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Difference Between Data Analyst, Data Scientist, and Data Engineer.pdf
- 1. Difference Between DataAnalyst, Data Scientist, and Data Engineer 1. Role & Primary Focus ● Data Analyst: Focuses on interpreting existing data to find trends and actionable insights. Data Science Course. Their main goal is to answer business questions using data summaries, dashboards, and reports. They support decision-making by transforming raw data into digestible formats for non-technical stakeholders. ● Data Scientist: Focuses on building predictive models and statistical algorithms to forecast future outcomes. They apply advanced techniques such as machine learning, deep learning, and statistical modeling to discover hidden patterns and enable data-driven innovation. ● Data Engineer: Primarily concerned with designing, building, and maintaining the data architecture (data pipelines, databases, data lakes). They ensure that data is available, reliable, and clean for downstream analysts and scientists. 2. Core Responsibilities ● Data Analyst: ○ Analyze historical data to generate reports and dashboards ○ Use BI tools like Power BI or Tableau for visualization ○ Perform basic data cleaning and transformation using SQL or Excel
- 2. ○ Present insightsto stakeholders through data storytelling ● Data Scientist: ○ Develop and deploy machine learning models ○ Conduct exploratory data analysis and feature engineering ○ Handle unstructured data like text, images, and audio ○ Translate business problems into statistical hypotheses ● Data Engineer: ○ Build scalable ETL (Extract, Transform, Load) pipelines ○ Work with distributed systems like Apache Spark, Kafka, or Hadoop ○ Design data warehouses and real-time streaming systems ○ Optimize data storage and processing performance 3. Required Skillsets ● Data Analyst: ○ SQL, Excel, Tableau/Power BI ○ Basic statistics and data cleaning ○ Business acumen and communication skills
- 3. ○ Python orR (basic level, optional) ● Data Scientist: ○ Strong in Python/R, NumPy, Pandas, Scikit-learn ○ Machine learning, deep learning (TensorFlow/PyTorch) ○ Statistical modeling and hypothesis testing ○ Knowledge of NLP, computer vision, or time series (based on domain) ● Data Engineer: ○ Advanced SQL, Python, Java or Scala ○ Big data tools (Apache Spark, Kafka, Hadoop) ○ Data modeling and warehousing (Snowflake, Redshift, BigQuery) ○ Cloud platforms (AWS/GCP/Azure) and DevOps practices 4. Tools & Technologies Used ● Data Analyst: ○ Excel, Google Sheets, SQL ○ Tableau, Power BI, Looker ○ Basic scripting with Python or R for analysis
- 4. ● Data Scientist: ○Jupyter Notebook, Python, R ○ TensorFlow, PyTorch, Scikit-learn ○ Git, MLflow, Docker (for model versioning/deployment) ● Data Engineer: ○ Apache Airflow (workflow automation) ○ Spark, Hadoop, Hive, Kafka ○ Cloud data services like AWS Glue, Azure Data Factory ○ Containerization tools like Docker and Kubernetes 5. End Goals & Business Impact ● Data Analyst: Delivers insights that help business users make informed decisions. Data Science Course in Mumbai. Their work supports marketing, finance, sales, and operations by answering "what happened" and "why it happened." ● Data Scientist: Builds models that can automate decision-making and predict outcomes. Their impact lies in enabling products (e.g., recommendation systems, fraud detection) and strategic planning through data forecasting. ● Data Engineer: Ensures that data flows efficiently and securely throughout the organization. Their
- 5. infrastructure work isfoundational—without clean, accessible data, analysts and scientists cannot function effectively. Business name: ExcelR- Data Science, Data Analytics, Business Analytics Course Training Mumbai Address: 304, 3rd Floor, Pratibha Building. Three Petrol pump, Lal Bahadur Shastri Rd, opposite Manas Tower, Pakhdi, Thane West, Thane, Maharashtra 400602 Phone: 09108238354, Email: enquiry@excelr.com