What is Data Science?

By | October 2, 2019

Data Scientist job is well in demand and is termed as “the coolest job” id this era. Nonetheless, data science is a hot and growing field, and it doesn’t take a great deal of sleuthing to find analysts breathlessly prognosticating that over the next 10 years, we’ll need billions and billions more data scientists than we currently have.

What is Data Science?

So, what is Data Science? According to the Venn diagram, data science lies in the intersection of :

  • Hacking skills
  • Math and statistics knowledge
  • Substantive expertise

Data Science is not the synthetic concept to unify statistics, data analysis, and their related methods, but also comprises its results. Data Science intends to analyze and understand actual phenomena with “data”. The aim of data science is to reveal the features or the hidden structure of complicated, natural, human and social phenomenon with data from a different point of view from the established or traditional theory and method. This point of view implies multidimensional, dynamic and flexible ways of thinking.

Data Science Life Cycle

A data science project comprises of following steps as stated:

life_cycle_data-science

  • Capture: The first step in the lifecycle involves acquiring data based on the question to be answered. The project begins with identifying various data sources like – log from web servers, social media data, data from online repositories or data could be present in an excel or can come from any other source. Data acquisition involves acquiring data from all the identified internal and external sources that can help answer the business question. It is important to track where does data slice comes from and whether it is up-to-date or not. It is recommended to track this information during the entire lifecycle of a data science project as data might need to be re-acquired to test other hypotheses.
  • Maintain: Often known as data wrangling process. The data scientists are required to clean and reformat the data by manually editing it in the spreadsheet or by writing code. Through regular data cleaning, data scientists can easily identify what foibles exist in the data acquisition process, what assumptions they could make and what models they could apply to produce results. Data once reformatted can be converted and uploaded into one of the data science tools.
  • Process: This defines the core activity of the project in data science. It requires writing, running and refining the programs to analyze and derive meaningful business insights from data. Such programs are often written in Python, R, MATLAB or Perl. Enormous different machine learning techniques are applied to the data to identify the ML model that best fits the business requirements. All the models are trained with the training data sets.
  • Analyze: After we get the set of data then the analysis process is started, which involves analysis of the random subset of data. This is a brainstorming stage for data analysis as this where the patterns in the data are observed and useful insights are retrieved. There could be a possibility that the dataset could be missing values, or there is some unnecessary data that can be removed. Such inconsistencies have to be identified and removed in this stage.
  • Communication: The goal of this stage requires to deploy the models into production or production-like environment for final user acceptance. The performance of the models must be validated by the user and if there exist any issues with the model then it must be fixed in this stage.

A data science project is an iterative process. Steps are repeated iteratively as data is acquired continuously and understanding becomes much easier.

Where do you Fit in Data Science?

Data is everywhere and expansive. A variety of terms related to mining, cleaning, analyzing and interpreting data are often used interchangeably, but they can actually involve different skill sets and complexity of data

Data Scientist

Data scientists examine which questions need answering and where to find related data. They persist in business acumen and analytical skills as well as the ability to mine, clean, and present data.

Skills required:

  • Programming Skills (SAS, R, Python)
  • Statistical and mathematical skills
  • Storytelling
  • Data visualization
  • Hadoop and SQL
  • Machine Learning

Data Scientist Skills

Data Analyst

Data analysts bridge the gap between data scientists and business analysts. They are provided with the questions that need answering from an organization and then organize and analyze data to find results that align with high-level business strategy. They are responsible for translating technical analysis to qualitative action items and effectively communicating their findings to diverse stakeholders.

Skills required:

  • Programming Skills (SAS, R, Python)
  • Statistical and mathematical skills
  • Data wrangling
  • Data visualization

Data Analyst Skills

Data Engineer

Data engineers manage exponential amounts of rapidly changing data. They focus on the development, deployment, management, and optimization of data pipelines and infrastructure to transform and transfer data to data scientists for querying.

Skills Required:

  • Programming languages (Java or Scala)
  • NoSQL databases (MongoDB)
  • Frameworks(Apache Hadoop)

Why do we need Data Science in Real Life?

Data science or data-driven science enables better decisions, predictive analysis, and pattern discovery. Data-driven science, associates various areas of work in statistics and calculation to translate data with an end goal of decision making.

Why do we need Data Science in Real Life?

The following are some sectors that widely use Data science for the growth of their organization.

