Data-Science IN chennai

Data science is a broad field that uses systems, algorithms, and scientific techniques to extract valuable information from unstructured and organised data. It combines concepts from computer science, mathematics, and statistics to examine complex datasets, identify patterns, and support well-informed decision-making. Data scientists extract useful information from massive data sources using a variety of techniques, including statistical modelling, machine learning, and data visualisation.

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Instructional Format Online
Live Courses 3 Months
Executive Course Certification
45+ Hiring Partners
Mentorship Support Placement Support
Resume Building Assisting mock

Data-Science Course Overview

A Data Science course offers an in-depth examination of the core principles, methodologies, and technologies employed in the realm of data science. Its goal is to furnish participants with the expertise and capabilities required to effectively analyse, interpret, and extract meaningful insights from a wide range of data sets.

Key-Highlights
  • 60+ hours of live classroom training
  • Projects aligned in domains industries
  • Master more than ten stack tools.
  • live Projects
  • 24*7 Support
  • Certification
  • Individual Consultation Industry Experts
  • Placement Support
  • Mock Job Interviews
  • Zero-Interest EMI
  • Flexible Session Scheduling
  • Discussion through Class

Data-Science Certification

A certification can help you develop in your career by allowing you to apply for positions that expressly call for data science competence or more advanced tasks. Additionally, it can help you stand out in the job market.Within the sector, certifications from respectable schools or organisations are frequently acknowledged and valued. Your credibility and reputation as a data science expert may increase as a result of this acknowledgment.

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Testimonials

Manoj

Data Science

"Completing the Data Science certification program was a transformative experience. The curriculum covered a wide range of topics, from statistics to machine learning, and the hands-on projects provided invaluable practical experience. The instructors were knowledgeable, and the community support enhanced the learning journey. This certification has significantly boosted my confidence and career prospects."

Manoj

Data Science

"The Data Science certification program offered a solid foundation, but some modules felt rushed, and I would have appreciated more in-depth coverage of certain topics. Additionally, greater emphasis on real-world applications would have been beneficial. Nevertheless, the program provided valuable insights and practical skills."

Who Is Eligible to Apply for the Course?
  • Working professionals in the IT industry seeking to transition into AI development or upgrade their skills may be eligible.
  • Students currently enrolled in computer science or related degree programs who wish to enhance their skills may be eligible.
  • Perfect for beginners!"
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Data Science syllabus

Module 1: Introduction to Data Science
  • Definition and scope of data science
  • Overview of the data science life cycle
  • Ethical considerations in data science
Module 2: Data Exploration and Preprocessing
  • Data cleaning and handling missing values
  • Exploratory Data Analysis (EDA)
  • Feature scaling and normalization
  • Data wrangling techniques
Module 3: Statistical Analysis and Hypothesis Testing
  • Descriptive statistics
  • Inferential statistics
  • Hypothesis testing
  • Statistical modeling and regression analysis
Module 4: Machine Learning Basics
  • Introduction to machine learning
  • Supervised and unsupervised learning
  • Model evaluation and selection
Module 5: Predictive Modeling
  • Linear and logistic regression
  • Decision trees and ensemble methods (Random Forests, Gradient Boosting)/li>
  • Model interpretation and evaluation
Module 6: Clustering and Dimensionality Reduction
  • K-Means clustering
  • Hierarchical clustering
  • Principal Component Analysis (PCA)
  • t-Distributed Stochastic Neighbor Embedding (t-SNE)
Module 7: Natural Language Processing (NLP)
  • Text preprocessing and tokenization
  • Bag-of-words and word embeddings
  • Sentiment analysis
  • Named Entity Recognition (NER)
Module 8: Big Data Technologies
  • Introduction to big data concepts
  • Hadoop and MapReduce
  • Apache Spark for large-scale data processing
Module 9: Data Visualization
  • Principles of effective data visualization
  • Tools and libraries for data visualization (e.g., Matplotlib, Seaborn, Tableau)
  • Designing informative and compelling visualizations
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Job Search Pre-interview Planning Resume Building
Guaranteed Interviews

Interviews guaranteed upon project and assignment submission. Interview with more than 45+of our hiring partners.


special access to the Jobs portal on Inetz

Dedicated job portal access with exclusive job applications. Over 45+ hiring partners, such as leading product companies and start-ups, are employing our learners. coached assistance in finding suitable jobs to advance your career.

Prepare for Mock Interviews

Technical experts will conduct several mock interviews with students, after which they will provide advice and helpful for future reference.


Individualized Career Guidance Sessions

Attend one-on-one meetings with career mentors to discuss how to cultivate the necessary abilities and mindset to land a dream job based on the educational background, prior work experience, and future career goals of learners.

Sessions with a Career Focus

Participate in more than 15 live, interactive sessions with an industry expert to learn and gain experience developing the kind of skills hiring managers look for. You will be able to stay on track with your upskilling goal by attending these guided sessions.


Building a LinkedIn Profile and Resume

Our career services team can help you create a stellar resume and LinkedIn profile. You can also learn how to attract the hiring manager's attention during the profile shortlisting stage.

Frequently Asked Questions

What are the key skills required for a career in Data Science?

Key skills include proficiency in programming languages (e.g., Python, R), statistical analysis, machine learning, data preprocessing, data visualization, and domain-specific knowledge. Strong problem-solving and communication skills are also essential.

Do I need a specific educational background for a career in Data Science?

While a background in computer science, statistics, or a related field is common, there is no strict educational requirement. Many Data Scientists come from diverse educational backgrounds, and practical skills and experience often weigh heavily in hiring decisions.

How does machine learning relate to Data Science?

Machine learning is a subset of Data Science that focuses on creating algorithms and models that enable systems to learn patterns from data. Data Science encompasses a broader range of activities, including data exploration, statistical analysis, and data visualization.

How important is domain knowledge in Data Science?

Domain knowledge is valuable in Data Science as it helps Data Scientists understand the context of the data they are working with. Having domain expertise enhances the ability to ask relevant questions, interpret results, and create actionable insights.