Software Training Institute in Chennai with 100% Placements – SLA Institute

Easy way to IT Job

Data Science Full Stack Training

(1987)
Live Online & Classroom Training
EMI
0% Interest

Our Data Science Full Stack Training will make students learn some of the most in-demand concepts in Data Science Full Stack such as – Core Python, Data Science Phase 1, Data Science Phase 2, EDA – Machine Learning – Working with Scikit- Learn, Deep Learning & AI etc. This curriculum will surely make students experts in the concept of Data Science Full Stack in a shorter span of time. Our Data Science Full Stack Course with 100% placement support is curated with the help of leading experts from the IT industry, which makes our Data Science Full Stack Course up-to-date in accordance with the latest trends.

Our SLA Institute is guaranteed to place you in a high-paying Data Scientist and other Data Science Full Stack related jobs with help of our experienced placement officers. SLA Institute’s Course Syllabus for Data Science Full Stack covers all topics that are guaranteed to give you a complete understanding of the Data Science Full Stack Course.

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+91 89256 88858

Upcoming Batches

Hands On Training
3-5 Real Time Projects
60-100 Practical Assignments
3+ Assessments / Mock Interviews
January 2025
Week days
(Mon-Fri)
Online/Offline

2 Hours Real Time Interactive Technical Training 

1 Hour Aptitude 

1 Hour Communication & Soft Skills

(Suitable for Fresh Jobseekers / Non IT to IT transition)

Course Fee
January 2025
Week ends
(Sat-Sun)
Online/Offline

4 Hours Real Time Interactive Technical Training

(Suitable for working IT Professionals)

Course Fee

Save up to 20% in your Course Fee on our Job Seeker Course Series

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Quick Enquiry

Placement

100% Assistance

Learning

Job-Centered Approach

Timings

Convenient Hrs

Mode

Online & Classroom

Certification

Industry-Accredited

This Course Includes

  • FREE Demo Class
  • 0% EMI Loan Facilities
  • FREE Softskill & Placement Training
  • Tie up with more than 500+ MNCs & Medium Level Companies
  • 100% FREE Placement Assistance
  • Course Completion Certificate
  • Training with Real Time Projects
  • Industry-Based Coaching By MNC IT Professionals
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Expected Criteria for Assured Placement

The following criteria help the placement team guide the candidates to get placed immediately after the course completion through SLA Institute.

  • 80% of coursework completion helps us arrange interviews in required companies.
  • 2 or 3 projects to be done for the selected course to ace the technical round effectively.
  • Ensure attending the placement training right from the first day of the selected course.
  • Practice well with resume building, soft skill, aptitude skill, and profile strengthening.
  • Utilize the internship training program at SLA for the complete technical skills.
  • Collect the course completion certificate and update the copy to the placement team.
  • Ensure your performance indicator meets the expectation of top companies.
  • Always be ready with the updated resume that includes project details done at SLA.
  • Enjoy unlimited interview arrangements along with internal mock interviews.
Have Queries? Ask our Experts

+91 89256 88858

SLA's Distinctive Placement Approach

1

Tech Courses

2

Expert Mentors

3

Assignments & Projects

4

Grooming sessions

5

Mock Interviews

6

Placements

Objectives of Data Science Full Stack Training

The primary objective of our Data Science Full Stack Course is to make enrolled candidates experts in Data Science Full Stack. This Data Science Full Stack Course will make students grow into successful and most in-demand Data Scientist, and more. SLA Institute’s Data Science Full Stack Course Curriculum is loaded with some of the most useful and rare concepts that will surely give students a complete understanding of Data Science Full Stack. So, some of those concepts are discussed below:

  • The syllabus starts with fundamental concepts such as- Core Python – String handling management, Native Data Types, Decision Making Statement, Looping Statements, Function Types, OOPs, Exception Handling, File Handling etc.
  • The syllabus then explores Data Science Full Stack further in two phases- in Data Science Phase 1 students will learn about Working with PANDAS & NUMPY, Numpy attributes, Shape manipulations, Random Number generation etc; then in the Data Science Phase 2 students will learn about – Data science terminologies, Exploratory data analysis intro, Types of machine learning algorithms, MATPLOTLIB Plotting etc.
  • After that the syllabus will move towards the advanced topics where students will learn about – Machine learning algorithm types, Working flow of dataset, Machine Learning algorithms with live demo and mathematical intuition; Deep Learning & AI – Neural networks introduction, Brain activation functions and layer components, Neural network terminologies of ANN, CNN, RNN, Natural Language Processing etc. 

Future Scope for Data Science Full Stack Training

The following are the scopes available in the future for the Data Science Full Stack Course:

  • Advanced AI and Machine Learning: Continued advancements in AI and ML will open up opportunities for sophisticated applications, such as deep learning, reinforcement learning, and autonomous technologies.
  • Big Data Analytics: The rise in data volumes will necessitate advanced big data technologies and techniques to manage, analyze, and extract meaningful insights from extensive datasets.
  • Cloud-Based Data Solutions: The expansion of cloud computing for data storage and processing will drive the need for expertise in cloud platforms like AWS, Azure, and Google Cloud, along with cloud-native data tools.
  • Real-Time Data Processing: Growing demand for real-time analytics in fields such as IoT, finance, and social media will highlight the importance of skills in streaming data technologies and real-time processing frameworks.

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SLA builds your future with comprehensive coursework and unparalleled placement support.
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Project Practices on Data Science Full Stack Training

Project 1Predictive Analytics for Sales Forecasting

Build a model to forecast future sales based on historical data and trends using Python and SQL.

