In general terms, some of the facts, sets of information, or details used to plan, organize, and analyze something are called data.
When knowledge is acquired through experiments and observations, it is science. The process by which skills can be acquired for a specific aspect is training.
Summarizing the three terms, we come to a phrase called Data Science Training, which refers to training that can store historical data and accurately predict trends.
WHY IS IT NECESSARY?
As it is a fusion of several areas such as database management, data analysis, predictive modeling, machine learning, distributed computing Big data, coding, data visualization and reporting, it's important.
Business strategies are based on data analysis, not primitive data. Data training is therefore necessary.
HOW TRAINING WORKS
Initially, it is not necessary to analyze. The first step is to clarify the basic statistics, Excel & SQL, software such as SAS, R, Python (used for coding such as the mean and the median). data scientists.
The following steps include knowledge of cleanup, manipulation, 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 the use of Blog data tools by Learn.
Once completed training with coverage of all aspects above, the individual is able to be a computer scientist.
DIFFERENCE BETWEEN BUSINESS INTELLIGENCE, DATA SCIENCE AND WHY DATA SCIENCE !?
Often, the two terms above are used synonymously while there is a difference between Business Intelligence and Data Science.
Business Intelligence is a traditional approach, in which it deals only with two business issues: what has happened? And why did this happen?
However, data science addresses both of these issues with a modern approach to questions such as: what is going to happen now? What should I do about it?
Therefore, from the above details, it could be clearly separated that the two substitutable terms (supposed to be!) Are distinct of their kind!
In addition, the content reveals that the science of data is selected at the expense of the strategic watch, because it is only descriptive and diagnostic, while the previous one is descriptive, diagnostic, predictive as well as normative and pragmatic.
Data Science can be used to plan routes from any business, allowing you to determine how your business will evolve and gain momentum.
Secondly, a predictive analysis can be performed to know that what could be done in the future with reference to various factors.
A company can plan its promotional offers, future demand, the next replenishment lead-time and things like this a long time in advance through a study of their perception through data science.
Finally, it can also be noted that with the help of data science, it becomes very easy to decide and disclose which resources might improve performance and which resources could be used to improve performance.