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

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Data Science Full Stack Online Course

(1254)
Live Online & Classroom Training
EMI
0% Interest

Explore our Data Science Full Stack Online Training at SLA Institute, where you’ll dive deep into essential data science principles and tools. Gain hands-on experience in data manipulation, statistical analysis, and machine learning algorithms, supported by real-world datasets. Master Python programming, SQL databases, and popular libraries like NumPy, Pandas, and TensorFlow. Our Data Science Full Stack Online Course with 100% Placement Support, equips you to solve complex data problems and make informed decisions using advanced data science techniques. Join us and unleash your potential in the dynamic field of data science!

At SLA Institute, we guarantee placement in a high-paying Developer job with the support of our experienced placement officers. Our Data Science Full Stack Course Syllabus covers all essential topics, providing you with a comprehensive understanding of Data Science Full Stack development.

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Upcoming Batches

Hands On Training
3-5 Real Time Projects
60-100 Practical Assignments
3+ Assessments / Mock Interviews
September 2024
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
September 2024
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 Online Course

The Data Science Full Stack Online Course aims to achieve several goals to help you become proficient in web development using Data Science. Throughout the course, you’ll:

  • Teach comprehensive skills in data manipulation, statistical analysis, and machine learning algorithms.
  • Provide hands-on practice with real-world datasets using Python, SQL, and key data science tools.
  • Develop expertise in data visualization to effectively present insights.
  • Support career readiness with rigorous training and placement assistance.
  • Enable application of advanced data science methods for solving real-world problems.

Future Scope of Data Science Full Stack Online Course

Career Opportunities: Our Data Science Full Stack Online Course at SLA Institute opens up numerous career opportunities in fields such as Data Science, Machine Learning Engineering, Data Analysis, and AI. These roles are in high demand as businesses increasingly rely on data to make decisions and improve operations.

Technological Advancements: Advances in technology continually expand the scope of data science. Our course covers the latest tools and techniques in data manipulation, statistics, and machine learning. Students learn Python, SQL, and advanced data science tools like TensorFlow and PyTorch, preparing them for the evolving tech industry.

Industry Applications: Data science applies to many industries, including healthcare, finance, retail, and marketing. Our curriculum focuses on practical projects and case studies, helping students solve real-world problems specific to these sectors. Whether it’s predicting market trends or improving healthcare outcomes, data skills are crucial across different fields.

Global Demand: There’s a strong global demand for skilled data scientists and analysts. Companies worldwide need professionals who can analyze data to drive innovation and business growth. Our course, combined with job placement support, ensures graduates are well-prepared for these opportunities.

Continuous Learning: Data science is a fast-changing field that requires ongoing learning. Our course teaches foundational skills and encourages graduates to stay updated with new trends and technologies. This ensures they remain competitive and adaptable throughout their careers.

Achieve Your Goals With SLA

SLA builds your future with comprehensive coursework and unparalleled placement support.
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Data Science Full Stack Course Syllabus

Download Syllabus

Join our Data Science Full Stack Online Training at SLA Institute to excel in crafting dynamic applications using Data Science. This extensive program covers fundamental programming skills all the way to advanced frameworks such as Spring and Hibernate. Develop proficiency in database management, master the art of deploying applications effectively, and benefit from dedicated job placement assistance. Launch your career in Data Science and web development through practical projects and expert mentoring at SLA Institute. Enroll today to uncover the vast opportunities in Data Science Full Stack development.

