Easy way to IT Job

Data Science Full Stack Developer Course Syllabus

4.50
(5142)

This data science full-stack course covers a comprehensive syllabus designed to equip you with the skills required for success in the field. It includes topics like foundations of data science, statistical analysis, machine learning, deep learning, big data technologies, web development, data visualization, database management, cloud computing fundamentals, etc. By the end of our Data Science Full Stack training in Chennai at SLA, you will have the skills and hands-on exposure to tackle complex data science projects, from data acquisition and analysis to model development and deployment in IT services.

Download our Data Science Full Stack syllabus PDF for the best Data Science Full Stack Training Institute in Chennai.

DURATION
Real-Time Location Services
3 Months
JOB READY
Syllabus
CERTIFIED
Courses

Let's take the first step to becoming an expert in Data Science Full Stack

Click Here to Get Started

100% Placement
Assurance

What Learning at SLA gives you

  • Technology Training
  • Aptitude Training
  • Learn to Code (Codeathon)
  • Real Time Projects
  • Learn to Crack Interviews
  • Panel Mock Interview
  • Unlimited Interviews
  • Life Long Placement Support

Breakdown of Data Science Full Stack Course Fee and Batches

Hands On Training
3-5 Real Time Projects
60-100 Practical Assignments
3+ Assessments / Mock Interviews
May 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
May 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

Learn More

Syllabus for The Data Science Full Stack Course

Download Syllabus
CORE PYTHON

1

  • 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

2

  • 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

3

  • 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

4

  • 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

5

  • 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

Want more details about the Data Science Full Stack Syllabus?

Fill out the form, and our counsellors will get in touch with you at your preferred time. You can have all your queries answered. Once you decide that SLA is the perfect fit for your training needs, our counselors will guide you through the process every step of the way.

Course Schedules

PDF Course Syllabus

Course Fees

CRM System Testing

or any other questions...

The SLA way to Become
a Data Science Full Stack Expert

Enrollment

Technology Training

Coding Practices
Realtime Projects

Placement Training

Aptitude Training
Interview Skills
CRM System Testing

Panel Mock
Interview

Unlimited
Interviews

Interview
Feedback

100%
IT Career

Google Reviews

Rating
4.8
1,053 Google reviews

Aswin Pandiyan

It's my genuine review i searched many institutes to do software testing course and finally landed in SLA. My counselor Bala backed me and my…
Click here for Full Review

Hari Krishnan

I am from Mechanical background. I have no command over programming before joining SLA Such a wonderful place to learn and achieve your goals.Having wonderful…
Click here for Full Review

Venkatesh

I joined SLA for python course one year back, and i got selected as software developer with decent salary, with after one year of experience,…
Click here for Full Review

Ganesan Vasu

I'm from non-IT background, but always wanted to be in IT, SLA make my dream true and I don't know how many will see this…
Click here for Full Review

Discover What Our Students Have To Say

See More Reviews

Related Blogs for
The Data Science Full Stack Course

Our counselors will share the Syllabus PDF with you via Email / Whatsapp

Just a minute!

If you have any questions that you did not find answers for, our counsellors are here to answer them. You can get all your queries answered before deciding to join SLA and move your career forward.

We are excited to get started with you

Give us your information and we will arange for a free call (at your convenience) with one of our counsellors. You can get all your queries answered before deciding to join SLA and move your career forward.