Being a data scientist, one can do data analysis in a business, to evaluate what users want, and how the products or services be such crafted to solve customer problems. The inferences can be drawn using complex procedures and algorithms, supported by the analysis done with the data.
- Have a detailed understanding of crucial technologies and tools in data science and analytics such as data mining, artificial intelligence based machine learning, methods of data visualisation, statistics and probability, and predictive modelling.
- Develop decision making ability in different use case scenarios.
- Carry a functional hands-on knowledge related to big data and statistical programming tools.
- Capable of applying data evaluation approaches and quantitative modelling to have interactive findings, and achieving optimum outcomes using data visualisation techniques.
- Realise and assess issues with regards to business ethics dealing with trademark, information safety, honesty, and privacy.
- Apply moral process in everyday corporate tasks with well-reasoned moral and decisions associated with data management.
- Thorough understanding of analytical data procedures into business decision making.
- Evaluating business issues by means of data extraction from data warehouse and then making mining to fix practical concerns.
- Employ tools and technologies like Big Data, R, Python, Business Analytics, Excel, Probability and Statistics.
- Make smart use of algorithms to devise machine intelligence.
- Should become capable of getting involved in team work, lead the team from the front, and become proficient in organisation theory.
- To gain an extensive understanding of how manipulation of data can be done for drawing key conclusions.
- To have an in-depth comprehension of both non-linear and linear with other data classification techniques.
- To secure complete understanding of unsupervised and monitoring models including pipeline, clustering, logistic regression, linear regression, and many more.
- To perform specialised cum scientific computing utilizing SciPy bundle with the likes of Weave, IO, Statistics, and Optimise.
- To make use of Scikit-Learn and NumPy to do mathematical computing.
- To understand elements of the Hadoop community.
- To team up with HBase, its construction, and information storage space, discovering distinction between RDBMS and HBase, using Impala and Hive for partitioning.
- To get through with MapReduce along with characteristics, plus know how to take in information using Flume and Sqoop.
- To expert the ideas of recommendation engine backed by time series modelling to have a complete control over machine learning uses, algorithms, and concepts.
- To analyse data making best use of Tableau to be proficient enough in creating interactive control panels or dashboards.