CORE PYTHON
  • Python Introduction & history
  • Color coding schemes
  • Salient features & flavors
  • Application types
  • Language components (variables, literals, operators, keywords…)
  • String handling management
    1. String operations – indexing, slicing, ranging
    2. String methods – concatenation, repetition, formatting
    3. Supporting functions
  • Native data types
    1. List
    2. Tuple
    3. Set
    4. Dictionary
  • Decision making statements
    1. If
    2. If…else
    3. If…elif…else
  • Looping statements
    1. For loop
    2. While loop
  • Function types
    1. Built-in functions
    2. Math functions
    3. User defined functions
    4. Recursive functions
    5. Lambda functions
  • OOPs
    1. Classes and objects
    2. __init__ constructor
    3. Self-keyword
    4. Data abstraction
    5. Data encapsulation
    6. Polymorphism
    7. Inheritance
  • Exception handling
    1. Error vs exception
    2. Types of error
    3. User defined exception handling
    4. Exception handler components
    5. Try block, except block, finally block
  • File handling
    1. How to create a txt file using python
    2. File access modes
    3. Reading and writing data to a txt file
    4. Data operations
DATA SCIENCE PHASE 1
  • Working with PANDAS & NUMPY
    1. PANDAS – data analysis intro
    2. PANDAS – data structures
    3. Series creation types
    4. Data Frame creation types
    5. Accessing data from Series and DataFrame
    6. Data merging
  • Working with PANDAS & NUMPY
    1. Data mapping
    2. Finding duplicates
    3. Removing duplicates
    4. Describing data
    5. Finding null values
    6. Group by function
    7. Sort values
    8. Statistical functions
    9. Reading and writing data from CSV
    10. Data operations on CSV file
    11. Basic visualizations
    12. NUMPY array processing intro
    13. Types of ndarray
  • Numpy attributes
    1. ndim
    2. shape
    3. size
    4. type
  • Shape manipulations
    1. Ravel
    2. Reshape
    3. Resize
    4. Hsplit
    5. Vstack
  • Numpy additional functions
    1. Tile
    2. Eye
    3. Zeros
    4. Ones
    5. Diag
    6. arange
    7. New axis addition
    8. Random number generation
DATA SCIENCE PHASE 2
  • Data science terminologies
  • Exploratory data analysis intro
  • Types of machine learning algorithms
  • Classification and regression intro
  • Prediction and analysis techniques to be used in ML
  • MATPLOTLIB – data visualization
    1. Histogram
    2. Pdf
    3. Adding axes
    4. Adding grid
    5. Adding label
    6. Adding ticks
    7. Setting limits
    8. Adding legend
  • MATPLOTLIB plotting
    1. Bar chart
    2. Pie chart
    3. Heat map
    4. Box plot
    5. Scatter plot
    6. 3d plot
  • SEABORN – advanced color palette visualization
    1. Bar chart
    2. Pie chart
    3. Dist plot
    4. Pair plot
    5. Reg plot
    6. Count plot
    7. Swarmplot
    8. Heat map
    9. Scatter plot
    10. Lm plot
EDA – MACHINE LEARNING –WORKING WITH SCIKIT-LEARN
  • Machine learning algorithm types
    1. Supervised learning
    2. Unsupervised learning
    3. Ensemble learning technique
  • Working flow of dataset
    1. Loading necessary modules
    2. Loading dataset
    3. Feature scaling
    4. Feature extraction
    5. Data standardization
    6. Data normalization
    7. Data manifesting
    8. Model creation
    9. Fitting data models
    10. Model prediction
  • ML algorithms with live demo and mathematical intuition
    1. Linear regression
    2. Logistic regression
    3. Naïve bayes classifier
    4. KNN (K nearest neighbor)
    5. KMC (K means clustering)
    6. Support vector machines
    7. Principal component analysis
    8. Decision tree
    9. Random forest
    10. XGBoost
DEEP LEARNING & AI
  • Neural networks introduction
  • Brain activation functions and layer components
  • Neural network terminologies of ANN, CNN, RNN
    1. Models
    2. Initializers
    3. Optimizers
    4. Layers
    5. Activation functions
    6. Loss functions
    7. Metrics
    8. Model compilations
    9. Model evaluation
    10. Max pooling layers
    11. Edge filters
    12. Back propagations
    13. Early stopping
    14. Epoch
  • Datasets to be used for MLP,ANN, CNN,RNN
    1. Boston house prediction
    2. CIFAR10
    3. CIFAR100
    4. MNIST
    5. FASHION MNIST
    6. IMDB Movie review analysis
  • NLP (Natural Language Processing)
    1. NLTK
    2. NLTK
    3. SPACY
  • COMPUTER VISION
    1. Digital Image Processing using CV2 library
    2. LIVE PROJECTS

Project Practices on Data Science Full Stack Training

Project 1Data Analysis and Visualization

Dive into exploratory data analysis (EDA) using Python and libraries like NumPy and Pandas. Visualize insights with Matplotlib or Seaborn to uncover trends and patterns in real-world datasets.

Project 2Machine Learning Model Development

Apply supervised and unsupervised learning techniques to build predictive models. Use algorithms such as linear regression, decision trees, or clustering methods to solve business problems and optimize outcomes.

Project 3Database Management and Integration

Design and implement a relational database using SQL. Practice data extraction, transformation, and loading (ETL) processes to integrate diverse data sources for analysis and reporting.

