Full time Internship Training

Data Analytics

Data analytics is the process of examining raw data to uncover patterns, draw conclusions, and make informed decisions. It involves collecting, cleaning, transforming, and modeling data to extract meaningful insights that support business intelligence and strategic planning. This field utilizes various techniques, tools, and methodologies to turn data into actionable information. 

Here's a more detailed breakdown:

  • Raw Data to Actionable Insights:  Data analytics transforms raw, often unstructured, data into usable information that can be applied to improve business operations, customer experience, and decision-making. 
  • Problem Solving:
    Data analytics helps identify and solve problems by revealing trends and patterns within datasets. 
  • Informed Decision Making:
    The insights gained from data analytics empower organizations to make better, more informed decisions. 
  • Multidisciplinary Field:
    Data analytics draws on principles from mathematics, statistics, computer science, and other fields to analyze data effectively. 
  • Data Collection: Gathering data from various sources is the first step. This can include structured data (like databases) and unstructured data (like social media posts). 
  • Data Cleaning and Transformation:
    Raw data often requires cleaning and transformation to remove errors, inconsistencies, and irrelevant information before it can be analyzed. 
  • Data Modeling:
    Data modeling involves creating representations of the data to understand relationships and patterns. 
  • Data Analysis Techniques:
    A range of techniques are used, including statistical analysis, data mining, machine learning, and data visualization. 
  • Visualization:
    Data visualization tools help to present findings in an understandable and impactful way, often using charts, graphs, and other visual aids. 
  • Interpretation and Insights:
    The final step involves interpreting the results of the analysis and drawing meaningful conclusions that can be applied to business strategy. 
  • Descriptive Analytics: Focuses on summarizing past data to understand what has happened. 
  • Diagnostic Analytics: Aims to understand why something happened. 
  • Predictive Analytics: Uses historical data to forecast future trends and outcomes. 
  • Prescriptive Analytics: Suggests actions to take based on the predictions made. 
Data analytics is used in a wide range of industries and applications, including: 
  • Business Intelligence: Improving business operations, identifying new opportunities, and gaining a competitive edge. 
  • Customer Relationship Management: Personalizing customer experiences, improving customer service, and building stronger relationships. 
  • Marketing: Optimizing marketing campaigns, targeting the right customers, and measuring campaign effectiveness. 
  • Finance: Detecting fraud, managing risk, and improving investment strategies. 
  • Healthcare: Improving patient outcomes, optimizing resource allocation, and identifying disease patterns. 
  • Supply Chain Management: Improving efficiency, reducing costs, and optimizing logistics. 

LLMs (Large Language Models) are increasingly used in data analysis to extract insights from textual data, enhance contextual understanding, and automate parts of the analysis process. They can help with tasks like sentiment analysis, text summarization, and identifying relevant features, ultimately improving the accuracy and depth of analysis. 

Here’s a more detailed breakdown:

  1. Analyzing Textual Data: LLMs excel at analyzing unstructured text data, such as social media posts, customer reviews, and documents. They can understand the context of words, identify themes, and even assign sentiment scores to different pieces of text.
  2. Enhancing Contextual Understanding: LLMs can provide a deeper understanding of the context behind data, which can be crucial for making accurate predictions or understanding customer behavior. 
  3. Automating Tasks: LLMs can be used to automate certain data analysis tasks, such as generating SQL queries, creating reports, or extracting insights from data. 
  4. Combining with Traditional Analysis: LLMs can be combined with traditional data analysis techniques to create a more powerful and comprehensive analysis. For example, LLMs can identify relevant features from textual data that can then be used in a predictive model. 
  5. Examples of LLM Applications in Data Analysis: 
  • Natural Language Query Agents: These agents convert natural language questions into SQL or other database queries, making it easier for non-technical users to access and analyze data.
  • Predictive Analytics Agents: These agents use LLMs to analyze historical data and generate predictive insights, such as trend analysis or anomaly detection.
  • Data Visualization Agents: LLMs can suggest appropriate visualizations for data, helping to create informative and engaging charts and graphs.
  1. Building an LLM-powered Data Analyst: Weights & Biases says that LLMs can be used to build an automated data analyst that can handle various tasks, including data cleaning, transformation, and analysis. 
  2. Future Trends: The use of LLMs in data analysis is expected to continue growing, with emerging trends including Bayesian LLMs, big data LLMs, and machine learning integration
  •  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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