All the Details About Data Science Courses
Table of Contents
What is Data Science Courses
Data Science Courses can be defined as a blend of mathematics, business acumen, tools, algorithms and machine learning techniques, all of which help us in finding out the hidden insights or patterns from raw data which can be of major use in the formation of big business decisions.
In data science, one deals with both structured and unstructured data. The algorithms also involve predictive analytics in them. Thus, data science is all about the present and future. That is, finding out the trends based on historical data which can be useful for present decisions and finding patterns which can be modelled and can be used for predictions to see what things may look like in the future.
Data Science is an amalgamation of Statistics, Tools and Business knowledge. So, it becomes imperative for a Data Scientist to have good knowledge and understanding of these.
My children were there to cheer me when I was racing against time, sweating and trying hard to meet deadlines. They checked my Leaderboard scores and encouraged me not to worry about missing a mark.
Every aspect of the learning was enjoyable, from the quiz to the assignments to the Capstone. It was an amazing experience to solve every problem statement using machine learning models created with the Predictive or Prescriptive methods.
Why to learn Data Science Courses ?
With the amount of data that is being generated and the evolution in the field of Analytics, Data Science has turned out to be a necessity for companies. To make most out of their data, companies from all domains, be it Finance, Marketing, Retail, IT or Bank. All are looking for Data Scientists. This has led to a huge demand for Data Scientists all over the globe. With the kind of salary that a company has to offer and IBM is declaring it as trending job of 21st century, it is a lucrative job for many. This field is such that anyone from any background can make a career as a Data Scientist
Components of Data Science
Data Science consists of 3 parts namely:
Machine Learning: Machine Learning involves algorithms and mathematical models, chiefly employed to make machines learn and prepare them to adapt to everyday advancements. For example, these days, time series forecasting is very much in use in trading and financial systems. In this, based on historical data patterns, the machine can predict the outcomes for the future months or years. This is an application of machine learning.
Big Data: Everyday, humans are producing so much of data in the form of clicks, orders, videos, images, comments, articles, RSS Feeds etc. These data are generally unstructured and is often called as Big Data. Big Data tools and techniques mainly help in converting this unstructured data into a structured form. For example, suppose someone wants to track the prices of different products on e-commerce sites. He/she can access the data of the same products from different websites using Web APIs and RSS Feeds. Then convert them into structured form.
Business Intelligence: Each business has and produces too much data every day. This data when analysed carefully and then presented in visual reports involving graphs, can bring good decision making to life. This can help the management in taking the best decision after carefully delving into patterns and details the reports bring to life.