Data Science: All you need to know

Data Science: All you need to know
on 10 Jan 2017 08:09 AM
  • Rang Technologies
  • Data Science

A multidisciplinary fuse of data inference, algorithm development, and technology to solve analytically complex problems is known as Data science.
Data science is eventually about using this data in creative ways to generate business value.

Data Science is divided into two sub plots.
1.Discovery of data insight which helps quantitative data analysis to help steer strategic business decisions
2.Development of Data product, consists of algorithm solutions in production, opening at scale. For example, recommendation engines.

What is the required skill set to be data scientist?

Based on my understanding, there are three things which makes data science.
1. Expertise in Mathematics
2. Hacking and Technology
3. A Strong Business intellect

1. Mathematics Expertise
At the heart of data mining and building data product is the ability to view the data through a quantitative lens. This consists of textures, correlations and dimensions in data that can be expressed mathematically. Solutions to many business problems involve building analytic models grounded in the hard math, where being able to understand the underlying mechanics of those models is key to success in building them.
There's even a delusion which implies, data science is all about statistics. While statistics is important, it is not the only type of math availed. First, the two branches of statistics - Classical statistics and Bayesian Statistics. When most people talk about stats they are generally referring to classical stats, but knowledge of both types is helpful.
Furthermore, several inferential practices and machine learning algorithms lean on knowledge of Linear Algebra. For example, a popular method to discover hidden characteristics in a data set is SVD (Singular Value Decomposition), which is grounded in matrix math and has much less to do with classical stats. Overall, it is helpful for data scientists to have breadth and depth in their knowledge of mathematics.

2. Technology and Hacking
Let's clarify on first hand we are not talking about hacking as in "breaking into computers". I'm referring to the tech programmer subculture meaning of hacking. Which is, quickness and creativity in using technical skills to build things and find clever solutions to problems.
Why is hacking ability important? Because data scientists operate a technology to wrangle enormous data sets and work with complex algorithms. It requires tools far more chic than Excel. Data scientists should be able to code, prototype quick solutions, as well as assimilate with complex data systems. Core languages associated with data science include SQL, SAS, Python etc. On the border are Java, Julia, Scala and all. But it is not just limited to knowing the language basics. A hacker is more like a samurai, able to creatively navigate their way through technical challenges to make their code work.
This is critical because data scientists operate within a lot of algorithmic complexity. They need to have a strong mental comprehension of high-dimensional data and tricky data control drifts. Have clear cut pieces come together to form a cohesive solution.

3. Strong Business intellect
It is important for a data scientist to be a strategic business consultant. Whilst working so closely with data, data scientists are positioned to learn from data in ways no one else can. This creates responsibility to translate observations to shared knowledge, and contribute to strategy on how to solve core business problems. This means no data-puking rather, present a cohesive narrative of problem and solution. Core competency of data science is using data to cogently tell a story, using data insights as supporting pillars, that lead to guidance.
Having this business acumen is just as important as having acumen for tech and algorithms. Eventually, the value doesn't come from data, math, and tech itself. It comes from leveraging all the above to build valuable capabilities and have strong business influence.

Here are some areas where Data science is applied

Internet search: Search engines make use of data science algorithms to deliver best results for search queries in fraction of seconds.

Digital Advertisements: In this age of digital marketing. Marketing spectrum uses the data science algorithms from display banners to digital billboards. This is the mean reason for digital ads getting higher CTR (Click-through rate) than traditional advertisements.

Recommender systems: The recommender systems not only make it easy to find relevant products from billions of products available but also adds a lot to user experience. Numerous companies use this system to promote their products and suggestions in harmony to the user's demands and relevance of big data. The recommendations are based on the user's previous search results.

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