Data Science: A Guide to Data Analysis and Insights

Aman Rajput

Sep 6, 2022
Data Science: A Guide to Data Analysis and Insights

What is Data Science?



Data science is an interdisciplinary field that combines Mathematics, Statistics, programming, analytics, artificial Intelligence and Machine Learning to find hidden insights in an organization’s data. 

Data science deals with vast amounts of data to find unseen trends, drive meaningful conclusions from data to make better business decisions.


Now we have some idea about what Data Science is. So, now let’s talk about the various stages of a Data Science project.




The Stages of Data Science Projects:



      a.  Collection: The first stage is to capture data. This stage is the accumulation of structure and unstructured data.

b.  Storing and Maintaining data: After we accumulate data we need to store this data in data warehouses and then we need to clean the data. This stage takes the raw data and puts it into a form from which it can be used for further analysis. Before storing data into a database or a data warehouse we need to ensure the quality of the data.

c.  Data Analysis: This stage involves performing the various analysis of the data like Regression, Text Mining, Predictive Analysis etc. Data scientists examine different patterns, biases and ranges of different values within the data. Depending on the results that come out of this analysis of the data businesses make better decisions and help them in their future scalability.

d.  Communication: Reporting Data through visualizations and reports to make it easier for data analysts and decision makers to view the data and come to some conclusions. A data science programming language such as R or Python includes components for generating visualizations.

Data Analyst vs Data Scientist.



A Data Analyst explains what is happening by processing the data generated, but a data scientist not only does the analysis to find out insights from the data, but a data scientist also uses various advanced machine learning algorithms to identify the occurrence of a particular event in the upcoming future.

In this way the role of a data scientist and technology that surrounds a data scientist are way bigger than that of a data analyst.




Prerequisites for Data Science:

1. Machine Learning: Machine learning is very important for a data scientist. Data scientists need to have a solid grasp of ML with the basic knowledge of statistics.


2.  Programming: Programming is also very important for a data scientist to successfully carry out some data science projects. The most popular and common languages for data science are Python and R. Python is especially popular because there are a lot of libraries of ML and data science on the internet.

3. Databases: An efficient and capable data scientist must have a thorough knowledge of Databases, How to extract, delete, and edit data in the databases.

4. Modeling: Statistical and Mathematical Models enable a data scientist to make calculations and predictions based on the available data. Modeling is a part of Machine Learning and involves identifying which algorithm is the most suitable to solve a given problem.




Who is a Data Scientist?


Data scientist is a very new profession in comparison to other IT professions. Data scientists have the technical ability to investigate what questions need to be answered. They are a mix of Computer Scientists, Mathematicians, and Statisticians.




Daily Tasks that a Data Scientist do:

1.   Discover trends and patterns to get insights.

2.   Create data models and forecasting algorithms.

3.   Improve data and product qualities by using machine learning techniques.

4.   Present conclusions to the other teams and management.

5.   Use Data tools such as R, SAS, Python, SQL, Hadoop, etc.

Future of Data Science:


Data science is relatively a new field in the tech sector and it’s growing rapidly, applications of data science are immense and the future holds great potential for this new technology.

Businesses generate over 2.5 quadrillion bytes of data every single day. Data science is important for any business to get insights and valuable information out of all this data as Digital data is becoming the tool that powers the global economy.



How Data science is helping businesses grow:

1.   Influencing potential customers to their purchase decision.

2.   Profitability analysis of the new products that are to be developed and also if there is any risk involved with building these new products.

3.   Increased knowledge of what customers want and their preferences.

4.   Better marketing campaign, to target the selected audience.

5.   Detecting any possible risk of customer loss.


At its core, the main goal of data science is to help businesses grow and help these big corporations keep increasing their profits. The better algorithms, data science tools and data scientists a corporation has, the more profits and beneficial it is for them.


In this profit chasing economy the role of a data scientist is very bizarre and important and the demand for data scientists and data analysts are increasing year by year and by more than 25% each year.


So, the future of data science is bright and necessary for businesses to make better decisions. And learning this new technology with the Industry experts is all the more important. 


For Data science enthusiasts, Perfect eLearning brings a Complete Data Science course with Python by Industry expert Mr. Sidhardhan S, the course is very thorough and elaborative, every Data Science models, algorithms and tools are discussed in depth with project based learning.


To view our course overview visit: Data Science Course 


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