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What is Data Science and Machine Learning?

Data Science and Machine Learning are the most searched terms in the 21st century in the world of technology. Right from the first-year aspirants in computer science to popular organizations like Amazon, Netflix, etc., are after these two amazing techniques.

Data Science is the study of understanding, interpretation, and application of new techniques and methods to organize useful data and develop new procedures to make significant business decisions.

In other words, Data Science is a domain that makes use of scientific methods, algorithms, and procedures to derive insights and information from unstructured and structured data to apply that knowledge to a wide range of applications.

Machine Learning is a study that gives computers capabilities so that they can learn without being programmed explicitly. Algorithms are applied in machine learning for processing the data and getting trained to give future predictions without humans’ intervention. Companies like Google and Facebook use Machine Learning extensively.

So how to learn Data Science smartly? The answers are right here for you. You can start learning Data Science in the following way:

  • Analyze Yourself
  • Learn the programming language
  • Learn Data Analysis, Visualization, and Manipulation
  • Learn Machine Learning
  • Practice until you master

Data Science is a complicated carrier choice, but it is creative too. Now, how do I start learning Data Science? Basic mathematical skills like complex equations, integration, differentiation, calculus, database and programming have to be learned. Then you have to learn the basic programming language- Python or R.

So, you have now decided to make a career in this exciting field. Let us have a look at the Step-by-step roadmap to learn Data Science. There are seven steps for learning Data Science.

  1. Getting the fundamental technical skills
  2. Loving the Data
  3. Practicing the knowledge of Data Science.
  4. Learning to Communicate the Insights
  5. Learning from Peers
  6. Increase the difficulty levels constantly
  7. Added skills required to be a good Data Scientist.

Internship in Data Science

An internship is the next course of action that you have to take. They give opportunities for beginners to get experience in this field. You need to get acquainted with the 10 useful tips for landing that Data Science Internship that you’ve always wanted. These tips include adding a certification course in Data Science to keep yourself updated about the organization.

Also, the top 12 data science studies and research opportunities can help you choose your course of action in the field of Data Science. These include:

  1. PG Program in Data Science and Business Analytics
  2. PG Program in Data Science and Engineering
  3. MS in Data Science Programme
  4. Data Science and Machine Learning: Making Data-Driven Decisions
  5. PG Program in Data Analytics
  6. MTech in Data Science and Machine Learning
  7. Master’s in Data Science
  8. Professional Certificate in Data Science
  9. Master’s Program in Statistics and Data Science
  10. PGDM in Big Data Analytics
  11. MSc Data Science
  12. PGP Machine Learning

Web scraping

Web scraping is nothing but extracting data from several web pages. Python is a straightforward, user-friendly, and object-oriented language to get started in Data Science and is most suited for web scraping.

How to use Web Scraping in data science with Python?

There are several steps that you should follow for web scraping. Firstly, locate the URL, inspect the page, write the code and finally, store the data in a file. Web scraping is accurate, easy for implementing, not very expensive, fast, and low maintenance. Web scraping is used in market research, etc.

The Top 10 Python libraries for Data Science in 2021 that are becoming popular and help you stay updated are:

  • Scrapy
  • Beautiful Soup
  • Pandas
  • NumPy
  • SciPy
  • Keras
  • TensorFlow
  • Bokeh
  • Seaborn
  • Scikit Learn

Of the above list of libraries, Pandas is great for data science and analytics, especially if you use Python, as it is more powerful than VBA and  Excel. It uses flexible, fast, and expressive data structures that are designed to make working easy with labeled or relational data both intuitive and easy.

Data Visualization is a very critical component in Data analysis. It helps to extract the most valuable information from business data. Data visualization charts are essential as they combine several data sets and produce a visual representation of that data by using diagrams, graphs, and charts.

The top 10 Python Libraries for Data Visualization are:

  • Matplotlib
  • Plotly
  • Seaborn
  • GGplot
  • Bokeh:
  • Pygal:
  • Geoplotlib
  • Altair
  • Gleam
  • Missingno

Amongst these libraries, Keras is one of the most user-friendly and influential Python libraries. How is Keras used in Data Visualization? Keras is a high-level API wrapper that runs on engines like CNTK, TensorFlow, or Theano, quick, modular, and straightforward to use. Keras is an API that is a human-centric library.

The advancement in Data Science makes it necessary for developing a broader range of competencies and skills amongst data scientists. Here are some of the essential data Science Skills that everyone needs.

  • Mathematics and Statistics
  • Coding skills
  • Data Wrangling
  • Data Visualization
  • Machine Learning
  • Big Data
  • Soft Skills
  • Analytical Mind
  • Eagerness to learn and
  • Process Improvement

Data Science and Machine Learning are going to rule the future world. Hence, we have to consider the pre-requisites for learning this course which is so much in demand.

The 7 books to grasp Mathematical Foundation for Data Science and Machine Learning include The Nature of Statistical Learning Theory, Machine Learning, and Algorithmic Perspective, Second Edition, etc.

Data Science uses Mathematics, and most IT engineers come with computer science or Science in their academic path. So, they need good exposure to the subject. How much mathematics does an IT engineer need for data science? The following 6 skills will help:

  • Calculus
  • Linear Algebra
  • Probability and Statistics
  • Discrete Math
  • Graph Theory and
  • Operations Research.

A large domain in itself, Data Science has several different areas for you to learn about.  The 5 Data Science projects that will get you hired are

  • Data Cleaning
  • Exploratory Data Analysis
  • Interactive Data Visualization
  • Machine Learning and
  • Communication

Digital media is full of online courses with interactive audio and visual programs. Yet, books still happen to be the primary source for self-education for a holistic approach.

The Top 10 best data Science books that are worthy of reading will help you gain more knowledge in this field. Most of these books are available in pdf format and are free.

Data Science, AI, and ML are interconnected but, each have their own specific applications. So, let us look at the best tool for AI, ML, and Data science:

TensorFlow, Py Torch,, Apace Mahout, Apache Spark MLib, SAS, Apache Spark, Big ML, Monkey Learn. Aylien, IBM Watson, Google Cloud, Amazon Comprehend, and NLTK.

Most of you who are aspiring to become data Scientists will have this question. Will automation eliminate the Data Science position?

Although AI can outperform Data Scientists, it cannot replace or lessen the demand for them. It actually creates more opportunities for them. It also fuels their workflow and supports them to focus on their demanding and creative jobs.

So, why IT needs to lead the next phase of Data Science? Data Scientists can try out new algorithms or tools and elevate the work level that is necessary across the company.


In today’s world, the field of Data Science and Machine learning is considered to be fast emerging and a vast one for you to grow and make a career in as IT will lead the new phase of Data Science.

Data Science simplifies data and converts it into user-friendly data. With the help of automation and AI, Data Science is bound to transform several industries like healthcare, businesses, transportation, manufacturing, and finance.