DATA SCIENCE
With GENERATIVE-AI

(PYTHON, ML, DL, NLP)

14,000 Students  Placed

40,000 Students Trained

200+ Placement Companies

14 Years of Students Trust​

no.1

Classrooms

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Offered Programs

JOB ORIENTED INTENSIVE PROGRAM (JOIP)​

1. Any Graduate

2. By Realtime Experts

3. Weekly Mock Interviews

4. Resume Preparation

5. Up to 3 Live Projects

6. 4 – 6 Hrs Daily Training

7. Offline / Online

8. Training Completion Certificate

9. Backup videos for 4mnths

JOB ORIENTED INTERNSHIP PROGRAM (JOIP)

1. Any Graduate

2. Must attend Mock Test

3. By Realtime Experts

4. practical in Company 

Environment (Hitech City

5. Live Project work assigned 

by IT Staff (Hitech City)

6. Preferred Offline / Online

7. Internship Completion Certificate

8. 6 – 8 Hrs Daily

9. Backup videos for 12 months

Why attend the Demo?

Data Scientist

$110k Median Base Salary

Data Analyst

$106k Median Base Salary
4.3/5.0 Level of job satisfaction

Analytics Manager

2000 openings
$112k Median Base Salary
4.3/5.0 Level of job satisfaction

WHY THESE JOBS IN DEMAND

Influx of data to be captured, cleaned and analyzed Technology is struggling to keep up Academic institutions scrambling to prepare students Candidates in short supply Limited technological capacity overwhelming demand

Top Majors for Data Scientist

Statistics Math Quantitative It Operations Research Highly technical social Science concentrations

Top Majors for Data Scientist

in-demand soft skills include intellecual curiosity Openness to Learning new things Ability to solve problems in unique ways Passion for innovation

Must have skills and expertise:

Must have skills and expertise in Classical statistics Bayesian statistics Linear algebra for machine learning applications in-depth knowledge of R and Secondarily SAS Coding in Python, SQL, NoSQL and Hadoop Knowledge of the Hadoop Platform Experience with spark Hive or Pig

Why attend the Demo?

Who is this Demo for?

Designed for Everyone Looking for a Software Job

Graduates & Final Year students of all branches

Mech, Civil, Chem, ECE, EEE, CS/IT etc.

Non-IT Professionals looking to shift to IT

Get higher salaries you deserve for your capabilities.

Those worrying about low CGPA/backlogs

Your academic performance will not hold you back.

IT Professionals looking for Product Development roles

Switch from support/testing roles to high-paid developer roles.

Your Seniors Got Placed. It’s Your Turn Now!

Our Students Are Placed In

companies

Course Objectives

The main objective of AI (Artificial Intelligence) and Data Science is to extract meaningful insights, patterns, and knowledge from data to make informed decisions, automate processes, and develop predictive models or algorithms that emulate human intelligence or behavior. Specifically, the primary goals can be summarized as follows:

Data Understanding and Processing: Collect, clean, preprocess, and analyze large volumes of data to extract valuable insights and patterns. This involves understanding the structure, quality, and characteristics of the data.

Pattern Recognition and Prediction: Develop machine learning models and algorithms that can recognize patterns within data and make predictions or classifications based on those patterns. This includes predictive analytics, classification, clustering, and regression.

Automation and Efficiency: Automate tasks and processes using AI and Data Science techniques to improve efficiency, reduce manual intervention, and enable real-time decision-making in various domains like manufacturing, healthcare, finance, etc.

Decision Support Systems: Build systems that assist human decision-making by providing accurate and timely insights derived from data analysis. These systems can be used in various fields to support critical decision-making processes.

Innovation and Problem-Solving: Drive innovation by applying AI and Data Science methodologies to solve complex problems, discover new correlations, and optimize processes that were previously challenging or impossible for humans to tackle effectively.

Personalization and Customization: Create personalized experiences for users by leveraging data to understand preferences, behaviors, and needs, leading to tailored recommendations, user interfaces, and services.

Ethical and Responsible AI Development: Ensure the ethical and responsible development and deployment of AI systems by addressing issues of bias, fairness, transparency, privacy, and accountability.

Continuous Learning and Improvement: Continuously improve AI models and algorithms by incorporating new data, feedback, and advancements in technology to enhance accuracy, robustness, and adaptability. Overa

Overall, the primary objective of AI and Data Science is to harness the power of data and intelligent algorithms to drive innovation, optimize processes, and solve complex problems across various industries, ultimately improving decision-making and creating value.

After Completing this course, you will be able to:

FAQ's

AI refers to the development of computer systems that can perform tasks that typically require human intelligence. It involves machines simulating cognitive functions like learning, problem-solving, understanding natural language, and recognizing patterns.

Data Science is an interdisciplinary field that involves extracting insights and knowledge from structured and unstructured data. It combines elements of mathematics, statistics, computer science, and domain expertise to analyze data and make data-driven decisions.

Data Science involves data collection, data cleaning and preprocessing, exploratory data analysis, feature engineering, model building (machine learning or statistical models), model evaluation, and deploying models into production.

Machine Learning is a subset of AI that focuses on enabling machines to learn from data without explicit programming. It includes supervised learning, unsupervised learning, and reinforcement learning techniques.

Selenium

Selenium is arguably the most popular automated software testing tool among web developers and testers. It was first released in 2004, and it has evolved considerably since then. The latest version of Selenium consists of several components, including Selenium IDE and Selenium WebDriver.

 

Python and R are the most commonly used programming languages in AI and Data Science due to their rich libraries and community support for data manipulation, analysis, and machine learning.

AI and Data Science find applications in various fields such as healthcare (diagnosis, personalized medicine), finance (fraud detection, algorithmic trading), marketing (customer segmentation, recommendation systems), manufacturing (predictive maintenance, quality control), and more.

Ethical considerations include issues related to data privacy, bias in algorithms, transparency, accountability, and fairness in decision-making.

Starting with foundational courses in statistics, programming, and machine learning, followed by hands-on projects and real-world applications, is a typical path. There are various online courses, bootcamps, and degree programs available for beginners.

The future of AI and Data Science is promising, with continued advancements in technology, increased adoption across industries, and the development of innovative applications in various domains.

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