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

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Data Science Full Stack Course in Chennai

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Live Online & Classroom Training
EMI
0% Interest

Our Data Science Full Stack Training in Chennai 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, 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 in Chennai.

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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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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 Course in Chennai

The primary objective of our Data Science Full Stack Course in Chennai 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 Science Full Stack Developers, 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 begins with fundamental topics like – Core Python – Python Introduction & history, Color coding schemes, Salient features & flavors, Application types, data types, OOPs etc.
  • The syllabus then moves a bit deep into Data Science by exploring Data Science in several phases namely, Phase 1 & 2 where students will learn – PANDAS – data analysis intro,  PANDAS – data structures, NUMPY, Data science terminologies, Exploratory data analysis intro, map, plots charts etc.
  • The syllabus finally moves to the advanced topics such as – Machine learning algorithm types, Working flow of dataset, Principal component analysis, Decision tree, Random forest, Neural networks introduction, Brain activation functions and layer components etc. 

Future Scope for Data Science Full Stack Course in Chennai

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

  • Growing Demand: There’s a rising need for professionals who can handle both data science and software engineering tasks. Companies are looking for individuals capable of building and deploying machine learning models into operational systems.
  • AI and ML Integration: With artificial intelligence and machine learning becoming pervasive across sectors, the demand for Data Science Full Stack experts who can effectively develop and implement AI/ML models is increasing.
  • Automation and Efficiency: Organizations are prioritizing automation and efficiency through data-driven insights. Data Science Full Stack specialists play a crucial role in creating and deploying automated solutions.
  • Cross-disciplinary Skills: Proficiency in bridging data science, software engineering, and business domains makes Data Science Full Stack professionals highly valuable. They excel in communicating with both technical and non-technical stakeholders.

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Data Science Full Stack Course Syllabus

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SLA Institute’s Data Science Full Stack Course Syllabus comes with 100% placement support so students will be guaranteed a placement in an esteemed organization. In addition to that the Data Science Full Stack Course Syllabus is also carefully curated with the help of leading professionals and experts from the IT industry with so many hours invested in it. So, everything that our students learn in the Data Science Full Stack course is fully up-to-date to the current trends in the IT industry, which increases their chances of getting employed.

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 1Predictive Modeling

Develop models to predict customer churn and perform sentiment analysis on reviews using NLP.

Project 2Data Visualization

Create interactive dashboards for visualizing sales trends and COVID-19 data analysis.

Project 3Recommendation Systems

Implement movie and music recommendation systems using collaborative and content-based filtering.

Project 4Time Series Analysis

Forecast stock prices and analyze electricity consumption patterns using ARIMA or Prophet.

Prerequisites for learning Data Science Full Stack Course in Chennai

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:

Programming Skills: Proficiency in at least one programming language is crucial. Python is widely used in data science for its versatility with data manipulation libraries (like Pandas, NumPy) and machine learning frameworks (such as Scikit-Learn, TensorFlow, PyTorch).

Mathematics and Statistics: A strong grasp of foundational mathematics (including calculus, linear algebra) and statistics (such as probability theory, hypothesis testing, regression analysis) is essential for effective data analysis and modeling.

Data Handling and Manipulation: Familiarity with data handling techniques like data cleaning, transformation, and manipulation using tools like SQL, Pandas, or similar libraries is important.

Machine Learning Fundamentals: Basic knowledge of machine learning concepts and algorithms is beneficial. This includes understanding supervised and unsupervised learning, model evaluation metrics, and the ability to implement basic machine learning models.

Our Data Science Full Stack Course in Chennai 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 are enthusiastic about expanding their expertise.
  • Individuals seeking opportunities in the Data Science Full Stack field.

