We have entered an era where there’s a need for large storage. In fact, storage need was one of the most challenging problems from enterprises who had to keep long records of their customers and sales. In 2010, people in the respective field started working for a framework or rather a solution to store big data in one place. After developing frameworks that could store large data, the main problem that rose was the processing and shifting of the data.
Due to the evolvement in the Internet of Things(1), 90% of data science’s framework was developed in today’s’ era(2). Every day, more than 2.5 quintillion bytes of data are generated, processed and stored, all thanks to data science. This data can vary from enterprise to enterprise. It includes data storage in shopping malls to posts in social media platforms. Generally. This data is known as big data.
TABLE OF CONTENT
- Introduction
- Data Science Definition
- History
- Importance
- Why choose data Science
- How to get into data science
- LifeCycle
- Process
- Tools
- Data science for business
- Benefits
- Challenges
- Data science vs data analytics
- Data science vs machine learning
- Data science vs software engineering
- Big data vs data science
- Future
- Trends
- Resources
- Conclusion
What Is Data Science?
For skilled computer scientists or professionals, this might be nothing more than a demanding career path. However, data science is an interdisciplinary field that refers to using algorithms, systems and mathematical equations to gain data, insights and knowledge from unstructured as well as structured data. To understand the natural phenomenon, the professionals combine machine learning, data analysis and statistics altogether.
History of Data Science
Data science holds a valuable place in history. However, it was not that much of a broad term as it is now. From ancient Greeks to Egyptian hieroglyphs, there were many professionals in history with tasks of compiling data or written records into one place. However, when the world progressed, we saw statisticians compiling data. They fall right under the category of data science. According to Forbes, data science has been helping enterprises and businesses to record and store data since the early 1940s.
Why Is Data Science Important?
In the past, the data that the enterprises had to use was smaller in size and mostly structured. Traditional data could be analyzed easily through BI tools. However, data of today’s enterprises is unstructured and larger in size. BI tools lack the capability to process huge volumes of data usually found in sensors, financial logs, forums and etc.
Therefore, we need advanced and complex analytical tools, processes and algorithms to draw meaningful insights out of the unstructured data.
Why Choose Data Science?
According to the annual Harvard University business review, a data scientist is considered to be the topmost profession(4) in today’s world. In fact, data scientists are among the most paid professionals of the century. So, what makes data science so important to be taken as a career path? Why is it important to learn in this century? It is not a hidden fact that a job is one of the most sought-after jobs in the current market.
Let’s not waste any time and see why it is better to opt for this profession. As we will go in the flow, we would also discuss the current requirements of data scientists that the large firms need to boost their performances.
In actuality, data science for business means an exponential increase in big data and data mining. It is the only fuel that is revolutionizing thousands of industries and putting them in toughest competitions. So, many enterprises need professionals who are proficient in understanding the current traits and trends of data while analyzing, managing and handling it in the best way possible.
Here are some reasons to choose data science as your career path:
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A Fuel Of 21st Century
We live in the 21st century and at this stage, data science revolutionizes the industries. Even the mobile and electronics industry is using big data techniques in order to make their products safe for use. The purpose behind using big data is to invent powerful high-end performing machines.
Every industry is in dire need of data analysis so that it can boost up its performance and sales. In order to do this, the owners need a team of skilled data scientists who could analyze data and understand the fluctuating patterns of consumer purchases.
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Problems of Demand and Supply
Every industry has huge voluminous unstructured or semi-structured data. However, there are not abundant of resources to convert useful insights for creating products. Moreover, there are not many people who possess the skills to understand and analyze data. Therefore, there is a shortage of data scientists in the market. In fact, the literacy rate is very low. So, in order to fill this void and gap, you need to choose data science.
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A Lucrative Career
Glassdoor states that a typical data scientist makes about 163% more than an average American’s national salary. Therefore, it is a very promising career path that would result in big income bubble.
A data scientist has command over machine language, mathematics and statistics. The learning curve is deep and steep. That is why, the value of data scientists in the market is quite high. All of the company’s processes are dependent on the data-driven approaches and decisions of a data scientist. So, to boost up their sales, every single industry requires a team of data scientists. This allows you to work in the most favorable industry of your choice.
