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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 mathematical, basic statistics and probability, 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 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 learning with python, visualization tools like Tableau or Power BI etc., Data Science requires the usage of both structured and unstructured data.

The machine learning requires two inputs for it to operate viz.,

a) Algorithms and

b) Data.

You should always provide clean data, otherwise the models that you develop will be all junk. You can derive insights and trends from data using any of the models and tools mentioned above. The choice is yours and the decision is taken after taking in 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 makes learning by using the algorithms and comparing your data with previous 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.,

Research Field like PhD 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.

Product Analytics and Visualization for industries and Service Sectors: To choosing this program, 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), Predictive Modeling,  and NLP.  Our master program in Data Science is basically dealing with developing Product Analytics and Visualization for companies and our training program covers all of the above.

Our Data Science master program at eduJournal (www.edujournal.com), is a comprehensive program, designed to help learners of all skill levels, master this technology. Our syllabus is designed in such a way that it covers everything from the basic to advanced concepts which include expert instructions, coding exercise, quizzes, case studies and real world projects. It provides learners with the skill and knowledge to analyze, visualize and derive insights, trends 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 a 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 the 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, neural networks (Deep Learning & AI), which are required for developing algorithms which are the core to 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. 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 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 the flexibility in career path.

3. Empowering managements 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 quantity of data and generate insights or predictions, creating 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., Ecommerce sites providing 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 at a solution.

6. Technological Advancement: With the improvements in technology, the ability to collect and store data, make analysis from data, deriving insights and make predictions etc., has made data science a popular field with greater potential for innovation.

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 domain in which the company is specialized in. For example, in a 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 provided 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 have been a cause for concern.

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

a) pandas

b) numpy

c) matplotlib

d) TensorFlow

e) Scipy

f) keras

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, opening 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 strength 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 experiences professional hoping to switch over to a Data Science career, our master 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!!!