A closer look at data analytics for data scientists. With a changing landscape in the workforce, many people are either changing their careers or applying to different companies after being laid off.
With a changing landscape in the workforce, many people are either changing their careers or applying to different companies after being laid off. Some of those people are data analysts who want to become data scientists, or some of them are data scientists who are changing companies and will need to focus some of their time on learning or refreshing their data analytics knowledge.
You could assume that data analysis is already taught in data science programs, but in my experience, I have seen an immediate jump to data science, or more specifically, machine learning algorithms. Just like how it is easy to assume now that a data scientist would already know data analytics, so do some universities or online courses that jump right into the meat of common machine learning concepts. This assumption can lead to some data scientists struggling in data analytics. Although it seems that it might be more simple at first, data analytics is the foundation of data science. You must understand your business, your data, and your metrics. This information is ultimately what feeds your statistical methods and data science models.
Below, I will summarize data analytics and data science, and give some examples of why data analytics is so important to data science.
Data analytics is oftentimes referred to as business intelligence, BI development, or product analytics. This field is found at nearly every tech business, and most other businesses as well. It is essential to practice data analytics at a company to ensure visibility of company finances, customer data, and areas for improvement where a future machine learning model could be applied. Data analytics can be found using tools like:
Tableau, Looker, Google Data Studio, SQL, Excel, and sometimes Python.
Examples of data analytics can be:
As you can see from the above examples, all can be applied to the data science process in some way. These examples can also serve as features or attributes that will be inputted into your model.
Data science is becoming more and more popular as a career path for many people to take. It is essentially a career where you automate otherwise manuals processes with the use of programming languages and statistics. Its foundation is based on data analytics and mathematics. Common tools that data scientists can expect to use are:
Data Analysis, SQL, Tableau, Python, R, SAS, Terminal, Jupyter Notebook, AWS, GCP, sklearn and TensorFlow libraries (as well as many more).
There are several parts of the overall process in data science that can include data analysis, such as data formation/creation, data cleansing, exploratory data analysis (especially this part), feature engineering, and interpretation of suggestions/predictions/results.
Examples of data science can be:
Most, if not all, of these examples, are based on data analysis firsthand, as well as after the data science process.
Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
Data science is omnipresent to advanced statistical and machine learning methods. For whatever length of time that there is data to analyse, the need to investigate is obvious.
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