As data science is emerging as an upcoming domain altogether there are various reasons for this growing field and the main cause being the increased digital penetration. Almost all the businesses are going digital, making their awareness felt digitally. When digital communication happens, it generates activity, log, transactions, trace and a long history of data.
As the world entered the era of big data, the need for its storage also grew. It was the main challenge and concern for the enterprise industries until 2010. The main focus was on building framework and solutions to store data. Data science is a multidisciplinary blend of data inference, algorithm development, and technology in order to solve analytically complex problems. At the core is data – troves of raw information, streaming in and stored in enterprise data warehouses, much to learn by mining it. Advanced capabilities we can build with it. Data science is ultimately about using this data in creative ways to generate business value.
All the ideas which you see in Hollywood sci-fi movies can actually turn into reality by Data Science. Data Science is the future of Artificial Intelligence. Therefore, it is very important to understand what is Data Science and how can it add value to your business. Data science is all about uncovering findings from data. Diving in at a granular level to mine and understand complex behaviors, trends, and inferences.
How do data scientists mine out insights? It starts with data exploration. When given a challenging question, data scientists become detectives. They investigate leads and try to understand pattern or characteristics within the data. This requires a big dose of analytical creativity. Then as needed, data scientists may apply quantitative technique in order to get a level deeper – e.g. inferential models, segmentation analysis, time series forecasting, synthetic control experiments, etc. The intent is to scientifically piece together a forensic view of what the data is really saying. This data-driven insight is central to providing strategic guidance. In this sense, data scientists act as consultants, guiding business stakeholders on how to act on findings.
A data science test assesses ability of a candidate to analyze data, extract information, suggest conclusions, and support decision-making. It’s the ideal test for pre-employment screening. Data scientists, data analysts, and statisticians need to be able to extract knowledge and insights from data. This test requires candidates to demonstrate their ability to apply probability and statistics when solving data science problems and to write programs using Python for the same purpose.
With the upsurge of big data collection and the need for analysis, data scientists have become in high demand in a range of companies and industries, small and large. Data science as a profession incorporates a range of skills within mathematics, statistics and computer programming. It is an industry dominated by men; estimates of women in data science are around 10%.
There are certain skillsets/software tools that remain consistent with the desire to being a data scientist:
- Multivariable calculus and linear algebra
- Database querying language such as SQL
- Basic statistics such as statistical tests, distributions, maximum likelihood estimators, and so forth
- Machine learning methods such as k-Nearest neighbors, random forests, ensemble methods, etc.
- Data logging and development of new products that are data-driven
- Familiarity with Hadoop platforming
- Visualization tools such as Flare, HighCharts or AmCharts
- Statistical programming languages, like R and SAS
There are numerous pros and cons of taking up data science. The pros of becoming a data scientist is to know everything and be a master in each of the three circles and the cons of becoming a data scientist is if the person does not know something from the other circle. One example: a person may be a good statistician but if the person lacks good programming mind set and lacks domain knowledge, then it is one of the disadvantages of becoming a data scientist. Knowing everything is a challenge for a data scientist as the information is growing day by day and so are the tools.
Simulation is an approach that is used most commonly in two situations.
The first situation is when uncertainty is high due to sparse data. A second common use of simulation is for experimentation in a low-cost, low-risk environment. Both of these applications of simulation are helpful to scientists and researchers, but they come with a set of advantages and disadvantages. The objective of the database simulators is to provide data representation and its relationship for analysis and testing purposes. Data Modeling was based on the concept of entities & relationships in which the entities are types of information of data, and relationships represent the associations between the entities. The latest concept for data modeling is the object-oriented design in which entities are represented as classes, which are used as templates in computer programming. A class having its name, attributes, constraints, and relationships with objects of other classes.
Compared to the cost of experimenting in the real world, the use of simulation requires very little time and resources. Think about marketing: if we were to run various experiments in which we varied the amount we invest in different channels, we would have to go through dozens of budgets over as many years to gather enough data to answer a question with certainty. In the meantime, our brand and business may have gone in an undesirable direction.
The alternative to real world experimentation is to run simulations to test different marketing plans. Within minutes we can test many ideas before acting on a plan and making decisions in the real world. The downside of this approach is that some audiences today are skeptical of simulation. Most of today’s analysis, especially in marketing, is based on reporting and building deterministic statistical models to describe what has happened in the past. Researchers often prefer these descriptive approaches to methods that test 31 theories about the future.
We believe that this skepticism is a result of the relative novelty of simulation in marketing analytics, and that with more success stories and validated forecasts, this skepticism will subside.