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A Beginner’s Guide to Data Science


By NIIT Editorial

Published on 25/08/2020

8 minutes

The science of dealing with data is data science. But surely its uses and applications are more complicated than that. Data science is responsible for the advent and modernization of Artificial Intelligence. It has given birth to new job roles with new business cases being discovered regularly. This guide on data science from scratch will be beneficial for all those who are interested in making a living for themselves in this field. We’ll be tackling the following questions

  • What is Data Science?
  • The need for data science
  • Life Cycle of Data Science
  • Applications of Data Science

Let’s get to it.

What is Data Science?

Data Science makes it possible for you to look at a subset of data and draw insights from it. Many people intermix the term data science with business intelligence but that is not correct. Business intelligence is used for reporting strictly the past performance based on numbers. It does not let you make future projections. And that is where data science comes in. Data science is the domain that can not only process data to show a fact-based picture of the past but also predict the future. Artificial Intelligence and Machine Learning algorithms are used for predictive modeling. Data science brings in the use of mathematics and statistics. Along with this, you also need specialized domain expertise for your respective industry.

 

Importance of Data Science

Data on its own is worth nothing. It has to be analysed to be made sense of and used to the business’s advantage. There was a time when organizations were facing challenges to store Big Data. With solutions like Hadoop, that has been resolved and the race is on to understand data and predict the future.  Various processes come together to give data science its shape and size with some of them being business analytics, data analytics, data mining, and visualization, and forecasting.

Due to the lack of technological penetration earlier most of the data that businesses collected was structured. But with each passing day, people started using more and more unstructured data. Due to this, the introduction of more complex tools was needed and that is what Data Science has aided in.

 

Applications of Data Science

Data science is being used in the following industries:

Finance

Financial organizations collect a good volume of structured and unstructured data on each customer given the governmental stance to impose KYC (Know Your Customer) and AML (Anti Money Laundering) guidelines. This became one of the first industries to employ data scientists to mitigate instances of bad debts, default payments, credit card closures, etc.

Healthcare

Health Information Technology (Health IT) is fast turning to data science solutions that can aid doctors in diagnosing ailments without the need for expensive tests. Algorithms are trained by processing thousands and millions of images in the database and helping doctors prescribe patients with accurate treatment.

Ecommerce

Various factors affect the purchase decision on an eCommerce store. Unlike in retail where shoppers could be swayed by hard-selling tactics, eCommerce owners have to devise new ways to understand the online expression. This includes analyzing the slightest of cursor movements to the purchase history, the time of the order, the device used, and the product itself. Data Science is used to configure everything you see on leading eCommerce stores.

Internet Businesses  

Search Engines, for one, have to consider multiple parameters before displaying the results for a user query. Moreover, there are multiple formats in which the data has to be presented, including textual, video, audio, and images. Without Data Science, you wouldn’t probably have the same accuracy in search engines like Google.

Advertising

It is estimated, in 2020 the global ad investment will grow to a mouth-opening US $656 billion industry and there is a good reason for that. Agencies have found online ads a much precise measure to reach their target audience. Every impression, click and bounce can be accounted for that is never the case with televised ads. No wonder the advertisers are making use of everything data science has to offer.

 

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Skills You Need for Data Science

Programming

Python, R, and Java are elementary languages to create machine learning algorithms. SQL, C/C++, and Perl hold equal importance among the developer community for data science. A data scientist creates probabilistic models based on the data at hand with the aforementioned programming languages. Python is handy in running statistical analysis on large data sets as it has a short learning curve and is easy to implement.

Machine Learning

It is a branch of Artificial Intelligence that aims to minimize human intervention in replicable business tasks. The algorithms tend to go through data clusters to identify patterns and learn from them. The ever-increasing volume of Big Data combined with the affordable cost of high-level computational processing has paved the way to generate algorithmic models. These models have the scope to analyze even larger and more complex sets of data and point businesses toward profitable outcomes. The fact that all industries from Government to retail and transportation use it, is proof that ML is a skill poised to grow with time.

