Why are data analytics training courses so important?

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Advanced analytic capabilities are going to be in high demand, and that's one of the reasons why the roles required to manage and analyse big data are probably going to be split. Although data science and data analysis are different, they have common basic traits. Taking data analytics training courses and learning python for data analysis are a must for whoever wishes to participate in the global trend of dealing with big data. Not for nothing do the words python data science often appear together.
The above-mentioned split between various data jobs is not set in stone; the best and most productive way to deal with data will be of course through integration of these skills. Remember - just because we have the data and the experts to analyse it doesn’t necessarily mean we have the answers we need.

What’s between data science and data analytics?

Data science consists of, among others, statistics, mathematics, programming, the ability to find patterns, and the capability to cleanse, prepare, and present the data.
The work of a data scientist includes both structured and unstructured data; it could be said to be an umbrella term for techniques used when trying to extract insights and information from data.
Data analytics is the task of examining raw data in order to find patterns, drawing conclusions and deriving insights from them, by applying an algorithmic or mechanical process. It’s a huge market; the data analytics market is projected to exceed $200 billion very soon.
The work of a data analyst lies in inference, i.e. to derive conclusions on the basis of what the data analyst already knows.

Required skills:

• For a Data Scientist:
  • Python coding: Python is the most commonly used coding language in data science (along with Java, Perl, C/C++)
  • In-depth knowledge of R and/or SAS, 
  • Knowledge of the Hadoop platform,
  • Knowledge of SQL database: (even though NoSQL and Hadoop are the major focus for data scientists), 
  • Knowing how to work with unstructured data; data from social media, video feeds, audio, or other sources.
    Before we proceed, it would be well to note that almost 90 percent of Data Scientists have a Master’s Degree, and almost half have PhDs. 
     
• For a Data Analyst 
  • Programming skills: Knowing programming languages such as R and python for data analysis is extremely important,
  • Statistical skills and mathematics: Descriptive and inferential statistics and experimental designs,
  • Machine learning skills,
  • The ability to map raw data and convert it into another format for more convenient data consumption,
  • Communication and Data Visualisation skills.

Therefore, with the way of the world as it is, taking python data analytics courses, or any analytics training course or data science course, can open incredible doors to those looking to be active participants in the world of data, machine learning, business intelligence and artificial intelligence. This can be you.

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