Benefits of Data Science Course in Pune

Data science course in Pune

NMD Pvt. Ltd. offers a data science course in Pune with 100% placements, certification, and affordable fees. Today, data science is considered one of the highest-paid jobs available in the business world. Data scientists are being hired on a frequent basis as the need for data scientists is increasing day by day.

There are a lot of places where you can find data science courses in Pune, but NMD Pvt. Ltd. is considered to be the best amongst the rest of them.
Data science jobs are considered to be done efficiently, so having good knowledge about them is necessary.
NMD Pvt. Ltd. offers the best courses in data science in Pune. They provide different opportunities to ensure they are placed with companies where they can enhance their careers. As mentioned earlier, data science is one of the most preferred job positions for people today.
NMD Pvt. Ltd. not only teaches their students but also ensures that they are being groomed well enough to face the corporate world. Moreover, our founder, Mr. Navnath, believes in not only taking classes but also ensuring that the classes are interactive and fun to learn.
NMD Pvt. Ltd. has classes with industry experts, guest sessions, and a lot more different things like sessions with experts, seminars, internships, and a lot more practical experience for our students.
Also, they ensure to bring in experts from the industry every month so that students get to interact with them.

Duration of the Data Science Course in Pune

Usually, data science courses take some time as the syllabus is quite vast, along with the projects given to the students. For data science courses in Pune, the maximum duration is one year. The syllabus has been designed according to the course structures for three months, six months, or one year. The three-month course consists of a basic-level course wherein a brief introduction of the topics is given along with projects. Topics like Python, SQL, basic Excel, HTML, CSS, etc. are a few that we cover along with live projects.
Six-month courses consist of an intermediate-level course wherein topics are covered in detail over a period of 10–15 days, and it is ensured that each and every topic is covered with examples. Topics like advanced Python, SQL, advanced Excel, the R language, data visualization, etc. are covered in the data science classes in Pune, along with a series of projects to complement this theoretical knowledge.
A one-year course consists of advanced courses where advanced certification, projects, internships—everything is available for this course. Apart from that, machine learning, AI, power BI, etc. are some topics that are covered for the one-year course with NMD Pvt. Ltd.
There are weekday and weekend batches available for data science classes where these topics would be covered. They are also available online or offline, with classes conducted from the comfort of both the faculty and the student.


Any certification course always turns out to be useful, especially in the corporate sector, as it increases your chances during placements.
NMD Pvt. Ltd. provides certification courses along with data science courses, wherein upon completion certificates are provided, which you can attach and increase the chances of getting your resume selected over other candidates.
Seven plus certification courses are available at NMD Pvt. Ltd. for the students to increase their knowledge.

Syllabus for the Data Science Course in Pune

The syllabus is designed keeping in mind the duration of the course and the amalgamation of both theoretical and practical knowledge for the students.
As mentioned above, these topics, along with live projects and internships, are provided for students at NMD Pvt. Ltd. to ensure good knowledge and expertise in their field.
Upon completion, placement opportunities begin for the students, and they are constantly provided with analysis of their performance to ensure that they are getting the best opportunities over others.

Job Opportunities

There are a wide range of job opportunities available for students, especially data science students, but finding the best opportunity with good work as well as a good package is necessary, and that is where NMD Pvt. Ltd. stands out from the rest.
NMD Pvt. Ltd., with its tie-ups with more than 30 companies, ensures that students receive the best packages from renowned companies like Infosys, HCL Technologies, etc.
Our alumni have been working for a long period of time with these companies and have grown in their field. Our founder, Mr. Navanath Darekar, himself works for Hotstar as their data and business analyst, which is so inspiring for students across the globe to learn directly from industry experts in a place where quality education is given priority over the rest.


Fees for the Data Science Course in Pune

NMD Pvt. Ltd. ensures that students are taught in the best and most effective way at affordable prices so that all students can have access to them. Therefore, our fees are affordable for a data science course in Pune. NMD Pvt. Ltd. also provides the option of PAP, which is a pay-after-placement opportunity wherein the last installment of the course can be paid after the placement of the student in order to ensure trust and fulfilment of the promise made by the institute to the student.

Frequently Asked Questions (FAQ) on the Data Science Course in Pune

What is data science?
Data technology is an interdisciplinary area that mixes techniques from arithmetic, facts, computer technology, and area understanding to extract insights and expertise from data.
What are the important capabilities required in order to be a data scientist?
Data scientists usually want talents in programming, statistics, mathematics, device learning, information visualization, and area expertise. Additionally, accurate communication and problem-solving skills are critical.