Data Modelling

It is used to describe relationships between various kinds of data that are stored in the database. With the requisite knowledge of data modeling, you can optimize the storage capacity of a database at the same time allowing access and reporting of information. Think of it as a starting block for any data scientist, as without knowing how to structure and relate data they wouldn’t be able to go that far.

Databases

Knowledge of Database Management Systems is a must for anyone who aspires to be a data scientist. You must possess subject matter expertise in Relational Databases as it involves working with structured data. Using programming languages like SQL, a data scientist can retrieve data from the server, update values, and store it in the database.

Statistics & Probability

Without statistics discovering patterns in data would be impossible. You can store and do all sorts of things with databases and programming languages yet the guiding principle behind all that is statistics and probability. Regression, Time Series Analysis, and Hypothesis Testing are some of the methodologies applied to data. Data science aspirants must focus on linear algebra and calculus.

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Life Cycle of a Data Science Project

As many people, as many interpretations of the life-cycle. Here we present you with a simple to understand version of the end-to-end stages involved in a data science project.

Gather Requirements

You cannot solve a problem unless you know, what it is you must solve. The very first stage of a project is to ask questions that matter. The primary job of data scientists is to predict future outcomes based on historical data. They begin by assessing the type of forecast they are expected to make and ensuring they have the resources in the form of data, talent, and tools to do so. Once both, the problem to be addressed and the means to solve it are clear, a hypothesis is made on the timelines.

Data Mining

Once there is clarity on how to go about the business it is time to begin data collection. Data Mining is how you collate data from all the target sources. If it is already stored in a database, great. All you need to do then is query the data through SQL. If that is not the case, then you would have to gather data from multiple means

Data preparation

This is one of the most important and time-consuming steps. After gathering sufficient data, you must begin cleaning and re-formatting it as per your requirements. For instance, consider an excel sheet containing information on nationalities. What if it had two entries for the same nation, one labelled indian and the other Indian. Depending upon the inputted information for ML algorithms, you would then set each column right. Usually, the process of cleaning your data consumes approximately 50 – 80 percent of the time.

Data Exploration

After setting up the data for use, it is explored to establish relationships between data subsets. Data Scientists use tools like Panda, to create visualizations and understand each factor affecting a proposed outcome. For instance, suppose you had a database of professional football clubs and you wanted to understand the factors affecting their chances of winning a trophy. A probable relationship you might want to explore would be the link between the mean and median team-age and the nationality of the player. On analyzing the data, you could perhaps suggest a change in recruiting practices if a club wants to win anything.

Feature Engineering

Features are characteristics of the trend that you observe as a data scientist. In the above example of a football club, a feature could be the age of players. There are two stages to feature engineering i.e.

  • Feature Selection
  • Feature Construction

During feature selection, you identify and mark-down features that don’t help in predicting anything. In feature construction, you create new features from the ones that you already have. If necessary, these could even replace the old ones.

Predictive Modelling

This is the stage when a data scientist executes machine learning algorithms to do their bit. It is industry practice to run multiple models in parallel to see which one bears the best result. The models must be backed up with statistics to prove their quantum of supremacy. Other factors influencing the outcome of a predictive model is the size of data and its quality, available resources, and the kind of output you want to generate.

Data Visualization

All the prior stages were about deriving the right insights from your data but visualization is all about presentation. Not everybody speaks the language of numbers, especially, c-suite stakeholders, due to which it has to be presented in a manner that makes sense to them. Python is used extensively for its visualization capabilities. Tableau is yet another tool that enables you to convert complex equations into graphs and heat maps.

 

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Your Gateway to Data Science

Data science will continue to grow and require professionals with a diversified resume to get into the field. Java and Python offer abundant use cases in data science and make students employable for a future that appears to be getting challenging. Join NIIT and commence your journey towards data science with the following certificate courses:

The programs are backed by a faculty with an undisputed track record along with job assurance to ensure learners get all they need to accomplish their dream of becoming data scientists. 

Apply now!

 



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