You can only pursue the program if you are a final-year graduate student (any discipline- MTech/BSc IT/MSc IT/BTech/BCA/MCA among other streams). You need to have scored 50% in 10th, 12th and graduation (score up to the last semester if you are in your final year). A tech background is highly preferred.

1. Data Analyst: A Data Analyst is responsible for gathering, processing, and performing statistical analyses on data. They may also be responsible for creating reports and visualizations to help business stakeholders understand trends and insights in the data.
2. Business Analyst: A Business Analyst focuses on identifying business problems and proposing solutions through data analysis. They may work closely with business stakeholders to gather requirements and help define project goals. They may also be involved in identifying and recommending new business opportunities.
3. Data Engineer: A Data Engineer is responsible for designing, building, and maintaining the infrastructure needed to support data processing and analysis. They may be involved in designing data pipelines, managing data storage, and developing tools and processes for data access and retrieval.
4. Data Scientist: A Data Scientist is responsible for identifying business problems that can be solved through data analysis, as well as designing and implementing statistical models and machine learning algorithms to extract insights from data. They may also be responsible for communicating insights to non-technical stakeholders and developing strategies for data-driven decision-making.

While the specific roles and responsibilities may vary depending on the organization, these are some general differences between a Data Analyst, Business Analyst, Data Engineer, and Data Scientist. It's worth noting that these roles may also require different skill sets, with some overlap in areas like statistics, programming, and data analysis.

You need to spend at least an hour during the weekdays, and 2-3 hours during the weekends, and 3-4 hours for the Weekly assignments.Which makes it 11-14 hours per week for the duration of 6 months. Overall this program will have 5-7 Live sessions every week, each session could be of 60-90 minutes

1. Programming Languages: Python and R are two of the most commonly used programming languages in data science. Python is popular because of its versatility and ease of use, while R is often preferred for its statistical capabilities.
2. BI Tools: There are several BI tools that are popular in data science, including Tableau, and Power BI. These tools are often used for data visualization and reporting, and can help to make insights more accessible to non-technical stakeholders.

We are going to cover Python Programming Language in this course, because it is the most popular programming language right now, and we will cover Tableau as a BI Tool because of its popularity and versatility.

No. It is mandatory for students to attend lectures as per defined program schedule and required to maintain 80% attendance for all live deliverables (lectures, mentorship calls).

1. Relevance to your career goals: If you have specific career goals in mind, choosing a domain specialization can help you gain the skills and knowledge you need to succeed in that field. For example, if you are interested in healthcare, specializing in healthcare data analytics could be a good choice.
2. Depth of knowledge: Specializing in a particular domain can help you gain a deeper understanding of the data and analysis techniques used in that field. This can be especially important when working with complex or sensitive data.
3. Generalizability of skills: While specializing in a particular domain can be beneficial, it is also important to have a strong foundation in the fundamentals of data science. This will allow you to apply your skills to a wide range of problems and industries.

Ultimately, the decision to specialize in a particular domain will depend on your individual goals and interests. If you are interested in a particular field, specializing in that area can help you gain a competitive advantage and stand out to potential employers. However, if you are more interested in developing general data science skills, it may be better to focus on building a strong foundation in the core concepts and techniques.

Frontend Developer:
Fresher:
The average salary for a fresher frontend developer in India ranges from INR 3 lakh to INR 6 lakh per annum.

Experienced:
Mid-level frontend developers with 2-5 years of experience can earn an average salary of INR 6 lakh to INR 12 lakh per annum.
Senior frontend developers with 5+ years of experience may earn salaries ranging from INR 12 lakh to INR 20+ lakh per annum.

Full Stack Developer:
Fresher:
The average salary for a fresher full stack developer in India ranges from INR 3.5 lakh to INR 7 lakh per annum.

Experienced:
Mid-level full stack developers with 2-5 years of experience can earn an average salary of INR 7 lakh to INR 15 lakh per annum.
Senior full stack developers with 5+ years of experience may earn salaries ranging from INR 15 lakh to INR 25+ lakh per annum.
Please note that these salary ranges are approximate and can vary significantly based on individual factors and market conditions.

It is completely up to you if you would like to take up the job offered to you, We will keep helping you to get more job opportunities. But our obligation to get an invitation for the interview will end with the first job offer you receive.

1. Purpose: A case study is usually done to analyze a specific situation, event or phenomenon in order to understand it better and draw conclusions. A project, on the other hand, typically involves solving a problem or creating something tangible.
2. Scope: A case study is often focused on a single organization, individual, or event. A project can have a broader scope and can involve multiple organizations or individuals.
3. Methodology: A case study typically involves extensive research and analysis of data collected through interviews, observations, and documentation. A project often involves designing and implementing a solution, and may include tasks such as coding, testing, and implementation.
4. Outputs: The outputs of a case study are typically reports or papers that present the findings of the research. The outputs of a project can include a wide range of deliverables, such as software, reports, presentations, or prototypes.

Overall, the main difference between a case study and a project is the purpose and scope of the work. While a case study is focused on understanding a situation or phenomenon, a project is focused on solving a problem or creating something tangible.

In this Program, You will get to work on both Projects, and Case studies. You will also get to work on domain specialized Projects and Case studies which will help you with enormous clarity into the subject.

The total Program duration is 9 months. Career services period starts post successful completion of the Major program and is of 6 months duration.

Yes, you will be able to successfully complete this program irrespective of your educational or professional background. All you need is to dedicate your time and efforts to this program—attend live sessions, complete the assignments on time to name a few. Please read the T&C carefully.

Apart from that, If in the middle of the program, you meet with some uncertain circumstances, such as an accident or any kind of medical emergency then we will provide you a fresh batch as per your convenience.

Or, If you are unable to focus on the Lectures due to work pressure in the office or some personal reasons such as Marriage/Vacations/Family functions etc, we will provide you with Recorded Lectures. If you get stuck with the recorded lectures, you can avail personalized doubt sessions as per your convenience.

Meritshot's Full Stack Professional Program covers 15+ Industry level Projects where you will get to learn about the User expectations, User Experience, User Interface, Project Management, HTML, CSS, Bootstrap, Jquery, Javascript, React, Node, Express, MongoDB, and AWS.