Data Analytics is the process or procedure of examining and deciphering large units of data with a purpose to pick out patterns, developments, and other insights which can inform commercial enterprise decisions. It includes the usage of tools and techniques such as statistics mining, statistical evaluation, and gadget learning to convert raw data into significant and beneficial data.
Data analytics is used in many special industries and fields, which include finance, healthcare, marketing, and authorities. By studying records, corporations could make informed selections about the entirety from product development and advertising techniques to monetary investments and operational efficiencies.
Overall, data and statistics analytics is an important tool, mechanism for businesses and organizations to gain insights into their operations, customers, and markets for you to make higher decisions and force fulfillment.
Data analytics is a unexpectedly growing area, and there are numerous profession alternatives available to those inquisitive about pursuing this vicinity. Data Analytics profession options and direction can range relying on the industry, agency, and stage of enjoy.
- Data Analyst: A Data Analyst is someone liable for reading statistics, figuring out traits and patterns, and the use of this facts to inform business decisions. Data Analysts require a combination of technical and tender capabilities, as well as understanding of lots of gear and technologies to excel of their position. Continuous getting to know and staying up to date with the latest tools and technology are vital to live applicable inside the subject.
Important Tools and Technologies required – Microsoft Excel, SQL, MySQL, Python or R, Tableau, Power BI, Machine Learning, Hadoop.
- Business Intelligence Analyst: A Business Intelligence Analyst or BI Analyst is responsible for designing and developing reports, dashboards, and other tools that help businesses make informed decisions.
Important Tools and Technologies required – Microsoft Excel, SQL,
– BI reporting tools: Tableau, Power BI,
– ETL Tools: Talend, Informatica or Microsoft SSIS,
– Data warehousing: Knowledge of data warehousing concepts and technologies, such as Snowflake, Amazon Redshift, or Google BigQuery,
– Predictive analytics: BI Analysts need to have knowledge of statistical and predictive analytics tools and algorithms to build forecasting models and predict future outcomes.
- Data Scientist: A Data Scientist is responsible for developing statistical models, analyzing data, and building predictive algorithms to inform business decisions.
Important Tools and Technologies required –
– Programming languages: Such as Python, R, and SQL
– Machine learning frameworks: Such as Scikit-learn, TensorFlow, and Keras
– Data visualization tools: Such as Tableau, Power BI, and QlikView
– Big data technologies: Such as Hadoop, Spark, and Kafka
– Cloud computing platforms: Such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP)
– Natural language processing tools: NLP tools such as NLTK, SpaCy, and Gensim
– Statistical analysis tools: Such as SAS, Stata, and SPSS
– Data storage and management tools: Such as MongoDB, MySQL, and PostgreSQL
- Data Engineer: A Data Engineer is responsible for building and maintaining the infrastructure that supports data analytics, including data pipelines, databases, and data warehouses.
Important Tools and Technologies required – Apache Nifi, Apache Kafka, Talend, Apache Hadoop, Apache Spark, Apache Flink, AWS, Microsoft Azure, Amazon S3, HDFS, Apache Cassandra, ER/Studio, ERwin, SQL, NoSQL, MySQL, PostgreSQL, Oracle, MongoDB, ETL tools, Data governance and quality tools.
- Data Architect: A Data Architect is responsible for designing and building the architecture that supports data analytics, including data models, data structures, and data flow diagrams.
Important Tools and Technologies required – Data modeling, management, integration tools, Enterprise architecture tools, Big data technologies, Data virtualization etc.
- Machine Learning Engineer: A Machine Learning Engineer is responsible for designing and building machine learning models and algorithms that help businesses make informed decisions.
Important Tools and Technologies required – Python, R, Java, Machine learning frameworks, Big data technologies, Data preparation tools, Model training & deployment tools, Deep learning tools, Data visualization tools.
- Data Visualization Specialist: A Data Visualization Specialist is responsible for creating compelling visualizations of data, including charts, graphs, and infographics, that help businesses understand and act on insights.
Important Tools and Technologies required – Visualization libraries and softwares, BI tools, Geographic information system (GIS) software, Data storage and retrieval technologies, User interface and user experience (UI/UX) design tools.
|Note : We will cover Skills, Tools and Technologies required for each of these career options in detail in our upcoming articles.|
These are just a few examples of the many career options available in data analytics. As businesses continue to collect more and more data, there is a growing need for skilled professionals who can analyze and interpret this data to inform business decisions. We will also cover other career options and career paths in our upcoming posts.
Happy Reading 🙂