data that can be easily analyzed. They help improve data flow for better decision-making, analyzing customer preferences to provide detailed insights for the company’s strategic choices.
Data Engineering Practical Example
An eCommerce platform in the initial days receives customers’ inputs about purchasing and their preferences. E-commerce brands need data engineers to analyze raw data and predict future sales. Data engineers simplify data for analysis by data scientists and analysts as brands expand or go global and handle more products.
Key Skills for a Data Engineer:
To become an adept IT professional, data engineering training from a top training institute is required. We recommend CodeSquadz, an IT training institute that teaches essential technical frameworks such as Python, Java, React, and Machine Learning. This institute can help you become a renowned Data Engineer by providing comprehensive training in these areas. Here are the skills required for Data Engineers-
- Data Warehousing & ETL
- Programming knowledge Java, SQL, SAS, Python, etc.
- Hadoop-based Analytics
- In-depth knowledge of SQL/ database
- Data architecture & pipelining
- Machine learning concept knowledge
- Scripting, reporting & data visualization
Who is a Data Analyst?
A data analyst is a professional who has expertise in collecting, organizing, and analyzing datasets to generate insightful information for companies.
In addition, these professionals conduct a complete lifecycle analysis that meets the company’s requirements and design. Data analysts also monitor business performances and serve better solutions to enhance efficiency with their analytical & problem-solving skills.
Job Description – Data Analyst
Data engineers and data analysts have a main difference. Analysts study sales and market research data. Engineers, on the other hand, extract data from different sources. Additionally, these professionals filter data and maintain data systems to reorganize data in a readable format.
Data Analyst Practical Example
If you analyze an online clothing store, for instance, that receives thousands of customer orders on a daily basis. The brand requires data analysts to examine previous sales data and customer activities. This analysis will help in making business decisions based on data. Thus, when the brand grows, it requires the technical skills of both data analysts and data engineers.
Key Skills of Data Analyst
Ensure to get the best training under expert guidance to enhance your technical skills to become the best data analyst with the below-mentioned skillsets.
- Scripting: SQL, Python, and R
- Data blending: Informatica, Alteryx, and SAS
- Data visualization: Excel, Tableau, Power BI, D3.js, and ElasticSearch
- Machine Learning: TensorFlow, Keras, PyTorch, and Pandas
- Practical database querying
- Data management and Data cleaning
Note: To learn Python, Data Analytics, Machine Learning, AI, and other frameworks that enhance your technical skills, get yourself enrolled in a top IT training institute, CodeSquadz.
Who is a Data Scientist?
A Data Scientist is an individual with more technical skills as compared to a data engineer and analyst. The professional holds advanced data techniques that include clustering and neural networks for extracting valuable insights from large and complex datasets. Additionally, data science roles for professionals involve predictive modeling, machine learning, and deep learning to drive data-driven solutions for a wide range of business problems.
Job Description of Data Science
A data science engineer plays a vital role in forming data-driven results by leveraging data and inputs from analysts. They apply and develop algorithms, statistical models, and machine learning practices to solve complex business problems. Additionally, after receiving inputs from data analysts and data engineers, they develop actionable business insights.
Data Scientist Practical Example
In the healthcare industry, Data Scientists might work on predicting disease outbreaks based on patient health status and previously collected data. They help hospitals and organizations make smart choices, create new products, and improve processes for better patient care.
To help these companies, start from the beginning and choose courses that teach Data Science to both beginners and experienced individuals.
Key Skills of Data Scientist
A data science master course becomes essential to opt for if you aim to be the top data scientist that tech giants require. Here are the following skills, every professional requires to master-
- Programming skills like Python or R.
- Proficiency in Machine Learning methods like k-Nearest Neighbors, Naive Bayes, and SVM.
- Strong Math Skills, including calculus and algebra
- Data Wrangling
- Deep understanding of data manipulation and data cleaning techniques.
- Experience with big data technologies such as Hadoop and Spark.
- Data visualization skills and proficiency in tools like Tableau or Matplotlib.
- Strong domain knowledge in the industry they work in.
- Excellent problem-solving and communication skills.
Salary Trend for Data Scientist vs Data Analyst vs Data Engineer
As per the statistics, the average salary of Data Engineers in India is Rs. 7 Lakh, whereas Data Analysts make approximately Rs. 6 to Rs. 10 Lakh per annum, and Data Scientists make approximately Rs. 14 Lakh per annum in India. The range might differ according to experience, skills, and companies these professionals work in.
Conclusion
The blog consists of crucial points on the major topic of “data engineer vs data scientist vs data analyst.” It becomes important to learn from scratch if you aim for any of the professional careers.
CodeSquadz is a popular IT training institute. They offer programs for Machine Learning, Artificial Intelligence, Java, Python, and other frameworks. These programs are designed to help you start your career.
