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What is Data Science?

Data Science is the area of study which involves extracting insights from vast amounts of data using various scientific methods, algorithms, and processes. It helps you to discover hidden patterns from the raw data. The term Data Science has emerged because of the evolution of mathematical statistics, data analysis, and big data.

Data Science is an interdisciplinary field that allows you to extract knowledge from structured or unstructured data. Data science enables you to translate a business problem into a research project and then translate it back into a practical solution.

In this Data Science Tutorial for Beginners, you will learn Data Science basics:

Why Data Science?

It helps you to prevent any significant monetary losses

Allows to build intelligence ability in machines

You can perform sentiment analysis to gauge customer brand loyalty

It enables you to take better and faster decisions

It helps you to recommend the right product to the right customer to enhance your business

Evolution of DataSciences

Data Science Components


Statistics is the most critical unit of Data Science basics, and it is the method or science of collecting and analyzing numerical data in large quantities to get useful insights.


Visualization technique helps you access huge amounts of data in easy to understand and digestible visuals.

Machine Learning:

Machine Learning explores the building and study of algorithms that learn to make predictions about unforeseen/future data.

Deep Learning:

Deep Learning method is new machine learning research where the algorithm selects the analysis model to follow.

Data Science Process

Now in this Data Science Tutorial, we will learn the Data Science Process:

1. Discovery:

Discovery step involves acquiring data from all the identified internal & external sources, which helps you answer the business question.

The data can be:

Logs from webservers

Data gathered from social media

Census datasets

Data streamed from online sources using APIs

2. Preparation:

Data can have many inconsistencies like missing values, blank columns, an incorrect data format, which needs to be cleaned. You need to process, explore, and condition data before modelling. The cleaner your data, the better are your predictions.

3. Model Planning:

In this stage, you need to determine the method and technique to draw the relation between input variables. Planning for a model is performed by using different statistical formulas and visualization tools. SQL analysis services, R, and SAS/access are some of the tools used for this purpose.

4. Model Building:

In this step, the actual model building process starts. Here, Data scientist distributes datasets for training and testing. Techniques like association, classification, and clustering are applied to the training data set. The model, once prepared, is tested against the “testing” dataset.

5. Operationalize:

You deliver the final baselined model with reports, code, and technical documents in this stage. Model is deployed into a real-time production environment after thorough testing.

6. Communicate Results

In this stage, the key findings are communicated to all stakeholders. This helps you decide if the project results are a success or a failure based on the inputs from the model.

Data Science Jobs Roles

Most prominent Data Scientist job titles are:

Data Scientist

Data Engineer

Data Analyst


Data Architect

Data Admin

Business Analyst

Data/Analytics Manager

Let’s learn what each role entails in detail:

Data Scientist:

Role: A Data Scientist is a professional who manages enormous amounts of data to come up with compelling business visions by using various tools, techniques, methodologies, algorithms, etc.

Languages: R, SAS, Python, SQL, Hive, Matlab, Pig, Spark

Data Engineer:

Role: The role of a data engineer is of working with large amounts of data. He develops, constructs, tests, and maintains architectures like large scale processing systems and databases.

Languages: SQL, Hive, R, SAS, Matlab, Python, Java, Ruby, C + +, and Perl

Data Analyst:

Role: A data analyst is responsible for mining vast amounts of data. They will look for relationships, patterns, trends in data. Later he or she will deliver compelling reporting and visualization for analyzing the data to take the most viable business decisions.

Languages: R, Python, HTML, JS, C, C+ + , SQL


Role: The statistician collects, analyses, and understands qualitative and quantitative data using statistical theories and methods.

Languages: SQL, R, Matlab, Tableau, Python, Perl, Spark, and Hive

Data Administrator:

Role: Data admin should ensure that the database is accessible to all relevant users. He also ensures that it is performing correctly and keeps it safe from hacking.

Languages: Ruby on Rails, SQL, Java, C#, and Python

Business Analyst:

Role: This professional needs to improve business processes. He/she is an intermediary between the business executive team and the IT department.

Languages: SQL, Tableau, Power BI and, Python

Tools for Data Science

Data Analysis Data Warehousing Data Visualization Machine Learning

R, Spark, Python and SAS Hadoop, SQL, Hive R, Tableau, Raw Spark, Azure ML studio, Mahout

Difference Between Data Science with BI (Business Intelligence)

Parameters Business Intelligence Data Science

Perception Looking Backward Looking Forward

Data Sources Structured Data. Mostly SQL, but some time Data Warehouse) Like logs, SQL, NoSQL, or text

Approach Statistics & Visualization Statistics, Machine Learning, and Graph

Emphasis Past & Present Analysis & Neuro-linguistic Programming

Tools Pentaho. Microsoft Bl, QlikView, R, TensorFlow

Applications of Data Science

Some application of Data Science are:

Internet Search:

Google search uses Data science technology to search for a specific result within a fraction of a second

Recommendation Systems:

To create a recommendation system. For example, “suggested friends” on Facebook or suggested videos” on YouTube, everything is done with the help of Data Science.

Image & Speech Recognition:

Speech recognizes systems like Siri, Google Assistant, and Alexa run on the Data science technique. Moreover, Facebook recognizes your friend when you upload a photo with them, with the help of Data Science.

Gaming world:

EA Sports, Sony, Nintendo are using Data science technology. This enhances your gaming experience. Games are now developed using Machine Learning techniques, and they can update themselves when you move to higher levels.

Online Price Comparison:

PriceRunner, Junglee, Shopzilla work on the Data science mechanism. Here, data is fetched from the relevant websites using APIs.

Challenges of Data Science Technology

A high variety of information & data is required for accurate analysis

Not adequate data science talent pool available

Management does not provide financial support for a data science team

Unavailability of/difficult access to data

Business decision-makers do not effectively use data Science results

Explaining data science to others is difficult

Privacy issues

Lack of significant domain expert

If an organization is very small, it can’t have a Data Science team


Data Science is the area of study that involves extracting insights from vast amounts of data by using various scientific methods, algorithms, and processes.

Statistics, Visualization, Deep Learning, Machine Learning are important Data Science concepts.

Data Science Process goes through Discovery, Data Preparation, Model Planning, Model Building, Operationalize, Communicate Results.

Important Data Scientist job roles are: 1) Data Scientist 2) Data Engineer 3) Data Analyst 4) Statistician 5) Data Architect 6) Data Admin 7) Business Analyst 8) Data/Analytics Manager.

R, SQL, Python, SaS are essential Data science tools.

The predictions of Business Intelligence is looking backwards, while for Data Science, it is looking forward.

Important applications of Data science are 1) Internet Search 2) Recommendation Systems 3) Image & Speech Recognition 4) Gaming world 5) Online Price Comparison.

The high variety of information & data is the biggest challenge of Data science technology.

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