data science as your career

Why choose data science for your career

Looking to get a career in the field of Data Science?   YES. it is a lucrative, in-demand, progressive, futuristic, and growth-oriented technology.   So what is it that makes data science such a scorching hot field to get into?

Being a Data Scientist involves having a basic skill set viz., knowledge of basic mathematics, basic statistics and probability, and basic computational and business analytical skills.  In other words, as a data scientist, you need to consistently have one foot on the IT sector, and the other planted firmly in the business world.  You need to have expertise in all the domains.

Data Science is mainly focused on the exploration of data, making an inference from the data, and deriving an insight or prediction from the inference with the help of various statistical and mathematical models, programming languages like python or R language, algorithms in machine language with python, visualization tools like tableau or Power BI, etc., Data Science requires the usage of both structured and unstructured data.

Machine learning requires two inputs for it to operate viz.,

  1. a) Algorithms and
  2. b) Data.

You should always provide clean data, otherwise, the models that you develop will be all junk.   You can derive insights from data using any of the models and tools mentioned above.  The choice is yours and the decision is taken after taking into account the complexity and scale of the problem. Thus, it helps business in taking the right decisions at the right time and also facilitates better strategic planning.

Let’s take an example in the healthcare sector where to detect or identify cancer in a person early, various medical reports (data) of the patient are provided to the system. The algorithms in machine learning make learning by using the algorithms and comparing your data with previously available records of patients, comparing the various parameters with the existing normal values, making an analysis, and derives at a final conclusion (result).  The more data (historical data)  of patients you have, the more accurate your result will be. Also, if you provide some arbitrary historical data, your result will not reflect the correct picture.

 If you wish to make a career in data science, you have two options viz.,

  1. Research Fields like Ph.D. and Post Doctoral: If you intend to go into the research field, you need to be qualified in that area of study, and have a thorough knowledge and understanding of mathematical, statistical, and computational concepts related to the study.
  2. Product Analytics and Visualization for industries and Service Sectors:  For choosing this field, you need to have a basic knowledge of the mathematical and statistical concepts, basic probability,  basic knowledge of python, and a good knowledge of python libraries like pandas, matplotlib and numpy and various tools associated with it viz., visualization tools like Tableau or Power BI.  Also, be well versed with SQL databases (MySQL,  SQL Server, or Oracle), and    Business Analytics along with machine learning and Deep Learning (including neural networks) and NLP.  Our master’s program in data Science is basically dealing with developing Product Analytics and Visualization for companies and our master’s program covers all of the above.

Our Data Science master program at eduJournal (, is a comprehensive program, designed to help learners of all skill levels,  master this technology.  Our curriculum is designed in such a way that it covers everything from the basic to advanced concepts which include expert instructions, coding exercises, quizzes, case studies, and real-world projects.  It provides the learners the skill and knowledge to analyze, visualize and make insights or predictions from the data and hone their skills by learning concepts by providing case studies associated with it and working on real-world projects.  We also hone your skills with Data Science Interview Questions widely asked in interviews like scenario-based interview questions, where you will be given a scenario and asked questions based on that scenario.  To get through this round you will need good working practical knowledge which can be achieved by doing some real-world projects.  We will guide you to prepare for that round.  Also, we have Data Science quizzes to measure your Data Science skills.

Roles and Responsibilities of a Data Scientist:

  1. Understanding the Clients requirements.
  2. Gather and Extract the dataset associated with the requirement.
  3. Clean and pre-process the data.
  4. Explore, Analyze, and visualize data using various analytical tools and various statistical or mathematical models and computational libraries and algorithms.
  5. Derive insights and make predictions.
  6. Evaluate the performance of these models and make improvements if required.
  7. Communicate the results and findings to stakeholders (client).
  8. Monitor and maintain the performance over time.

How to become a Data Scientist:

  1. Learn the basics of python (viz., libraries like pandas, matplotlib, numpy, scikit-learn, TensorFlow, etc.,  and developing algorithms for machine learning using python)  or R Language (if you are developing statistical and mathematical models).
  2. Familiarize yourself with the tools used for data analysis like Power BI, Seaborn and Tableau, and the various libraries mentioned above.
  3. Understand the basic mathematical concepts (linear algebra, decision trees), statistical concepts (Linear regression) and probability, and neural networks (Deep Learning & AI), which are required for developing algorithms that are the core of data science.
  4. Familiarize yourself with working with different types of data such as structured and unstructured data and various file formats like JSON, CSV, xls, sql dump.
  5. Understand the importance of data ethics and how to handle sensitive data carefully.


Advantages of Data Science:

  1. The abundance of opportunities: Data Science is greatly in demand today, and there is lots of opportunities for job seekers with excellent remuneration packages. It is estimated to generate 11.5 million jobs by the year 2026. As the data becomes increasingly important to aid in the decision-making process, the demand for data scientists continues to grow, making it a highly demanding skill in the job market.
  2. Used in multiple domains: it is a versatile field used in multiple domains such as finance, healthcare, marketing, banking, insurance, telecommunication, automobile, consultancy services, etc., giving you flexibility in your career path.
  3. Empowering management to make better decisions: Enables companies to make smart business decisions thus, improving the overall performance of the company. The ability to work with large quantities of data and generate insights or predictions, create new patterns, analyze data and generate reports, etc., can help in the overall development and increase the productivity of the company.
  4. Provide personalized insights: Enables computers to understand and predict human behavior and make data-driven decisions based on historical data. For eg., E-commerce sites provide personal insights to users based on past historical purchases.
  5. Handling complex problems: Facilitates breaking a larger complex problem into smaller manageable units and deriving a solution.
  6. Technological Advancement: With the improvements in technology, the ability to collect and store data, make analysis from data, derive insights and make predictions, etc., has made data science a popular field with greater potential for innovation.
  7. 7. Personal growth: It is a rewarding career for professionals who wish to use their problem-solving skills and creativity to find solutions to problems.

 Disadvantages of Data Science:

  1. Mastering Data Science is close to impossible: Data Science is a vast subject. The role of a data scientist depends on the domain in which the company is specialized in. For example, in the healthcare sector, a data scientist working on the analysis of genome sequencing will require some knowledge of genetics and molecular biology to create an algorithm for machine learning.
  2. Arbitrary data may yield unexpected results: Many times, the data provides is arbitrary and does not yield desired results.
  3. Data Privacy issues: While data scientists help clients make data-driven decisions, the ethical concern of individuals regarding the preservation of data privacy and its usage has been a cause for concern.

 Some common Python libraries used in Data Science for data analysis:

  1. a) pandas
  2. b) numpy
  3. c) matplotlib
  4. d) TensorFlow
  5. e) Scipy
  6. f) keras
  7. g) scikit-learn

Data Science has become an inevitable part of any industry today. The role of a data Scientist is to assist the management to make better decisions.  Data Science is a trending field today, helps you develop valuable skills, opens up newer career opportunities, and has a great impact on society at large by offering both personal and professional growth. Our program provides students with real world projects which strengthen their portfolios to get their dream Data Science job by implementing these real-world Data Science projects.

Dive into the world of endless possibilities as you learn to harness the power of data to uncover hidden insights, from predicting trends to uncovering patterns, Data Science has the power to transform the way we live and work.  Whether you are an absolute beginner or an experienced professional hoping to switch over to a Data Science career, our master’s program will take care of your journey to explore the world of data analytics and visualization.  Get ready to uncover the future with Data Science!!!

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