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The integration of data science tools and big data science for effective data analytics

What are big data and data science? Why are they important? These are the hot topics of the current era. There are no universally agreed definitions of big data and data science, but they are highly related to each other. There are also several tools used in data science. Learning them has also become important and increasingly critical. The volume and variety of data is increasing at a faster rate.

 Data has the importance of gold these days. Hence, there is a need to understand the tools that can represent and process this data as well as there is a need to understand the relationship between big data and data science.

 

Tools that employers want data scientists to know

If your goal is to get hired as a data scientist, one way is to study and learn the essential tools. Employers look for employees who have enough knowledge about the latest specific data science tools. If you are a beginner, then it might be a little tough for you. The tools that companies use vary according to their needs. So, look for the job description for the tools needed.

Besides that, the basic tools needed for any data scientist are Python, Hadoop, R, SQL, C++, Java, MATLAB, Ruby, Perl, SAS, Hive, Tableau, Perl, NoSql, AWS, HBase, Spark, PHP, Shark, Redis, JavaScript, Fortran, JMP, Cassandra, Sed and Excel.

 

Data science tools to start with

As you know, there are almost 30 tools a data scientist to know. But it is not possible to learn all of them at the same time. But you have to start somewhere. These tools range from data technologies, statistical programming tools, to scripting languages. Some of the tools are similar while some are way different from each other. So try to learn unique tools and master at least one tool.

The best tools to get started with our SQL, Hadoop, and R. You should also have a good understanding of Python, and the tools used with Hadoop like Pig and Hive. This always gives a good start.

 

Big data and data science

When data science and big data are combined, it gives us big data science, which helps to overcome logistics challenges concerned with enormous data sets.

It is not easy to process and analyze huge data sets. Hence, special tools, techniques, and algorithms are applied with the help of data science. Data analysis is applied to these data sets to create predictive models, or to find trends.

Big data represents both large volumes of structured and unstructured data. Analysis and extraction of meaningful data from big data involve data science. Hence, big data and data science are conceptually related to each other. Data science encompasses various tools and techniques during the lifecycle of big data processing.

 

Resource box

If you want to learn the popular tools used in data science, Data science certification is the correct option for you. The data science certification covers the popular programming and analytical tools along with its use in big data science.

https://datasciencecourseinbngl.blogspot.com/2019/04/history-of-dat...

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