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Data Science Makes World A Better Place
Data science for business is an intellectual concept. Organizations and enterprises are making good use of big data to create useful products. For instance, data can help the physicians to have better insights about their patient’s health.
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Data Science Is the Career of Tomorrow
Every industrialist knows that entering in this field means securing your financial position in the future. It is basically a career of tomorrow. As industries are moving towards automation, data-driven products are being introduced in the market. Therefore, industries might need data scientists for the long-term to help them make better data-driven decisions. A job of a data scientist is only limited to drawing insights from useful data. However, this skill would help that company to grow and prosper.
How to Get into Data Science?
Data is a valuable asset to every company and regarded as the most expensive one. You can get into data science through a variety of ways such as by acquiring skills for data mining, analyzing, cleaning and interpreting.
However, here are some sections in a vast inter-disciplinary field that you can choose to get into it.
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As a Data Scientist
The job of the data scientists is to find relevant, company-related or sales-related data. Not only do they have business skills but also know how to clean, mine, structure and present data. All the businesses need a team of data scientists to handle, analyze and manage voluminous unstructured data. The results derived by the scientists are then analyzed and used in making data-driven decisions.
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As a Data Analyst
Data analysts basically bridge the gap that is commonly between the company’s business analysts and data scientists. They are just provided with the queries that need data-driven answers. The organization then uses those answers to make a data-driven business strategy. A data analyst is not only responsible for communicating their findings to board officials but also to turn analyzed results into doable qualitative call-to-action items.
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As a Data Engineer
Data engineers are mainly responsible for handling and managing the rapidly or exponentially changing data over time. Their main focus is to optimize data pipelines, deploy, manage and transfer data so that it could go to a data scientist or a data analyst.
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Data Science Life Cycle
Here are the main points:
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Discovery
Before beginning any research project, it is important to acknowledge project requirements, budget, and specifications. As a data scientist, you must have the capability to ask and prioritize the right queries and questions. Here, you are just required to assess the given workforce, budget, time and technology. Moreover, you might also need to form an IH, known as initial hypotheses and put it to test.
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Data Preparation
In the second phase, you need advanced analytical tools, (not just IB tools) or a sandbox to perform an overall analysis for the project. For that, you need to model your data for pre-processing. In the end, you would extract, upload and transform the data right into the sandbox.
R language could help you to mine, cleanse and transform data. R provides an outline so that you can build a relationship between two variables easily. Once the data is clean and ready to be processed, move on to the third phase.
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Model Planning
You have not come up with the tactics and methods to state a relationship between two variables. These relationships are necessary to set the base for algorithms that you are going to build in the next phase.
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Model Building
This phase is entirely allocated to use datasets for testing purposes. You need to consider some testing to ensure that the tools being used are enough for running the methods. For making the performance and methods more robust, you need to analyze learning techniques such as clustering, association, and classification.
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Operationalize
After building the model, you need to submit the technical reports, codes, reports, briefings and etc. all the structured data would help you to have a certain view about the performance on a very small level.
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Communicate Results
The last phase determines whether you were able to achieve your goal or not. This phase is to communicate all the results, key findings and methods to the stakeholders. The results would determine whether the project is a failure or success.
Data Science Processes
There are 5 major processes for creating models with the help of machine learning language and data mining techniques. Every process is two-way because they can always loopback. We will discuss the processes briefly before moving to the data science tools:
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Goals
Identifying opportunities and goals is the first step towards a data-driven result. To begin with, you need to create a hypothesis and test it.
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Acquire
The second step is to hunt the data, acquire it and then prepare it for building the model.
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Build
After that, you need to explore the ways in which you could build the model. Select the best modeling method.
Use certain datasets to test and validate. After that, you can find ways to improve it.
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Optimize
Monitor the processed data, analyze it and improve for best findings.
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Deliver
In the last phase, you have to deliver meaningful insights that you have gained from your findings. This would help the stakeholders to make data-driven business strategies.
Data Science Tools
A data scientist has a tool sandbox to perform his job. Let’s look at some of his tools:
Computer or programming language plays an essential role in this field. So, a data scientist must be proficient in modern languages such as python, R-language, Scala, Java, Julia and etc. Usually, it is not necessary to have commands on all of these languages but having command on SQL, python and R language is very crucial.
