What is Data Science? A Beginner's Guide To Data Science

In recent years, the tech space as been serenaded with the buzzword: data science, especially after the Harvard Business Review, in 2012, tagged it "the sexiest job of the 21st century". Institutions and bootcamps alike have introduced courses on data science, often without a coherent curriculum that strictly determines it's a range of knowledge or what differentiates it from other disciplines as are business analysis, predictive modeling, and statistics.

But this cannot be unusual since relatively new fields struggle to find definite coherence. But since the Harvard review, a lot has changed with data science. From who a data scientist is to the duties and responsibilities as well as the knowledge that qualifies a data scientist, there has been massive growth in the field since it's relevance isn't only to the small fishes of the tech world but the giant whales in Google, IBM, Uber and many more tech firms who deals with massive raw data requiring high speed processing.

What is data science, and why is it important?

Picture data science is an interdisciplinary field that blends the practical knowledge of statistics, machine learning, data analyst and related fields in order to identify data, analyze it and decipher new knowledge that is often beneficial to a particular subject of interest, or the subject of disinterest. Data science often unravel unfamiliar patterns to create meaningful and insightful connections that create new paradigms formally unknown.

Why is Data Science important?

Today, the company's are flooded with a colossal amount of data every day, thus the need to know what to do with it and how to use it.  Data science often look for behavioral patterns, through these patterns, inferences are generated, these inferences go on to reshape the landscape of processes helping to save cost, break market jinx, explore new demographics, evaluate the effectiveness of a marketing campaign or launch a new product or service.

Data science also help companies to forecast the future of product and services since consumers leave behavioral blueprints through which charts can be created in line with possible future moves and how the company can be proactively prepared for it.

The lifecycle of a Data Scientist:

There's are 6  phases to the data science procedural cycle.  They go as follow:

Discovery:

As are other scientific fields, data science's first cycle is that of discovery. It could be that of a recurring pattern, or the identification of a problem through which you put forward and hypothesis.

On discovery, various requirements, specifications, and human input necessary to go further in better understanding of how the pattern works are expected to be brought into place.  You must ask cogent questions that open up the pattern further. You must also ensure that all of the resources you have on the ground are sufficient to help you better understand the problem.

Data Preparation:

This is the second phase in the data science life cycle. It involves an analytical sandbox with which you can create an end to end analysis that is sufficient for the entire process. It entails an explorative disposition and a detailed assessment aimed at refining the data before modeling comes to play.

In order to get data into a sandbox, you'll have to perform a one-way-four-stop process, extract, transform, load, and transform with the acronym ETLS.

In the data preparation stage, there are summarily four steps.

• Preparing the analytic Sandbox
• Preforming ETLT
• Data Conditioning
• Surveying and Visualization

Model Planning:

The third stage in the cycle is that of modeling. After all, the essence of refining is to create a workable model to solve your data. Model planning requires that you find an insightful method and technique to find where the variables confluence. 

The relationship you're able to discover will dictate the algorithm you'll need in creating the actual model. Here are tools that data scientists use in model planning:

SQL Analysis Services:

The SQL tool is equipped to perform simple data mining functions and preliminary modeling prediction.

SAS/ACCESS:

The SAS model can be used to access Hadoop data. It is used primarily for creating repeatable and reusable model diagrams.

R:

R posses a complete archive of modeling tools and capabilities, providing a good environment for creating interactive models.

Despite the avalanche of model planning tools in the market, R is the most common owing to its near completeness. Most data scientists also combine their capabilities with that of other modeling tools to create plans that are more concise and easy to go by.

With these tools, you would have your algorithm ready to create the right model, which is next on the data science cycle.

Model Building:

In this phase, you will create a database for training and data testing. You'll consider whether the tools you have at hand is sufficient to create a well-representing the model and if the algorithm you currently have is sufficient or if you need a more robust environment. Common tools like MATLAB,  SPC Modeler, Statistica, and Alpine Miner are very efficient in creating working models even with some degree of complexity.

Operationalize:

This is the fifth stage of the data science cycle. In this stage, you deliver codes, briefings, final reports, and technical documentation. Often times, a model of the actual project is displayed on a small scale to test the functionality of the bigger one and to see if your larger project will work under a similar environment.

Communicating Result:

This is the last and final stage of the data science cycle. It involves sharing information about your findings to stakeholders so everyone in the team can be informed of the project outcomes.

What is a Data Scientist?

Considerably, much of what we have elaborated above is on the field of data science. Becoming a data scientist in itself follows a different cycle. The first phase will be that of mathematical expertise.

Data scientists are known to have strong mathematical footing for the hard maths. And in understanding the underlying principles required to create models, a sound grasp of quantitative techniques that could involve knowledge in algebra, matrixes, calculus, and integration will be needed.

Soundness in the progression of technology and hacking will also be required to find creative solutions to complex problems.

The implementation and marketability of findings cannot be possible without a sound business acumen that is fine-tuned to maximize profit for the greater good of your organization.