Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...Full Bio
Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...
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Different Roles In Data Science & Analytics
- Analytics-Enabled jobs
- Data science jobs
- Overview of analytics field,
- Basic domain & functional understanding,
- Communication & presentation skills,
- Energy & passion towards job,
- Logical thinking (structural approach, problem solving approach, attention to details, ability to handle pressure etc)
- Basic knowledge of Math and Statistics
- Data Base & Big Data concepts
- Statistical programming
- Competence with Statistical & Data Visualization tools
- Machine Learning & Deep Learning
- The primary task is to consume historical data from transactional databases; denormalize, flatten, reshape and aggregate the data with the help of tools like SQL/Hive/Pig etc
- Perform some basic statistical analysis
- Build effective and attractive data visualizations (Reports & Dashboards) using visualization tools (Tableau/Power BI)/program to present or communicate the data effectively.
- Preparing dashboards which will be used by senior management and Decision makers.
- These people are not necessarily in possession of strongest technical skills, but they can fill in anyone├?┬ó??s shoes whenever required. This is also a very cross-functional role as you work with data engineers to get the data, data scientists to get statistical analysis done and with business analysts/managers to present the insights.
- The primary role of data analyst/business analyst is compilation and quantitative analysis. They usually have a computer science and business degree. They are tasked with getting out analytical insights of the bulk of data available to the organization. These insights, compiled into decent reports, should make sense to the non-technical counterpart of the company helping them decide their course of action.
- An analyst├?┬ó??s job profile might require basic statistics although it does not usually require advance statistics and has nothing to do with "Big Data" in particular.
- A well-established organization can have multiple analysts with different roles. For example - an operations analyst may look at operational & productivity metrics of different resources and figure out a strategy to improve operations efficiency and communicate the report to the leadership.
- Large enterprises generate huge amounts of data from various sources. The Data Architect is someone who can understand all the sources of data and work out a plan for integrating, centralizing and maintaining all the data.
- Data Architects must be able to understand the relevance of the data in hand with regard to the current operations of the organization and also the how the handling and effectiveness of the data may change with future changes in the organizations work process.
- Data Architect needs to have an end-to-end vision. It is important to understand the translation of a logical design into one or more physical Databases. They also recognize the flow of data through different stages.
- The job role may include things like designing relational databases, developing strategies for data acquisitions, archive recovery, and implementation of a database, cleaning and maintaining the database by removing and deleting old data etc.
- These are competent engineers who know the internals of database software.
- Data engineers are assigned with a myriad of critical tasks like compiling and installing database systems, writing complex queries and scaling them to multiple machines, ensuring backups and putting disaster recovery systems in place.
- Data Engineers are usually required to have a deep knowledge of and expertise in one or more different database software like SQL, NoSQL, and/or Big Data Frame works like Hadoop & Spark etc.
- "Data Scientist" is a topical phenomenon. The general mission of a data scientist is similar to that of an analyst - drawing insights out of data. But once the volume and velocity of the data scales beyond a certain limit, getting effective insights requires a fairly sophisticated skill set.
- A "Data Scientist" usually has many overlapping skills - Database Engineering, handling Big Data systems, knowledge of statistical programming languages, business knowledge and knowledge of statistics or data mining.
- Unlike a traditional analyst who is likely to look at data from a different source a data scientist looks into data from different sources to get better perspective.
- The data scientist will ideally sift through the massive influx of data in order to find the hidden insight that might help the business to strive forward.
- Good data scientists have keen business acumen. They do not just try to solve the problem at hand but also pick up problems the solution of which can help the organization in long run.