{"id":1928,"date":"2019-09-04T03:18:06","date_gmt":"2019-09-04T00:18:06","guid":{"rendered":"http:\/\/kusuaks7\/?p=1533"},"modified":"2024-04-22T10:23:18","modified_gmt":"2024-04-22T10:23:18","slug":"a-laymans-guide-to-data-science-how-to-become-a-good-data-scientist","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/bigdata-cloud\/a-laymans-guide-to-data-science-how-to-become-a-good-data-scientist\/","title":{"rendered":"A Layman\u2019s Guide to Data Science: How to Become a (Good) Data Scientist"},"content":{"rendered":"\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

How simple is Data Science?<\/h1><\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

Sometimes when you hear data scientists shoot a dozen of algorithms while discussing their experiments or go into details of Tensorflow usage you might think that there is no way a layman can master Data Science. Big Data looks like another mystery of the Universe that will be shut up in an ivory tower with a handful of present-day alchemists and magicians. At the same time, you hear about the urgent necessity to become data-driven from everywhere.<\/p>\n

The trick is, we used to have only limited and well-structured data. Now, with the global Internet, we are swimming in the never-ending flows of structured, unstructured and semi-structured data. It gives us more power to understand industrial, commercial or social processes, but at the same time, it requires new tools and technologies.<\/p>\n

Data Science is merely a 21st century extension of mathematics that people have been doing for centuries. In its essence, it is the same skill of using information available to gain insight and improve processes. Whether it\u2019s a small Excel spreadsheet or a 100 million records in a database, the goal is always the same: to find value.\u00a0What makes Data Science different from traditional statistics is that it tries not only to explain values, but to predict future trends.<\/mark><\/p>\n

In other words, we use Data Science for:<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

Data Science is a newly developed blend of machine learning algorithms, statistics, business intelligence, and programming. This blend helps us reveal hidden patterns from the raw data which in turn provides insights in business and manufacturing processes.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

What should a data scientist know?<\/h1>\n<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

To go into Data Science, you need the skills of a business analyst, a statistician, a programmer, and a Machine Learning developer. Luckily, for the first dive into the world of data, you do not need to be an expert in any of these fields. Let\u2019s see what you need and how you can teach yourself the necessary minimum.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t

\n

Business Intelligence<\/h1><\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

When we first look at Data Science and Business Intelligence we see the similarity: they both focus on \u201cdata\u201d to provide favorable outcomes and they both offer reliable decision-support systems. The difference is that while BI works with static and structured data, Data Science can handle high-speed and complex, multi-structured data from a wide variety of data sources. From the practical perspective, BI helps interpret past data for reporting or\u00a0Descriptive Analytics<\/a>\u00a0and Data Science analyzes the past data to make future predictions in\u00a0Predictive Analytics<\/a>\u00a0or\u00a0Prescriptive Analytics<\/a>.<\/p>\n

Theories aside, to start a simple Data Science project, you do not need to be an expert Business Analyst. What you need is to have clear ideas of the following points:<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t