  • E-Commerce Price Comparison Websites: These websites are fueled with data that is fetched used APIs and RSS feeds. We can buy the same goods with a variety of price range that these sites have to offer, by comparing price from numerous sellers at a single place. For example, PriceRunner, Junglee, Shopzilla are few of such sites.
  • Internet Searching: All search engines like Google, use data science algorithms to provide accurate outcomes for our searched query in a few seconds.
  • Digital Marketing: Although internet surfing is one of the most significant applications of data science and machine learning, the entire digital advertising sector is it’s another application. Data science algorithms are used to display banners on different websites. For example, if we search for a data science course, we would start getting recommendations and adds on other websites as well as on our Instagram account.
  • Image and Speech Recognition: These can be explained how algorithms actually work in these two areas. For example, if we upload a photo on Facebook we start getting tag suggestions, which is because of image recognition that Facebook offers. Whereas speech recognition is used in our daily lives as well by using our smartphones featuring Siri and Google Assistant as the voice assistants. Following the trend, voice supported speakers and TV are also being introduced in the market.
  • Health Sector: The most benefited industry with the advancement of this technology is the healthcare sector. Data science is used for detecting tumors, artery stenosis and organ description employs various methods and frameworks like Map Reduce to find ideal parameters for tasks such as lung texture sorting.
  • Airline Route Planning: With data science, an airline can optimize their operations in many ways and thus providing a hassle-free experience to its valuable customers. Presently, they can :
  • Plan the routes thus predicting if connecting flights will be needed or direct flights could be scheduled.
  • Predict if any delays of flight are possible.
  • Offers promotional offers by observing the booking patterns of customers.
  • Decide which class of planes to purchase depending on demand.
  • Logistic Delivery: Companies like DHL, FedEx, and UPS use data science algorithms to enhance their operational efficiency. With optimized algorithms, these companies have discovered the best possible way to ship, the most appropriate time to deliver, the best method of transport to pick subsequently thus, prompting cost-effectiveness, etc.
  • Gaming: Gaming skills have advanced to the next level with data science. Games developed these days using machine learning are designed in such a way that it upgrades itself as the user climbs up a higher level in the game. EA Sports, Sony, Zynga are amongst few giants who have taken the gaming experience altogether to a new level.

How does Data Science Differ from Business Intelligence?

Business intelligence comprises of both strategies and technologies used for the analysis of business data or information. It can also provide historical, current and predictive views of business insights. But still, there are some key differences. Unlike data science that uses both structured and unstructured data, business intelligence uses only structured data. Then, business intelligence is analytical in nature that means it provides historical reports of data, in contrast, data science in scientific in nature as it performs in-depth statistical analysis on the data. Further, Data science leverages more sophisticated statistical and predictive analysis and Machine Learning(ML), whereas business intelligence uses basic statistics with an emphasis on visualization. Lastly, business intelligence compares historical data and current data to identify trends, on the other hand, data science combines historical and current data to predict future performance and outcomes.

Data Wrangling: Why is it important in Data Science Life Cycle?

Often termed as data munging, it is a crucial part of data analysis. By dropping null values, filtering and selecting the right data, data scientists can ensure that any algorithm to the refined data is fully accurate and effective.

Therefore, Data wrangling is the process that involves converting and mapping the raw or unstructured data to appropriate data sets making it valuable for the scientist to run algorithms on it and thus predict insights.

The wrangling of data involves six core activities, let’s see them below:

  • Discovering: To discover what the data consists of and figure out what could be the best approach for productive analytical exploration.
  • Structuring: This stage is needed because data comes in all sizes and shapes.
  • Cleaning: This step requires to extract the data that might distort the analysis. For example, removing null values from the data sets.
  • Enriching: This step further enhances and refines the data.
  • Validating: This activity takes care of data quality and consistency issues or verifies that they have been addressed properly by applied transformations.
  • Publishing: It is the final stage and it is responsible for planning and delivering refined data for the projects.

Data Analytics: An overview and how is it different from Data Science?

Data analytics is a technique to examine the data sets and generate insights by connecting and observing patterns in trends to achieve organizational targets.

Unlike data science, it involves dealing with historical data in context, and less in AI, machine learning and predictive modeling. Data Analysts are usually required to wrangle data that either localized or smaller in footprint.

Following is the comparison with data science:

Data Science Data Analytics
Goal Asking business questions and planning strategy. Analyzing and mining business data.
Size of data The broad set of data(Big data) The limited set of data
Involves Data preparation, cleansing, analysis to gain insights. Data querying and aggregation to find trends.
Focus Pre-processed data Processed data
Purpose Finding insights from raw data Finding insights from processed data
Data Types Structured and unstructured data Structured data
Source Data scientist explores and examines data from multiple disconnected sources. Data analysts usually look at the data from a single source.
AI and ML Deals more Deals less
Predictive Analysis More Predictable Insights Less Predictable Insights

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