PowerPoint Presentation

000. DATA ANALYST. DATA ENGINEER. DATA SCIENTIST.

[Audio] Data Analyst: A data analyst is responsible for collecting, processing, and performing exploratory analysis on data. They are often tasked with creating reports and visualizations to communicate insights to stakeholders. They require strong analytical and communication skills, as well as knowledge of data querying and manipulation tools such as SQL and Excel. Key Responsibilities : 1.Data Visualization Tools: Data visualization tools such as Tableau, Power BI, and Google Data Studio are used to create interactive and compelling visualizations that help stakeholders understand complex data. These tools allow you to create interactive dashboards, charts, and graphs that make it easier to communicate insights and trends. 2.Statistical Analysis Tools: Statistical analysis tools such as R and SAS are used to perform advanced statistical analysis on data. These tools are essential for data analysts who need to identify trends, perform hypothesis testing, and develop predictive models. 3.Data Cleaning and Preprocessing Tools: Data cleaning and preprocessing tools such as OpenRefine, Trifacta, and Talend are used to clean and prepare data for analysis. These tools help to identify and remove errors, duplicates, and inconsistencies in data, ensuring that the data is clean and ready for analysis. 4.Cloud Computing Platforms: Cloud computing platforms such as AWS, Google Cloud, and Microsoft Azure are essential for data analysts who need to store, process, and analyze large volumes of data. These platforms provide scalable and cost-effective solutions for data storage, processing, and analysis..

[Audio] Data Engineer: A data engineer is responsible for building and maintaining data infrastructure. They are often tasked with designing and building data pipelines, developing and maintaining databases, and ensuring data quality and integrity. They require strong technical skills, particularly in database design and development, as well as knowledge of programming languages such as Python and Java. Key Responsibilities : Database Management Systems: Database management systems such as MySQL, Oracle, and SQL Server are essential for data engineers. These systems are used to store and manage large volumes of structured data. Data engineers should be familiar with the concepts of database design, data modeling, and data normalization. Big Data Platforms: Big data platforms such as Hadoop, Spark, and Hive are used to store and process large volumes of unstructured data. These platforms provide scalable and distributed solutions for data storage and processing. Data engineers should be familiar with the concepts of distributed computing, parallel processing, and data partitioning. 3.ETL Tools: ETL (Extract, Transform, Load) tools such as Informatica, Talend, and Apache Nifi are used to extract data from various sources, transform it into a format suitable for analysis, and load it into a data warehouse or data lake. These tools are essential for data engineers who need to integrate data from multiple sources. 4.Data Integration Tools: Data integration tools such as Apache Kafka, AWS Glue, and Azure Data Factory are used to integrate data from various sources into a central repository. These tools provide scalable and reliable solutions for data integration, ensuring that data is synchronized and consistent across systems. 5.Data Quality Tools: Data quality tools such as Talend Data Quality, Informatica Data Quality, and Trifacta are used to ensure that data is accurate, complete, and consistent. These tools provide solutions for data profiling, data cleansing, and data enrichment..

[Audio] Data Scientist: A data scientist is responsible for designing and building predictive models to make data-driven decisions. They are often tasked with developing machine learning algorithms, performing advanced statistical analysis, and conducting experiments to test hypotheses. They require strong skills in programming languages such as Python and R, as well as a solid understanding of machine learning algorithms and statistical analysis. Key Responsibilities : 1.Programming Languages: Programming languages such as Python, R, and Java are essential for data scientists. Python and R are widely used for data analysis, machine learning, and statistical modeling. Java is used for big data processing and distributed computing. 2.Data Visualization Tools: Data visualization tools such as Tableau, Power BI, and D3.js are used to create interactive and compelling visualizations that help stakeholders understand complex data. These tools allow you to create interactive dashboards, charts, and graphs that make it easier to communicate insights and trends. 3.Machine Learning Tools: Machine learning tools such as TensorFlow, Scikit-Learn, and PyTorch are used to develop predictive models and algorithms. These tools are essential for data scientists who need to identify patterns and trends in data, and develop predictive models. 4.Statistical Analysis Tools: Statistical analysis tools such as SAS and SPSS are used to perform advanced statistical analysis on data. These tools are essential for data scientists who need to identify trends, perform hypothesis testing, and develop predictive models. 5.Cloud Computing Platforms: Cloud computing platforms such as AWS, Google Cloud, and Microsoft Azure are essential for data scientists who need to store, process, and analyze large volumes of data. These platforms provide scalable and cost-effective solutions for data storage, processing, and analysis. 6.Data Wrangling Tools: Data wrangling tools such as Pandas, Numpy, and Apache Spark are used to clean, preprocess, and manipulate data. These tools help data scientists to prepare data for analysis and modeling..

000. THANKS FOR WATCHING.