INTRODUCTION:
- In general terms some of the facts, information sets or details used to plan, organize and analyze something is known as data.
- When knowledge is gained through some experiments and observations, it is science. The process by which skills can be learned for a specific aspect is training.
- Summing up all the three terms, we arrive at a phrase named Data Science Training, which means the training which allows one to store historical data and also accurately predict the patterns.
WHY IS IT NEEDED?
- As it’s an amalgamation of several fields like database management, data analytics, predictive modelling, machine learning, big data distributed computing, coding, data visualization and reporting it’s important.
- Business strategies are built upon data analysis and not on primitive data and hence data training is needed.
HOW TRAINING PROCESS STEPS FORWARD?
- Initially there is no need of analysis and thus the first and foremost step includes getting clear with basic statistics, excel & SQL, software such as SAS, R, Python (Used for coding such as mean and median) Hive and Pig for most of the data scientists.
- Further steps are inclusive of having knowledge about data cleaning, data handling, data analysis, predictive knowledge and software such as Hadoop, Tableau, Qlikview, Spark and Spark SQL.
- The final step consists of Machine Learning Techniques, Unstructured Data Analysis Techniques and Learn usage of Blog data tools.
- The training once completed with coverage of all the above aspects, the individual is able to be a data scientist.
DIFFERENCE BETWEEN BUSINESS INTELLIGENCE AND DATA SCIENCE AND WHY DATA SCIENCE!?
- Often, both of the above terms are used synonymously whereas there is a difference between Business Intelligence and Data Science.
- Business Intelligence is a traditional approach, wherein it only addresses two questions of business i.e. What happened? And why did it happen?
- However, data science deals with these two questions along with modern approach towards questions like what will happen now? What should I do in its accordance?
- Therefore, from the above details it could be clearly segregated that both the substitutable terms (believed to be!) are distinct in their own kind!
- Also, the content reveals that data science is selected over Business intelligence because Business Intelligence is only descriptive and diagnostic wherein former is descriptive, diagnostic, predictive as well as prescriptive and pragmatic.
CLOSURE:
- Data Science can be used for route planning of any of your business which embarks how would your business move on and gain momentum.
- Secondly, predictive analysis can be made so as to know that what could be done in the future in reference to diverse factors.
- A business can plan well in advance for the promotional offers, future demand, next re-order time and such stuff about consumers through a study of their perception by way of data science.
- Lastly, it can also be noticed that with the help of data science it really becomes comfortable to decide and disclose what resources could perform better and what resources could be used to perform better.