Project 2Customer Segmentation

Apply clustering algorithms to group customers based on their behavior and demographics with Python and SQL.

Project 3Sentiment Analysis of Social Media Data

Analyze social media data to determine public sentiment using Python and web scraping tools.

Project 4Recommendation System

Develop a system to recommend products or content to users based on their past interactions with Python.

Prerequisites for learning Data Science Full Stack Course 

SLA Institute does not demand any prerequisites for any course at all. SLA Institute has courses that cover everything from the fundamentals to advanced topics so whether the candidate is a beginner or an expert they will all be accommodated and taught equally in SLA Institute. However having a fundamental understanding of these concepts below will help you understand Data Science Full Stack better, However it is completely optional:

  • Fundamental Programming Skills: A solid understanding of core programming principles such as variables, loops, conditionals, and functions is essential. Proficiency in a language like Python or R can be particularly beneficial.
  • Mathematics and Statistics: A good grasp of mathematics and statistics is vital for analyzing data. This includes knowledge in areas such as linear algebra, calculus, probability, and statistical inference.
  • Data Structures and Algorithms: Familiarity with fundamental data structures (e.g., arrays, lists, trees) and algorithms (e.g., sorting, searching) is crucial for effective data handling and processing.
  • SQL and Database Management: Understanding SQL for querying relational databases and grasping key database concepts (e.g., tables, joins, indexing) is essential for data management and retrieval.

Our Data Science Full Stack Course is fit for:

  • Students eager to excel in Data Science Full Stack 
  • Professionals considering transitioning to Data Science Full Stack careers
  • IT professionals aspiring to enhance their Data Science Full Stack skills
  • Data Scientists who are enthusiastic about expanding their expertise.
  • Individuals seeking opportunities in the Data Science Full Stack field.

Job Profile for Data Science Full Stack Training

After finishing the Data Science Full Stack Training, students will be placed in various organizations through SLA Institute. This section will explore the various range of job profiles in which students can possibly be possible be placed as in the Data Science Full Stack sector;

  • Data Scientist: Data Science Full Stack Training will train students into successful Data Scientists who use statistical techniques and machine learning to analyze complex datasets and provide insights for decision-making.
  • Data Analyst: Data Science Full Stack Training will turn students into Data Analyst who interprets and visualizes data to extract actionable insights and support strategic decisions.
  • Machine Learning Engineer: Data Science Full Stack Training will make students into skilled Machine Learning Engineer who develops and deploys machine learning models and algorithms to address complex issues and enhance system performance.
  • Data Engineer: The SLA Institute will train students into a skilled Data Engineer who constructs and maintains data pipelines, databases, and ETL processes to ensure effective data management and analysis.
  • Business Intelligence (BI) Developer: The SLA Institute will make students into BI Developers who design and manage BI tools and reporting systems to facilitate data-driven business decisions.
  • Data Architect: The SLA Institute will provide students with enough resources that it will turn students into Data Architect who creates and oversees the data infrastructure and architecture for efficient data storage, access, and management.
  • Healthcare Data Scientist/Analyst: Focuses on analyzing healthcare data to enhance patient care, optimize treatments, and support medical research.
  • Quantitative Analyst: Utilizes mathematical and statistical methods to analyze financial data and assist in investment decisions.
  • Data Science Consultant: Offers expert guidance on data science projects, helping organizations make the most of their data.
  • Data Visualization Specialist: Creates visualizations to present data clearly and make complex information more comprehensible.
  • AI Research Scientist: Conducts research to advance artificial intelligence and machine learning technologies, developing innovative models.
  • Operations Analyst: Applies data analysis to enhance business operations, improve efficiency, and support strategic planning.

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The Placement Process at SLA Institute

  • To Foster the employability skills among the students
  • Making the students future-ready
  • Career counseling as and when needed
  • Provide equal chances to all students
  • Providing placement help even after completing the course

Data Science Full Stack Course FAQ

What is the purpose of a Data Science pipeline in a project?

A Data Science pipeline streamlines and automates the stages of data handling and analysis—such as data collection, preprocessing, transformation, modeling, and evaluation—ensuring an orderly and efficient process for extracting insights.

How do machine learning models enhance their performance over time?

Machine learning models improve by undergoing repeated training with updated data, optimizing algorithms, and adjusting parameters, which allows them to better identify patterns and trends, thus increasing their accuracy and effectiveness.

Why is data visualization important in Data Science?

Data visualization converts complex datasets into visual formats like charts and graphs, making it simpler to identify trends, patterns, and anomalies, and to effectively convey insights to stakeholders.

How do cloud computing services benefit Data Science projects?

Cloud computing services offer scalable infrastructure for data storage, processing, and analysis, along with tools for managing extensive datasets and deploying machine learning models, enhancing flexibility and efficiency in data science endeavors.

How many branches does the SLA Institute have right now?

At the moment, the SLA Institute has two branches – one is in K.K.Nagar and another is in OMR, Navalur. 

Is EMI an option in the SLA Institute?

Yes, EMI is an option in SLA Institute which is offered with 0% interest. 

How long is the Data Science Full Stack Training ?

The Data Science Full Stack Training is 4 months long,

Is it easy to learn Data Science Full Stack Training?

The Data Science Full Stack Training can be easy to grasp with the help of our experienced trainers and up-to-date syllabus. 

On Average Students Rated The Data Science Full Stack Course 4.70/5.0
(1987)
See More Reviews

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