#datascienceplacement

[bestdatasciencecourseindia]

🌟 Learner Speaks: Aadi Roy’s Take on Great Learning’s Data Science Course 🎓

Meet Aadi Roy, one of our latest visitors at Analytics Jobs — India’s only course reviews platform. He recently shared his honest feedback on the Great Learning Data Science Course, highlighting his placement journey, course experience, and overall learning outcomes.

🔍 Thinking of enrolling in this course?

Don’t go in blind. 📉📈 Check out real, unfiltered reviews from learners like Aadi to make a smart, informed decision about your career in Data Science.

📩 Already taken this course? Share your experience and help others choose wisely.

👉 Read or drop your review here: 🔗 https://analyticsjobs.in/question/how-is-your-experience-with-great-learning-data-science-placement/

#DataScience #EdTechReviews #AnalyticsJobs #CourseReview #GreatLearning #StudentFeedback #CareerInDataScience #UpskillIndia #RealStories #DataSciencePlacement #SocialProof #ReviewAndDecide

[akshay-s]

PGP - Data Science and Engineering

Why Join our PG Data Science and Engineering Course?

Great Lakes PG Data Science and Engineering Course is a 7-month classroom program for fresh graduates and early career professionals looking to build their career in data science & analytics. Candidates from the course are able to transition to roles such as business analysts, data analysts, data engineer, analytics engineer etc. by learning relevant data science techniques, tools and technologies and hands-on application through industry case studies.

What you will get :-

- Ranked #1 in analytics education 

- PG Certificate from Great Lakes 

- World-Class Faculty

- Intensive Bootcamp format

- Hands-on-Learning

- Placement assistance

Program Structure :-

The 7-month PG Data Science Course uses a combination of learning methods that include classroom teaching, hands-on exercises, and sessions with industry practitioners. Classes are conducted on weekdays and are assisted by online discussions and assignments.

[goraviranjan-blog]

Data Science

Data science is a mixture of different tools, algorithms, and principles of machine learning to identify hidden sources. Data science is mainly used to determine and predict the use of prescription tests, predictive causes, and machine learning predictions. SkyWebcom is a leading data science organization in Noida that offers industry professionals with 17 years of IT training experience data science training. 

  Prescriptive analytics-  If you want a product to make decisions with wisdom and change it in dynamic settings, you really need medication. This new area is all about offering. In other words, not only structures and associated results are predicted, but also adopted. The best example of this is Google's self-driving car. The information collected by the vehicles can be used for training. You can run algorithms on this data to pay attention to it. In this way, your vehicle can determine which time passes, which direction slows down or accelerates. 

 Predictive Causal-  If you want a model that can predict the likelihood of a future event, you have to use many prediction factors. For example, when you spend money on a loan, you are concerned about the ability of customers to pay leisure time for future payments. Here you can develop a framework that can make predictions based on the customer's payment history to predict whether your next payment will be on time. 

 Machine learning for making Predictions-  If you have a financial firm's financial data and need to take measurements to determine future trends, machine learning algorithms are the best choice. It falls under the standard of care. This is called supervised training because you already have information about what you should train your car for. For example, a fraud detection model can be trained using a historical record of counterfeit purchases.  

 Machine learning for pattern discovery-  If you don't have the settings to make predictions, you need to find hidden data methods to make meaningful predictions. This is just a product that needs to be tracked because you have no specific functions for the company. The most common method of detecting cereals is incubation. Suppose you work for a telephone company and you need to build a network by building a tower nearby. You can then use the existing method to determine the location of the tower to ensure that all users get the best signal strength.

 Where Do You Fit in Data Science? 

 Data grows everywhere. Many terms related to data mining, cleanup, analysis, and interpretation are often used interchangeably but can be linked to a variety of technical and complex systems. 

Data Analyst- Data analysts compete with "business analysts". They were asked questions that the team had to answer, and then analyzed and evaluated to find out the results associated with the big business strategy. Data analysts are responsible for translating relevant articles into technical data, and making their results available to stakeholders. 

Data Engineer- Data engineers monitor the volume of data that changes data speed. They focus on data center development, data transfer, data transfer development, scope, management, and development. 

 Data Scientist- Data Scientists examine which questions should be answered and where relevant information can be found. They have business and analytics capabilities, as well as the ability to filter, clean, and display data. Companies use data scientists to collect, manage and evaluate large amounts of unprotected data. The result connects with important stakeholders to make strategic decisions within the company.