What programming languages are usually utilized in information technology?
Python and R are the most famous programming languages within the statistical technological network. They have tremendous libraries and gear mainly designed for record analysis and system studies.
What is the difference between gadget mastery and fact-based technology?
Data technology is a broader area that entails accumulating, cleansing, and reading facts to extract insights, even as system-gaining knowledge is a subset of information technology that specializes in algorithms to make predictions or decisions based totally on the information.

What is the CRISP-DM method?
CRISP-DM (Cross-Industry Standard Process for Data Mining) is an extensively used data mining and analytics method.   

What are the statistics in information technology?
Statistics is essential in data science for tasks such as statistics exploration, speculation testing, and constructing statistical fashions. It facilitates information styles, making inferences, and drawing significant conclusions from records.
What is the distinction between supervised and unsupervised mastering?
Supervised learning is gaining knowledge, which includes education and a model on classified information, where the target variable is thought, to make predictions on new, unseen facts. Unsupervised mastering, on the other hand, deals with unlabeled facts, searching to discover styles or groupings without predefined consequences.
How do you deal with missing information in a dataset?
There are diverse techniques to handle missing information, such as casting off the rows or columns with missing facts, imputing lacking values with statistical measures (e.g., suggest, median), or using superior imputation strategies like regression or a couple of imputations.

What is overfitting in gadget studying?
Overfitting takes place when a model performs extremely well on the training information but fails to generalize to new, unseen information. It happens when a version captures noise or irrelevant styles from the training set. Regularization techniques and cross-validation can help mitigate overfitting.
How do you evaluate a device learning model’s performance?
Common evaluation metrics for class issues encompass accuracy, precision, bearing in mind, F1 rating, and location under the ROC curve (AUC-ROC). For regression issues, metrics like mean squared errors (MSE), mean absolute error (MAE), and R-squared are usually used.

The Future of Data Science

Increasing demand: The call for professional data scientists will continue to go upward throughout industries. Organizations are recognizing the price of statistics-driven selection and are investing heavily in statistical technological teams.

The exponential increase in statistics, coupled with improvements in the era and growing opposition, will fuel the need for record-keeping scientists who can extract meaningful insights and force innovation.
Advancements in a Generation: Technology will play a critical role in shaping the future of statistics science. As computing strength and storage talents continue to improve, data scientists could have access to larger datasets and more state-of-the-art algorithms. The development of artificial intelligence (AI) and system learning (ML) will enable statistics scientists to build extra-accurate models and make an increasing number of complicated predictions.

Integration of records science with different fields: Data technology will increase the number of integrations with different disciplines, consisting of commercial enterprise, healthcare, social sciences, and environmental sciences. Collaborations among information scientists and domain specialists will result in more focused and impactful solutions. This interdisciplinary technique will open up new possibilities and challenges in solving complex problems and addressing societal troubles.

Ethical considerations: With the developing impact of record science, moral concerns will become even more essential. The accountable use of records, privacy protection, algorithmic equity, and transparency might be at the vanguard of discussions. Ethical frameworks and rules will continue to conform to make certain that facts and technology practices align with societal values and deal with potential biases and risks.
Automated record analysis: Automation will transform positive components of statistics technology. Routine responsibilities along with record cleansing, preprocessing, and model selection could be automated, allowing facts scientists to focus more on decoding outcomes, imparting insights, and making strategic selections. Automated machine getting-to-know (Auto ML) tools will simplify the manner of building and deploying fashions, democratizing facts, and increasing technological know-how to a degree.

Explainable AI and interpretability: As AI and ML become more widely included in selection-making strategies, the need for explainable AI will intensify. The capacity to interpret and explain the reasoning behind AI-pushed decisions will become vital, especially in domain names inclusive of healthcare and finance. Research and development efforts can be directed towards constructing fashions that are both correct and interpretable.

Continuous studying and upskilling: Data technology is a swiftly evolving discipline, and professionals will need to embrace lifelong studying and upskilling to live effectively. As new technologies, algorithms, and methodologies emerge, record scientists will want to conform and accumulate new abilities. The potential to examine and adapt quickly can be a key differentiator in the destiny task market.

Democratization of statistics technology: The democratization of data technological equipment will allow more individuals to leverage statistics for decision-making. User-pleasant interfaces, low-code or no-code systems, and pre-constructed fashions will make information technology available to a much broader target market. This democratization will empower area specialists, commercial enterprise users, and people with confined technical backgrounds to harness the energy of facts.
Data privacy and safety: The destiny of statistics technology will contain a heightened focus on information privacy and security. With increasing issues about record breaches and misuse, organizations will invest more in robust statistics protection measures and compliance with privacy policies. Data scientists will need to make sure that their analyses and models are developed in a manner that safeguards sensitive records.