What Is Data Science? Who Can Become A Data Scientist?

Definition of Data science:

Data science is actually a field of algorithms, tools, principles of machine learning which help in identifying hidden patterns.

Data science is similar to data mining in a way that it uses both unstructured and structured forms of data.

Data science involves use of processes, algorithms, methods for data extraction and analysis.

Data science involves knowledge of multiple subjects such as statistics, mathematics, computer sciences and information science.

Data Science: Next Generation of AI:

Data science is basically the next generation of artificial intelligence. So, no matter you are a project manager or a cloud geek, you must learn data science now as AI has already taken human jobs in al over the world..

How Data Science Can Benefit Ecommerce Businesses?

Let’s consider a scenario; how, if you are able to understand the precise requirements of individual customers like purchase history, age, income, past browsing history.

Although, you already had this information but now you can train models and then recommend products to customers accordingly with a much more precision.

Also such practices will increase number of retaining customers. Moreover, on basis of user behavior specific products can be reached at home on specific events by Drones.

Data science is revolutionizing the eCommerce industry as well. Ali baba is the greatest example.

How Data Science Can Help You In Decision Making?

Suppose your car has the intelligence to drive you towards home. Self driving cars gathers data from cameras, sensors, lasers, radars and then takes decisions like speeding up or down, overtaking, u turn by making use of advance machine learning algorithms.

How Data Science May Forecast Weather?

Data science is helpful in predictive analysis. Weather forecasting is the greatest example.

Data science helps in creating models for predicting climatic changes.

Models are built by collecting data from satellites, radars, aircrafts and ships.

These models may help you to take measures before occurrence of extreme weather conditions.

Predictive Analysis Using Data Science:

As discussed in above lines, data science helps in predictive analysis for example you may calculate the probability of customers which will pay their payments of credit card on time.

Prescriptive Analysis Using Data Science:

Data science helps not only in predictive analysis but prescriptive analysis as well. It may suggest you the best possible solutions and their possible outcomes.

For example: do you remember the self driving car example discussed above? Self driving cars can be trained and then with the help of algorithms , car will be able to take decisions itself like which path to adopt where to speed up or slowing down, etc.

What if you don’t have parameters for predictions?

Don’t worry, in such case, you will have to analyzed data sets for meaningful predictions. A common algorithm used for pattern discovery in unsupervised model is called clustering.

Data science in Telecommunications:

If you are working in telephone company ad you are required to find best tower locations the you can use clustering algorithm to to ensure users get optimum signal strength.

How Data Science Helped Companies in Making Smarter Decisions?

Viewing patterns are analyzed by Netflix to identify users interest. It then uses it on producing original series.

Gamble uses time series models to preduct future demand and then optimize production levels accordingly.

Why World Needs Data Scientist?

After 2010, it was a growing concern of enterprise industries that data storage limits should also increase. World has already identified  big data needs.

Frameworks like Hadoop has mainly addressed the above issue. World now needs secure processing of data.

I am sure, you have watched science and fiction movies of hollywood. These all wonders can turn into realty by the miracle of data science.

Techniques Used by Data Scientists:

Quantitative techniques are applied by data scientists to get a level deep. These techniques include synthetic control experiments, time series forecasting, segmentation analysis and inferential models.

Data Product:

Another important job role of a data scientist is to help developing data product. It includes making algorithms, testing, qa and then final deployment into the systems.

Examples of these data products are gmail which identifies spam emails itself and take actions.

Core Job of Data Scientist:

The core job of data scientist is basically to identify or discover hidden patterns.

Hence, analytical skills are mandatory for data scientists.

They also guide business stakeholders to act on findings, hence in this way they behave as consultants.

Skill Set for a Data Scientist?

Data scientist job requires a person having following skills set:

• Statistics(both Classical and Bayesian)
• Linear Algebra
• Hacking mindset
• Coding (SQL, Python, R, SAS)
• Business development mindset

Academic Requirement for A data scientist:

Its a quite common misconception about data scientist that a math PhD is required. Reality is Data sciences is a multidisciplinary field.a PhD statistician may even require a lot of programming skills and business experience to fit the job.

It’s a new discipline , universities have not even designed comprehensive programs for it. Hoever, best proven data scientists has software engineering background.

Final Words:

Hence, data sciences is a new field emerged in last few years having a great scope in the world. You need to learn statistics, coding and machine learning to get into data sciences. I would like to quote lines of Michael Hochster, director of data science at Stitch fix as the conclusion:

“Data Scientists are people with some mix of coding and statistical skills who work on making data useful in various ways. In my world, there are two main types:

Type A Data Scientist: The A is for Analysis. This type is primarily concerned with making sense of data or working with it in a fairly static way.The Type A Data Scientist is very similar to a statistician (and may be one) but knows all the practical details of working with data that aren’t taught in the statistics curriculum:  data cleaning, methods for dealing with very large data sets, visualization, deep knowledge of a particular domain, writing well about data, and so on.

The Type A Data Scientist can code well enough to work with data but is not necessarily an expert. The Type A data scientist may be an expert in experimental design, forecasting, modeling, statistical inference, or other things typically taught in statistics departments. Generally speaking though, the work product of a data scientist is not “p-values and confidence intervals” as academic statistics sometimes seems to suggest (and as it sometimes is for traditional statisticians working in the pharmaceutical industry, for example). At Google, Type A Data Scientists are known variously as Statistician, Quantitative Analyst, Decision Support Engineering Analyst, or Data Scientist, and probably a few more.

Type B Data Scientist: The B is for Building. Type B Data Scientists share some statistical background with Type A, but they are also very strong coders and may be trained software engineers.  The Type B Data Scientist is mainly interested in using data “in production.” They build models which interact with users, often serving recommendations (products, people you may know, ads, movies, search results).

At Google, a Type B Data Scientist would typically be called a Software Engineer. Type B Data Scientists may use the term Data Scientist to refer just to themselves, and since the definition of the field is very much in flux, they may be right. But I see the term being used most often in the general way I am proposing here.

This categorization is crude. Many Data Scientists are some mix of A and B. But this answer is long enough already.”

If you still have any question about data science and data scientist basic concept, ask me in comments section.