Everything about Data Science

8 min readMar 8, 2022


What is Data Science?

Data Science is a blend of various tools, algorithms, and machine learning principles to discover hidden patterns from the raw data. It is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data.

The following things can be considered as examples of Data Science. Such as; Identification and prediction of disease, optimizing shipping and logistics routes in real-time, detection of frauds, healthcare recommendations, automating digital ads, etc.

What do you think data scientists do?

A data scientist is a professional responsible for collecting, analysing, and interpreting extremely large amounts of data. Data scientists are responsible for discovering insights from massive amounts of structured and unstructured data to help shape or meet specific business needs and goals The data scientist role is an offshoot of several traditional technical roles, including mathematician, scientist, statistician, and computer professional. This job requires the use of advanced analytics technologies, including machine learning and predictive modelling.

The insights that data scientists uncover should be used to drive business decisions and take actions intended to achieve business goals.

The data scientist’s role is becoming increasingly important as businesses rely more heavily on data analytics to drive decision-making and lean on automation and machine learning as core components of their IT strategies. A data scientist requires large amounts of data to develop hypotheses, make inferences, and analyse customer and market trends. Basic responsibilities include gathering and analysing data, using various types of analytics and reporting tools to detect patterns, trends, and relationships in data sets.

In business, data scientists typically work in teams to mine big data for information that can be used to predict customer behaviour and identify new revenue opportunities. In many organizations, data scientists are also responsible for setting best practices for collecting data, using analysis tools and interpreting data

Why is data science important?

Data science is a highly interdisciplinary practice involving a large scope of information and one that usually takes into account the big picture more than other analytical fields. In business, the goal of data science is to provide intelligence about consumers and campaigns and help companies create strong plans to engage the audience and sell their products.

Data scientists must rely on creative insights using big data, the large amounts of information collected through various collection processes, like data mining.

What are the real-time applications of data science? Make a list of common data science deliverables?

• Prediction (predict a value based on inputs)

• Classification (e.g., spam or not spam)

• Recommendations (e.g., Amazon and Netflix recommendations)

• Pattern detection and grouping (e.g., classification without known classes)

• Anomaly detection (e.g., fraud detection)

• Recognition (image, text, audio, video, facial, …)

• Actionable insights (via dashboards, reports, visualizations, …)

• Automated processes and decision-making (e.g., credit card approval)

• Scoring and ranking (e.g., FICO score)

• Segmentation (e.g., demographic-based marketing)

• Optimization (e.g., risk management)

• Forecasts (e.g., sales and revenue)

What is the difference between a data analyst Role and a data scientist Role?

Data analysts share many of the same skills and responsibilities with Data scientists.

Some of these shared skills include the ability to:

• Access and query (e.g., SQL) different data sources

• Process and clean data

• Summarize data

• Understand and use some statistics and mathematical techniques

• Prepare data visualizations and reports

Some of the key differences, however, are that data analysts typically are not computer programmers, nor responsible for statistical modelling, machine learning, and many of the other steps outlined in the data science process above. The tools used are usually different as well. Data analysts often use tools for analysis and business intelligence like Microsoft Excel (visualization, pivot tables, …), Tableau, SAS, SAP, and Qlik.

Analysts sometimes perform data mining and modelling tasks but tend to use visual platforms such as IBM SPSS Modeler, Rapid Miner, SAS, and KNIME.

Data scientists, on the other hand, perform these same tasks usually with tools such as R and Python, combined with relevant libraries for the language(s) being used.

Lastly, data analysts tend to differ significantly in their interactions with top business managers and executives. Data analysts are often given questions and goals from the top-down, perform the analysis and then report their findings. Data scientists, however, tend to generate the questions themselves, driven by knowing which business goals are most important and how the data can be used to achieve certain goals. In addition, data scientists typically leverage programming with specialized software packages and employ much more advanced statistics, analytics, and modelling techniques.

What are the job opportunities for data science and artificial intelligence?

Here is a list of top 5 job opportunities that are open to you in the data science and artificial intelligence industry:

Data Scientist:

A data scientist is someone who is able to extract valuable information out of the given data. A data scientist’s job usually involves conducting predictive analysis and utilising machine learning and related tools to extract data. The data received by any company is often muddled and needs cleaning, and that is where data scientists come into the picture. A data scientist is expected to clean and present the data in a manner that can benefit the company.