Project 4Web Application Development

Combine data science skills with web development frameworks like Flask or Django. Build interactive dashboards or recommendation systems that showcase your data insights in a user-friendly interface.

Prerequisites for learning Data Science Full Stack Online Course

To join our Data Science Full Stack Online Training at SLA Institute, no specific prior knowledge is required. Whether you’re new to programming or have some experience, everyone is welcome. However, it can be helpful to have a basic understanding of:

  • Basic Programming Knowledge: Knowing some programming basics, especially in Python or R, is beneficial.
  • Math and Statistics Understanding: Having a grasp of fundamental math concepts like algebra, calculus, and basic statistics will make learning advanced data science easier.
  • Data Handling Skills: Knowledge of how to work with data, including cleaning, manipulation, and visualization, is useful.
  • Database Basics: Understanding basic database concepts and SQL (Structured Query Language) helps with data storage and retrieval.
  • Curiosity and Problem-Solving Mindset: A keen interest in exploring data-driven solutions and a knack for problem-solving are crucial for success in the course.

Our Data Science Full Stack Online Course is ideal to:

  • 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 Analysts enthusiastic about expanding their expertise
  • Individuals seeking opportunities in Data Science Full Stack

Job Profile in Data Science Full Stack Online Course

In the Data Science Full Stack Online Course at SLA Institute, participants are prepared for various job profiles that require expertise in both front-end and back-end data handling:

  • Data Scientist: Entry-level positions start around ₹500,000 to ₹800,000 per annum. With experience, salaries can range from ₹1,000,000 to ₹2,000,000 annually.
  • Machine Learning Engineer: Salaries for entry-level roles range from ₹600,000 to ₹900,000 per year. Experienced professionals can earn between ₹1,200,000 to ₹2,500,000 annually.
  • Data Analyst: Entry-level salaries typically range from ₹400,000 to ₹700,000 per annum. Senior Data Analysts can earn between ₹800,000 to ₹1,500,000 annually.
  • AI Specialist: Entry-level salaries start around ₹600,000 to ₹900,000 per year. Experienced AI Specialists may earn between ₹1,200,000 to ₹2,500,000 annually, depending on expertise and industry.
  • Full Stack Developer: Junior Full Stack Developers earn approximately ₹400,000 to ₹600,000 per annum. Experienced professionals can command salaries ranging from ₹800,000 to ₹1,500,000 annually.
  • Database Administrator: Entry-level salaries typically range from ₹500,000 to ₹800,000 per year. Senior Database Administrators may earn between ₹1,000,000 to ₹2,000,000 annually, depending on skills and responsibilities.

These roles require proficiency in programming languages like Data Science and R, data visualization tools, and machine learning algorithms taught in the course. Graduates are equipped for rewarding careers in the data-driven industry with competitive salary prospects.

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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 Data Science full-stack?

Data Science full-stack involves mastering both data analysis and machine learning, along with building complete applications. It includes skills in front-end technologies like HTML, CSS, and JavaScript, as well as back-end frameworks such as Python with Django or Flask.

Is Data Science full stack easy or hard?

Data Science full stack can be challenging due to its dual focus on advanced data analysis and application development. However, with dedication and structured learning, mastering both aspects is achievable, offering rewarding career opportunities in data-driven fields.

What is Data Science used for?

Data Science is used to find useful information from large amounts of data. It uses methods like data mining, machine learning, and statistics to discover patterns, make predictions, improve processes, and help make better decisions in different fields and industries.

Is Data Science enough to get a job?

Having skills in Data Science is important for getting jobs in fields like data analysis, machine learning, and AI. However, it’s also important to have practical experience, good problem-solving skills, and the ability to communicate well to secure job opportunities in a competitive market.

Does SLA Institute have HR personnel?

Yes, SLA Institute has an HR personnel who will look into students issues and grievances.

Does SLA Institute support EMI options?

Yes, SLA Institute supports EMI options with 0% interest.

Is Data Science Full Stack a good career?

Data Science Full Stack is a promising career choice that combines skills in data analysis, machine learning, and application development. It offers strong job prospects due to high demand across various industries for professionals who can manage both data and software tasks.

Does SLA Institute provide Lifetime Placement Support?

Yes, SLA Institute provides Lifetime Placement Support to assist students in securing job placements throughout their careers.

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