Job Profile for Data Science Full Stack Course in Chennai

After finishing the Data Science Full Stack Course in Chennai, 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 Course will train students into successful Data Scientist who analyze intricate datasets to extract insights and construct predictive models using statistical methods, machine learning algorithms, and data visualization tools.
  • Machine Learning Engineer: Data Science Full Stack Course will make students into skilled Machine Learning Engineer who designs and implements machine learning models for deployment in production systems, emphasizing scalability, performance optimization, and reliability.
  • Data Engineer: Data Science Full Stack Course will make students into a Data Engineer who develops and manages data pipelines, warehouses, and ETL processes to ensure data availability, integrity, and accessibility for analytics and decision-making.
  • Full Stack Developer with Data Focus: The SLA Institute will turn students into Full Stack Developers with Data Focus who integrate data-driven functionalities and analytical features into web applications, handling both front-end user interfaces and back-end data processing.
  • AI Engineer: The SLA Institute will make students into skilled AI Engineers who specialize in the development and deployment of AI solutions, including machine learning models and applications of natural language processing, often leveraging scalable cloud environments.
  • Business Intelligence (BI) Developer: The SLA Institute will train students into successful  Business Intelligence (BI) Developers who create interactive dashboards and reports to visualize data insights, enabling business stakeholders to derive actionable intelligence and make informed decisions.
  • Data Analyst: Examines data to uncover patterns, trends, and correlations that inform strategic business decisions and operational strategies.
  • Research Scientist: Conducts research in artificial intelligence, machine learning, and data science domains, contributing to advancements in academic or industry research settings.
  • Data Consultant: Advises organizations on data strategy, analytics implementation, and the utilization of data-driven insights to drive business outcomes and competitive advantage.
  • Product Manager (AI/ML): Manages the lifecycle of AI and machine learning products from inception through to deployment, ensuring alignment with business objectives and user needs.
  • Data Architect: Designs and implements data systems and structures that support scalability, security, and efficient management of large datasets, ensuring data integrity and accessibility.
  • Quantitative Analyst (Quant): Applies mathematical and statistical models to solve complex financial and risk management problems, typically within banking, finance, or trading sectors.
  • Healthcare Data Scientist: Applies data science techniques to healthcare data, including electronic health records and medical imaging, to enhance patient outcomes and operational efficiencies in healthcare settings.
  • IoT Data Scientist: Analyzes data from Internet of Things (IoT) devices to derive insights and optimize device performance and usability, contributing to IoT ecosystem advancements.
  • Cybersecurity Data Scientist: Utilizes data science methodologies to identify, analyze, and mitigate cybersecurity threats, safeguarding digital assets and ensuring data integrity and security.

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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

Which programming languages are essential for the Data Science Full Stack course?

Proficiency in Python is highly recommended because of its robust libraries for data manipulation (e.g., Pandas, NumPy), machine learning (e.g., Scikit-Learn, TensorFlow), and web development frameworks (e.g., Flask, Django). Knowledge of SQL for database management and querying is also advantageous.

Is prior experience with cloud platforms necessary for this course?

While not always mandatory, familiarity with cloud platforms such as AWS (Amazon Web Services), Azure, or Google Cloud can be beneficial. The course may cover topics related to deploying data science applications in cloud environments, so having a basic understanding of cloud concepts can be useful.

What types of machine learning algorithms will I study in this course?

The course typically includes a variety of machine learning algorithms, encompassing supervised learning (e.g., regression, classification), unsupervised learning (e.g., clustering, dimensionality reduction), and potentially reinforcement learning. Deep learning techniques utilizing neural networks may also be part of the curriculum.

Are there specific software tools or environments that I need to install for the course?

Depending on the course requirements, you may need to install essential tools such as Python (often recommended through the Anaconda distribution), Jupyter Notebook or JupyterLab for interactive coding, and possibly IDEs for software development (e.g., PyCharm, Visual Studio Code). Additionally, Docker for containerization and version control tools like Git might be necessary components of the course setup.

Does SLA Institute have an EMI option?

Yes, SLA Institute offers EMI as an option in payment with 0% interest. 

How does the SLA Institute deal with issues among students?

The SLA Institute deals with issues among students through the appointed HR personnel.

Is it easy to learn Data Science Full Stack Course?

The Data Science Full Stack Course will be easy to grasp once students have a good grasp on the basics. But since the SLA Institute teaches all the courses from the fundamentals, students will be able to easily learn Data Science Full Stack under the guidance of experienced trainers.

How long is the Data Science Full Stack Course?

The Data Science Full Stack Course in SLA Institute is over 4 months long.

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