For statistical calculations, the scientists use libraries and pre-existing software whenever possible. Some of the basic software and libraries that these scientists use are Numpy, Pandas, Shiny, D3 and ggplot2.
For reporting and research, they usually use frameworks such as Jupyter, R markdown, Knitr, and iPython. There are some data science associated tools as well that the scientist use. They are Presto, Pig, Drill, Spark, Hadoop and etc.
Moreover, experts also know how to handle database management and handling systems.
Data Science for Business
A data science expert needs to be a business consultant as well. As they work with data, they learn so much from data that no one else can. This creates an opportunity for the scientists to contribute to making the best business strategies by sharing knowledge and useful insights. Data insights are nothing but supportive pillars that allow scientists to present results in the form of solutions.
Benefits of Data Science
Here are some benefits of data science and deliverables:
- Data science is used to predict the values based on datasets and inputs.
- It can be used for grouping and pattern detections.
- It helps us to identify fraud or anomaly detection.
- It allows facial, video, image, audio and text recognition.
- It helps to improve the FICO score.
- It can also benefit marketing based entirely on demographics.
- It helps us to track sales, revenue and optimization.
Data Science Challenges
Despite huge investments, many companies are not able to get meaningful insights from their data. The chaotic environment is the main reason why the enterprise has to face data science challenges. Some of the challenges are:
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The inefficiency of Data Science Experts
As the data science, experts need to access data with the permission of IT administration, they have to wait for very long before they can start working properly. Other challenges can also affect the scientists’ efficiency such as language conversion.
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No Access to Usable Machine Learning Models
Some of the machine learning models cannot be deployed or recoded in the applications. That is why all the work becomes the responsibility of the application developer.
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IT Administrators Spend More Time on Support
A team of data scientists in the marketing department might not be using the same tools as the team in finance is using. So, it takes a lot of time for the IT administrators to provide support to the data scientists.
Data Science Vs. Data Analytics
Is data analytics the same thing as data science? Well, it all depends upon the context. An expert usually uses raw or unstructured data to build anticipated algorithms. This falls under the category of analytics. Simultaneously, interpretation of already-built reports by a non-technical business user is not considered as data science. Data analytics is a very broad term as compared to data science.
Data Science Vs. Machine Learning
Even though the term, ‘machine learning’ is deeply associated with data science, they slightly differ. The machine learning techniques use toolbox to solve open-minded problems but there are other methods in this category as well, that does not fit in the broad category of machine learning.
Data Science Vs. Software Engineering
Software engineering focuses on developing features, applications, and functions for the end-users. Whereas, data science is only concerned with the process of mining, gathering, analyzing and testing unstructured and structured data.
Big Data Vs. Data Science
Big data is a very broad term. It basically comprises of everything such as data mining, data munging, data cleansing and etc. moreover, big data is a collection of valuable data that cannot be stored. Whereas, data science is concerned with predictive analysis, deep learning, statistics and getting meaningful insights from data.
The Future of Data Science
It is expected that market value for data science would continue to rise. Every company, related to the algorithm, technology, artificial intelligence, pattern recognition and deep learning would provide jobs. However, to take advantage of this, you can enroll in a data science career path Bootcamp and learn all its basics.
Data Science Trends
- Data science automation such as auto-data cleaning and feature engineering.
- Data security and privacy are becoming important day by day.
- Cloud computing in data science allows anybody to access and store large-sized data with limitless processing power.
- After deep learning, natural language learning and processing is making its way in data science.
Resources
There are many resources to learn the basics of data science. Two of them are:
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Data Science for Business Pdf
Companies are refining services and products by using data science. For instance, the data collected from the support service center or call center is collected and then sent to the data scientist and data analysts to get valuable insights as results. Moreover, logistics are collecting data related to weather and traffic patterns to optimize delivery speed.
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Data Science Podcasts
Data science podcasts focus on trends and news from data science. Topics such as artificial intelligence, natural language processing, and biasing data are some of the hottest topics of data science podcasts.
Conclusion
Data science creates a significant impact on an enterprise’s ability to achieve business goals. Not matter if those goals are strategic, operational or financial, data science can reveal great discoveries through useful and meaningful data insights.
