A 21ST CENTURY MANDATE
Data Science is a self-explanatory term that has been seen, heard and applied in every domain in the recent past. It is the science revolving around the tremendous amounts of data we constantly acquire at a steep rate. It wasn’t a term that could be resounded by most individuals even up till 50 years ago, but it gained momentum and popularity around the early 1990’s right after the boom in personal computer technology by IBM and Apple. Early 2000’s observed research articles and recognition of data science as a scientific disciple. This skyrocketed numerous opportunities of research, development, and application in the field and since has been an upward trend.
WHAT IS DATA SCIENCE?
Data science is referred to as the process of interpreting huge clusters of raw data to give meaningful information from it using statistical and computational methods. Data acquisition can vary from labeled, unlabelled, primary, secondary and so on. All the data is then manipulated or undergoes algorithms that find patterns and commonalities that can lead to its categorization.
Data science is an umbrella term that covers a wide variety of methodologies applied to a set of data to achieve desired results.
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WHY DATA SCIENCE?
As is evident from the graph, technological advancements have seen a steep rise from 1950 onwards. Subsequently, all these advancements have been generating and storing unmanageable amounts of data. Scientists soon realized that this data can be priceless in determining various decisions, leaving a legacy and a memory map for future generations. This meant that all the data needed to be organized for any human to be able to make sense of it. It is said
that necessity is the mother of invention, and such was the case for data science.
LIFE CYCLE OF DATA SCIENCE
A data science process often undergoes similar steps and each step has an unparalleled significance in obtaining the end product satisfactorily.
- Business Understanding
This constitutes understanding and viewing the problem holistically. It requires one to understand the various factors in play and define objectives for the end result.
- Data Mining
This process mostly involves scrapping and obtaining data relevant to the problem at the granular level. It can be achieved in a multitude of ways depending upon the problem to be resolved. For instance, online consumer-based companies like Amazon, Netflix and so on collect data from user preferences and purchase patterns to arrive at more efficient business decisions.
- Data Cleaning
Data cleaning occasionally referred to as a subprocess of data mining is in relation to the removal of inaccurate or corrupt data from the acquired data set which might lead to inaccurate results. Essentially, the outliers of the dataset are omitted to arrive at a consistent data cluster.
- Data Exploration
This process is similar to the initial analysis of the problem but is done via sieving the data often through visual tools. This is crucial for a data scientist to develop an intuition about data and leads to a creative resolution of problems.
- Feature Engineering
Feature engineering is carried out to obtain a well-defined data so as to easy and logical application of algorithms. The feature set needs to be designed for improved efficiency of working models that are later applied to the data at hand.
- Predictive Modelling
Predictive modeling is the application of machine learning models to forecast missing data values or predict values for future data sets. It utilizes probabilistic and statistical models to train and test data to formulate a framework.
Examples of predictive models:
- Data Visualization
The obtained data needs to be client-friendly and so is converted into visual graphs, plots and so on to clearly put across the findings to stakeholders. There are various data visualization tools available to a data scientist:
ROLES AND CONSTITUENTS OF DATA SCIENCE
- Big Data is the term used for ginormous data sets that are continually growing. The complexity of these data sets is extremely high and cannot be resolved using traditional or conventional data management/ manipulation systems or software.
Instances of big data:
- Social media
- Stock exchange
The software employed for big data:
- Machine learning refers to the development of computer programs that can utilize provided data to arrive at conclusive decisions on their own. It enables an analysis of mass data and decreases human involvement and error.
Some major machine learning software tools consist of:
- Scikit learn
- Artificial Intelligence is a synthetic simulation of human intelligence by employing various computer systems and networks. These programs are modeled to function as closely with a human mind as possible so as to completely automate any task or process. There are various types of AI used depending on the task to be achieved.
- Limited memory
- Reactive Machine
- Theory of Mind
They find patterns in big data to learn or reveal information that is not apparent to the human mind.
WHO IS A DATA SCIENTIST?
This video is a day in the life of a data scientist and provides a real and accurate representation of what a data scientist does as part of their jobs.
A Data scientist is a professional responsible for analyzing, cleaning, interpreting and manipulating data to resolve problems and retrieve meaningful information from unstructured data. They often have traditional backgrounds of computer science, mathematics, statistics and so on. Essentially one needs to be able to observe and think uniquely to establish unseen patterns in data. As data sets have grown in size, it has become essential for a data scientist to be comfortable with coding and data science tools such as R, Python and so on. However, this title is oftentimes interchanged with a data analyst. A Data Analyst is responsible for the history of data and explains what is happening but a Data Scientist does the exploratory analysis to discover insights from it.
Conclusively, data science is a vast field with its roots in a majority of sectors in our current functioning society. It has intrinsic levels and approaches that lead to unique solutions, patterns, and similarities. No two data sets are the same and so each data set has infinite possibilities of being interpreted, each revealing a new unknown.