Machine Learning Engineer:

A machine learning engineer has to combine the scopes of data science and artificial intelligence to programme machines to learn how to perform certain tasks. To become a machine learning engineer, you must develop strong computer programming skills. Machine learning engineers basically create computer programmes that enable machines to perform actions without being specifically designed or directed to perform these tasks.

Business Intelligence Developer:

A business intelligence developer is a unique and important designation in any organisation. As a branch of artificial intelligence, business intelligence development is necessary to collect and analyse complicated data to decipher and predict business and market trends. A business intelligence developer is expected to design, remodel, and hold complicated data in an accessible cloud-related platform.

Big Data Engineer/Architect:

A big data engineer and architect is one of the most lucrative artificial intelligence and data science career options. Every big data engineer and architect is tasked with creating a system through different areas of a business

can communicate with one another effectively, and collect data with ease. Big data engineers and architects are expected to be experts at computer programming and computer languages like C++, Java, Python, and Scala.

Database Developer:

A database developer is expected to improve prevailing database processes and update the business standards and structures by effectively coding programmes. Each database developer is also tasked with maintaining existing databases, detecting and fixing bugs in the system, improving the existing database, and solving any issues or concerns that may rise up in the database systems along the way in the future.

What are the Top Data Science Tools That we use?

1. SAS:

It is one of those data science tools which are specifically designed for statistical operations. SAS is a closed source proprietary software that is used by large organizations to analyse data. SAS uses the base SAS programming language for performing statistical modelling.

It is widely used by professionals and companies working on reliable commercial software. SAS offers numerous statistical libraries and tools that you as a Data Scientist can use for modelling and organizing your data.

2. Apache Spark:

Apache Spark or simply Spark is an all-powerful analytics engine and it is the most used Data Science tool. Spark is specifically designed to handle batch processing and Stream Processing.

It comes with many APIs that facilitate Data Scientists to make repeated access to data for Machine Learning, Storage in SQL, etc. It is an improvement over Hadoop and can perform 100 times faster than MapReduce.

3. BigML:

BigML is another widely used Data Science Tool. It provides a fully interactable, cloud-based GUI environment that you can use for processing Machine Learning Algorithms. BigML provides standardized software using cloud computing for industry requirements.

4. D3.js

JavaScript is mainly used as a client-side scripting language. D3.js, a JavaScript library allows you to make interactive visualizations on your web browser. With several APIs of D3.js, you can use several functions to create dynamic visualization and analysis of data in your browser.

Another powerful feature of D3.js is the usage of animated transitions. D3.js makes documents dynamic by allowing updates on the client-side and actively using the change in data to reflect visualizations on the browser.


MATLAB is a multi-paradigm numerical computing environment for processing mathematical information. It is a closed-source software that facilitates matrix functions, algorithmic implementation and statistical modelling of data. MATLAB is most widely used in several scientific disciplines.

In Data Science, MATLAB is used for simulating neural networks and fuzzy logic. Using the MATLAB graphics library, you can create powerful visualizations. MATLAB is also used in image and signal processing.

6. Excel

Probably the most widely used Data Analysis tool. Microsoft developed Excel mostly for spreadsheet calculations and today, it is widely used for data processing, visualization, and complex calculations. Excel is a

powerful analytical tool for Data Science. While it has been the traditional tool for data analysis, Excel still packs a punch.

7. ggplot2

ggplot2 is an advanced data visualization package for the R programming language. The developers created this tool to replace the native graphics package of R and it uses powerful commands to create illustrious visualizations.

It is the most widely used library that Data Scientists use for creating visualizations from analysed data. Ggplot2 is part of tidy verse, a package in R that is designed for Data Science.

8. Tableau

Tableau is a Data Visualization software that is packed with powerful graphics to make interactive visualizations. It is focused on industries working in the field of business intelligence.

The most important aspect of Tableau is its ability to interface with databases, spreadsheets, OLAP (Online Analytical Processing) cubes, etc. Along with these features, Tableau has the ability to visualize geographical data and for plotting longitudes and latitudes in maps.

9. Jupyter

Project Jupyter is an open-source tool based on Python for helping developers in making open-source software and experiences interactive computing. Jupyter supports multiple languages like Julia, Python, and R.

10. Matplotlib

Matplotlib is a plotting and visualization library developed for Python. It is the most popular tool for generating graphs with the analysed data. It is mainly used for plotting complex graphs using simple lines of code. Using this, one can generate bar plots, histograms, scatterplots etc.

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