Full Stack Data Science

Statistics | Python| Visualization| Forecasting | Segmentation | Natural Language Processing | Image Processing

Data Wrangling | Data Cleaning | Data Visualization | Reports | Dashboard

Big Data| Volume | Data Pipe Line | Hadoop | PySpark

Domains Specialization | Business Problem & Solutions

About the course

Full Stack Data Science is a combination of Big Data Engineering, Business Intelligence and Data Analytics. Our Full Stack Data Science course helps develop deep understanding of business problems and strong domain knowledge. It involves the extraction of large volumes of data and performance of exploratory data analysis. The program ensures that the learners become acquainted with real business problems and address them using tools and modern solutions. You learn about robust models, their deployment and the entire life cycle of Data Science.


Graduation in Engineering, Math or Statistics and a passion for data analysis

Time Commitment

14 weeks full time


Virtual Instructor-Led Training



Course Content

  • Life Cycle
  • Machine Learning/Big Data
  • Tools
  • Environment Creation
  • Connecting with Python
  • Data Types
  • Table Creation
  • SQL Queries
  • Python Advantage
  • Python Installation/Anaconda Installation
  • Basics Of Python
  • Variables
  • Data Types
  • Arithmetic Operators
  • Assignment Operators
  • Comparison Operators
  • Logical Operators
  • Exceptions
  • If Then Else
  • For Loop
  • While Loop
  • Lambda Function
  • Module
  • Descriptive Statistics
  • Cumulative Distribution
  • Continuous Distribution
  • Probability
  • Operations on distributions
  • Hypothesis testing
  • Correlation
  • Data Extraction methods
  • Raw Data/Processed Data
  • Text format to Data frame
  • JSON to Data frame
  • PDF to Data frame
  • Excel to Data frame
  • Web to Data frame
  • Data Cleaning Pandas
  • EDA
  • Data Pre-processing
  • Imbalanced
  • Types of Graphs
  • Seaborne
  • Matplotlib
  • Plotly
  • Missing no
  • Introduction
  • Process Flow
  • Supervised Learning
  • Linear Regression
  • Logistic Regression
  • Decision Tree
  • Random Forest
  • XGBoost
  • Unsupervised Learning
  • K Means
  • Data Structure and Tools
  • Live Projects and Use Cases
  • Text Classification Applications
  • Similarity Recommendation App
  • Develop a Live Chat Bot
  • Data Structure and Tools
  • Live Projects and Use Cases
  • Finance
  • Manufacturing
  • Healthcare
  • IOT
  • AWS Cloud
  • Model Building
  • Model Deployment
  • Providing Solution
  • Introduction to Business Intelligence
  • Microsoft Excel Basics
  • Types Of Graphs
  • Business Intelligence Tools
  • Introduction to Tableau
  • Microsoft Excel Basics
  • Tableau Advantages
  • Tableau Installation
  • Connecting to Databases
  • Data Blending
  • Visual Analytics
  • Reports Dashboards and Stories
  • Punishing Reports
  • Introduction to Power BI
  • Power BI Basics
  • Power BI Advantages
  • Installation of Desktop Version
  • Connecting to Databases
  • Types of Graphs
  • Data Reports and Colors
  • Data Cleaning
  • Data Modelling
  • Saving BI Reports
  • Other BI Tools
  • Live Projects and Use Cases
  • Introduction to Big Data
  • Five V’s of Big Data
  • Source of Big Data
  • Big Data Challenges
  • Introduction to Hadoop
  • Hadoop Architecture
  • Name Node, Data Node, Secondary Node
  • Job tracker, Task Tracker
  • HDFS
  • Map Reduce
  • Hadoop Configuration
  • Introduction to PySpark
  • RDD Programming :Overview of Spark basics - RDDs
  • Spark SQL, Datasets, and Data Frames
  • Structured Streaming: Processing structured data streams with relation queries
  • Spark Streaming
  • Applying machine learning algorithms
  • Creating Cluster
  • Data Frame
  • Pipe Line Components
  • Parameters
  • Saving and Loading Pipelines
  • Big Data Life Cycle
  • Executing ML in Big Data

Why Study With Us?

Trainer Profile Sample

Work Experience

Core Technology Faculty
  • 15 + Years of experience in 5 continents data analytics team
  • Masters in Software Engineers
  • Worked on building accurate models in predicting the companies’ profits with 98% success rate
  • Provided analytical solutions for business decisions in critical situations
  • Handled end to data analytics life cycle projects of Banking, Insurance, Manufacturing Industries


  • Database: SQL, Oracle
  • Language: Python, R, PySpark
  • Tools: Tableau, Power BI
  • Other Skills: Statistics, Machine Learning

Education and Awards

  • PhD in Data Science
  • Master in Engineering (Computer Science)
  • IBM Certified Data Analyst


The minimum requirement is a Bachelor’s degree with statistics or mathematics, or a Bachelor’s degree in engineering with programming skills.
Yes, a minimum experience of 5 years in analyzing the data sets or full end to end application development is required.
The exponential growth of data worldwide has led to companies investing in the management of large databases. These companies aim to extract meaningful information for decision-making. Hiring a resource with all the skills such as scrapping , connecting database , rectifying data problems , analyzing, reporting , predicting and deployment is difficult in the current job market. The rising need and shortage of skills have increased the demand for certified Full Stack Data Scientists.
Yes, you will get placement assistance from NLL Academy.
The average annual compensation of a full stack data scientist is about $150,000 in USA and Rs. 20, 00,000 in India, depending on the project exposure.
After completion of the Full Stack Data Science course, you may become a Junior Data Scientist or even a Full Stack Data Scientist.

Contact us now for detailed curriculum and more!