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Data Science (10 blogs)

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Last updated on Nov. 16, 2020, 2:55 p.m. 880 Views



He is a tech-expert with 7 years of industrial experience in Python, Data Analysis, Big Data, Machine Learning and NLP. He has 360 degrees of expertise in all these subjects. He is known for his practical approach to different real-time industrial problems. He is known for his great interest in helping students reach their true potential and scale greater heights. Believing in Problem-based teaching pedagogies, he left his job in Malaysia as a data engineer and came back to the newly born state to fill the void between students and the industry.


Last updated on Nov. 16, 2020, 2:55 p.m. 880 Views


The craft of revealing the insights and patterns in data has been around since antiquated times. The antiquated Egyptians utilized statistics data to expand productivity in the collection of tax and they precisely predicted the flooding of the Nile River every year. From that point forward, individuals working in data science have carved an interesting and unique field for the work they do. This field is data science. Through this blog, we will understand the exact definition of data science, the life cycle of data science, and will understand a few facts related to it.

What is Data Science?

Data Science is something that appears to be on people’s minds. Data Science is the talk of the town and because of the popularity, more people are hiring for it. But the question is what makes something to be called data? As per the context of Data Science, the collection of information in a digital form is known as data or digital data.


The above-mentioned series is digital data. But can you interpret this? The straightforward answer is No.

Digital data is not at all interpreted by a common individual; instead, he has to rely on a machine to decipher it in a human understandable manner. The words that you can read on the screen of your PC are an example of this. These digital letters are really a methodical collection of ones and zeros that encodes to pixels in different shades and at a particular density.

Let us now understand the definition of Data Science in a technical manner. Data Science is a combination of different tools, algorithms and machine learning principles with the objective of the discovery of hidden patterns from the raw data. The primary use of Data Science is to make decisions and predictions with the use of predictive basic analytics, predictive and decision science (analytics), and machine learning.

Predictive basic analytics

If you need a model that can foresee the possibilities of a specific event in the future, you have to apply predictive basic analytics. For example, if you are giving cash on credit, at that time the probability of clients making future credit instalments on time involves worry for you. Here, you can assemble a model that can apply predictive basic analytics on the instalment history of the client to foresee if the future instalments will be on schedule or not.

Predictive and decision science (analysis) 

If you need a model that has the insight of taking its own choices and the capacity to alter it with dynamic parameters, you unquestionably need predictive and decision science analysis for it. This moderately new field is tied in with giving counsel. In different terms, it predicts as well as proposes a scope of recommended activities and related results. The best example to be taken for this is Google's self-driving car.  The data assembled by vehicles can be utilized to prepare self-driving cars. You can run calculations on this data to carry insight into it. This will empower your vehicle to take choices like when to turn, which way to take, when to back off or accelerate.

Machine Learning for making expectations

If you have value-based data of a company related to finance and need to fabricate a model to decide the future pattern, at that point machine learning algorithms are the best wagered. This falls under the paradigm of supervised learning. It is known as supervised on the grounds that you as of now have the data dependent on which you can prepare your machines. For instance, a fraud discovery model can be prepared to utilize a verifiable record of fraudulent buys. 

Machine Learning for design revelation

If you don't have the parameters depending on which you can make predictions, at that point you have to discover the hidden designs inside the dataset to have the option to make significant forecasts. This is only the unsupervised learning model as you don't have any predefined names for grouping. The most widely used algorithm used for design revelation is Clustering. Let us take an example; suppose you are working in a phone company and you have to set up a system by placing towers in a locale. At that point, you can utilize the clustering strategy to discover those tower areas which will guarantee that all the clients get ideal signal quality.

Why Data Science?

If we look at the traditional collection of data, we will say that data previously was mostly structured and small in size that is easy to analyze using various Business Intelligence tools. Unlike traditional data, today, most of the data is unstructured and semi-structured and we have a large number of it. Most of the data currently we have is generated from various sources like financial logs, text files, multimedia forms, tools, and sensors. Basic Business Intelligence tools cannot process such a huge amount of unsorted information and a variety of data. We need different complex and advanced tools and algorithms to process, analyze and draw such a giant volume of data and draw meaningful insights out of it. 

In short, because data is getting bigger and bigger! Data was started when you began reading on the internet. How? Let us consider some facts given below-

  • More than 180,860+ minutes of videos are uploaded on YouTube daily
  • More than 4,793,687+ tweets are tweeted daily
  • More than 540,638+ photos are posted on Instagram daily
  • More than 49,804,557+ Facebook posts are liked daily
  • More than 48,318,929+ Google searches are made daily
  • More than 221,881,550+ text messages are sent daily
  • Google uses about 1,000 computers to answer every single search query
  • Every second 60,000 search queries are performed on Google and 1.2 trillion searches per year

That’s insane, right? But that’s just the beginning! The numbers are gradually growing and will increase with time. So, the job of Data Science is to utilize these numbers for good use.

Data Science is not only about trends but also about correlations!!

Most of us are now using trackers. We track our sleep to check whether we are taking quality sleep or not. Also tracking how much exercise has been done in the week. Other than this, steps, walking, running, cycling, swimming, etc. every daily activity is getting tracked. A basic correlation to see this with Data Science would be- the number of days you took more than 3000 steps, your sleep quality was really good. This algorithm is far more than an analysis. Based on this analysis, an action plan is created. This is an example of a real-time scenario of the use of Data Science.

What are the fields of Data Science?

When Data Science is applied to various fields may lead to remarkable new insights and the people related to the field are reaping benefits from it. Data Science has become the ubiquitous term for people working in tech. Let us personify data in every field and list it down here-

  • Manufacturing and Production
  • Agriculture
  • Logistics
  • Politics
  • Automotive, etc.

A bag of skills required to learn Data Science

Regardless of your skills in computer science and IT, the following skills are required to learn Data Science:

  • Programming Skills: R or Python and SQL
  • Statistics: familiarity with statistical tests, distributions, estimations, etc.
  • Machine Learning: Detailed understanding of Python libraries and algorithms
  • Calculus and Linear Algebra
  • Mathematics
  • Logical Reasoning
  • Data Wrangling
  • Data Visualization and Communication
  • Software Engineering skills
  • Data intuition
  • Apache Spark
  • Tableau
  • Apache Hadoop

The life cycle of Data Science

(Image credits – Data Science. Berkeley)

The image above represents a total of 5 stages of the life cycle Data Science.

  1. Capture, (data acquisition, data entry, signal reception, data extraction)
  2. Maintain (data warehousing, data cleansing, data staging, data processing, data architecture)
  3. Process (data mining, clustering/classification, data modelling, data summarization)
  4. Analyze (exploratory/confirmatory, predictive analysis, regression, text mining, qualitative analysis)
  5. Communicate (data reporting, data visualization, business intelligence, decision making)

Fast track into a Data Science career

If you are considering your career in Data Science then let me give you a ray of hope that “the US leads the data science market, requiring 190,000 data scientists by next year.” The skillset of Data Science rewards high salaries to the people who choose this profession. All big and small industries are in great demand for Data Science professionals. Over 4,500 vacant positions are listed on Glassdoor waiting for the professionals with appropriate experience and education in this lucrative career. Do you want to among them? So take a step ahead to make your IT career more effective. Happy learning!

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