Full time Internship Training

Data Science:

Data science is a multidisciplinary field focused on extracting knowledge and insights from data using scientific methods, techniques, and algorithms. It involves collecting, cleaning, analyzing, and interpreting large datasets to uncover patterns, make predictions, and support decision-making. Data science draws on various fields like computer science, statistics, mathematics, and domain expertise. 

Here's a more detailed breakdown:

  • AI is the overarching field of creating machines that can perform tasks that typically require human intelligence. 
  • It includes various techniques and approaches, such as expert systems, natural language processing, computer vision, and machine learning. 
  • AI aims to develop systems that can reason, learn, and adapt to new situations. 
  • Machine learning is a specific methodology within AI that focuses on enabling machines to learn from data. 
  • It involves training algorithms on data so that they can identify patterns, make predictions, and improve their performance over time. 
  • ML algorithms can be supervised (trained on labeled data), unsupervised (trained on unlabeled data), or reinforcement learning (trained through trial and error). 
  • Healthcare: Predicting disease outbreaks, personalizing treatment plans, and improving patient care. 
  • Finance: Detecting fraud, assessing risk, and optimizing investment strategies. 
  • Marketing: Personalizing customer experiences, optimizing marketing campaigns, and understanding customer behavior. 
  • Transportation: Optimizing traffic flow, predicting travel times, and improving logistics. 

In essence, data science is about using data to gain a deeper understanding of the world and to make better decisions. 

Large Language Models (LLMs) are powerful tools with significant applications in data science. They can automate tasks like data analysis, code generation, and text summarization, freeing up data scientists for more strategic work. LLMs can also uncover hidden insights from unstructured data and enhance predictive modeling by integrating LLM-generated insights. 
 
Here’s a more detailed look at how LLMs are used in data science:
  • Uncovering hidden patterns: LLMs can analyze vast amounts of text data to identify trends and relationships that might be missed by traditional methods. 
  • Automating data analysis:
    LLMs can automate tasks like data cleaning, feature engineering, and even generate SQL queries to extract data. 
  • Improving model interpretability:
    By analyzing the text generated by LLMs, data scientists can gain a better understanding of how models arrive at their predictions. 
  • Writing code: LLMs can generate code in various programming languages based on natural language descriptions, accelerating the development process. 
  • Code completion: LLMs can predict and suggest code snippets, helping data scientists write code more efficiently. 
  • Code debugging: LLMs can assist in identifying and fixing errors in code. 
  • Text summarization: LLMs can generate concise summaries of large text documents, saving time and effort. 
  • Sentiment analysis:
    LLMs can analyze the sentiment expressed in text data, providing insights into customer opinions and feedback. 
  • Question answering:
    LLMs can be used to answer questions based on textual data, providing quick and efficient access to information. 
  • Chatbots:
    LLMs can power chatbots for customer service, internal support, or other applications where natural language interaction is needed. 
  • Combining LLMs and traditional models: LLMs can be used to enhance predictive models by incorporating insights derived from text data. 
  • Feature engineering:
    LLMs can be used to generate new features from text data that can be used in predictive models. 
  • Model interpretability:
    LLMs can help explain the predictions made by traditional models, improving trust and transparency. 
  • Not a replacement for human expertise: While LLMs can automate many tasks, they are not yet able to fully replace human data scientists, especially in complex or novel situations. 
  • Potential for bias: LLMs are trained on data and can inherit biases present in that data. 
  • Hallucinations: LLMs can sometimes generate inaccurate or nonsensical information, which needs to be carefully monitored. 

In conclusion, LLMs offer a wide range of capabilities that can significantly enhance the work of data scientists. By automating tasks, uncovering hidden insights, and integrating with other AI techniques, LLMs are transforming the field of data science and paving the way for more efficient and effective data-driven decision-making

  •  Any degree graduates can apply.
  • Must have passed out from 2018 – 2025 graduates and Percentage is not mandatory.
  • Must agree with the company terms and conditions to act professionally and develop the career.
  • Must be able to work anywhere in Karnataka companies in top or reputed companies in every district.
  • Fresher Level – (0-1 Year): Min 5,00,000 lakhs per year – 6,00,000 lakhs per year 
  • Mid-Level – (1 – 3 year): Min 6,00,000 lakhs per year – 9,00,000 lakhs per year 
  • Senior Level – (3 – 6 year): Min 9,00,000 lakhs per year – 12,00,000 lakhs per year 
Internship Training Hrs./Day: 8 hrs./day (Morning – 4 hrs. & Evening – 4hrs.)
Days in a week: Monday, Tuesday, Thursday & Friday 
Mock Interviews (By MNC companies Staff): Every Wednesday & Saturday: Min 20 mins – Max 30 mins 
Mock Aptitude & Coding tests: Wednesday & Saturday: Min 1 hr. – Max 2 hrs.
Recap of the Training Sessions: Wednesday: 3hrs/day
Internship Practical Training Mode: Online Virtual
* Trainee must have a high-end internet connection during the training period, attend the internship training and present the min 90 % of whole attendance, follow the strict discipline during the training period, complete the practice, assignments and must be updated on time to develop the skills, pass the exams and mock interviews with a percentage of 90% to clear the main four interviews.
* Trainee should be well behaved at all the activities and become a professional at the end of the training.
* Trainees will be selected easily and must join after receiving the joining letter from the company.
 
Internship Training Fees: INR 60,000 Inclusive 18% GST 
Payment Options:
 
Option 1: Without Educational Loan
Installment 1: Down payment of 20%: INR 12,000 at the time of enrollment 
Installment 2: 50% of the remaining balance during 1st week of Internship Training: INR 24,000
Installment 3: Within 21 days from the date of second installment: INR 24,000
No Cash Accepted, Only Company Transaction online payment. 
 
Option 2: With Educational Loan
Apply for the Bajaj Finance in educational training loan process and must have a minimum credit score of 700+ to get approval for the 80% amount of INR 48,000.
Once Approved, Down payment of 20%: INR 12,000 at the time of enrollment and loan processed amount is credited to the company account.
You need to repay back the loan amount in 8 months for a monthly EMI of INR 6,000/month.
 
Once the payment is done, Invoice GST will be shared to you. 

Client Hiring Details:

  • We’re associated with fortune 500 companies and the interviews will be scheduled immediately after completion of the training within a week.
  • With over 15 mock interview calls with MNC staff, you’ll ease the interviewer and grab the opportunity to work at high scale in reputed companies.

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