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Data Science Course

Step into the booming field of Data Science by learning data analysis, machine learning algorithms, and statistical tools.

This course provides both foundational and advanced insights into the world of data. Whether you’re a beginner or have some experience, you’ll gain valuable knowledge and practical skills.

> Step 1: Practical Projects

2 live projects and 10+ demo projects to build your real-world experience.

> Step 2: Mock Interviews & Placement Support

Guidance on preparing for data science interviews and building a data-driven resume.

> Step 3: Trainer Expertise

Our trainers are data scientists with industry expertise, providing real-world insights and applications.

  • 70 Hours Practical
  • 50 Hours Practical
  • 120 Total Hours

Step into the booming field of Data Science by learning data analysis, machine learning algorithms, and statistical tools.

  • Online Training
  • Offline Training

Course Syllabus

Downlaod Syllabus PDF

The Data Science course syllabus is designed to provide you with a strong foundation in both theoretical and practical aspects of data science.

  • Python IDE
  • Hello World Program
  • Variables & Names
  • String Basics
  • List
  • Tuple
  • Dictionaries
  • Conditional Statements
  • For and While Loop
  • Functions
  • Numbers and Math Functions
  • Common Errors in Python
  • Functions as Arguments
  • List Comprehension
  • File Handling
  • Debugging in Python
  • Class and Objects
  • Lambda, Filters, and Map
  • List Item
  • Introduction to Algorithmic Thinking
  • Algorithm Efficiency and Time Complexity
  • Example Algorithms: Binary Search, Euclid’s Algorithm
  • Data Structures: Stack, Heap, and Binary Trees
  • Memory Management/Technologies
  • Best Practices: Keeping it Simple, DRY Code, Naming Conventions, Comments, and Docs
  • Assessment
  • Introduction to Pandas
  • Series Data Structure: Querying and Indexing
  • DataFrame Data Structure: Querying, Indexing, and Loading
  • Merging DataFrames
  • Group By Operation
  • Pivot Table
  • Data Handling in Python - Pandas 2
  • Date/Time Functionality
  • Example: Manipulating DataFrame
  • Data Modeling
  • Normalization and Star Schema
  • ACID Transactions
  • SQL 1
  • Select, Insert, Update & Delete (DML and DQL)
  • Join Operations
  • Window Functions (Rank, Dense Rank, Row Number, etc.)
  • Data Types, Variables, and Constants
  • Conditional Structures (IF, CASE, GOTO, and NULL)
  • Integrating Python with SQL
  • No Schema
  • Install MongoDB
  • How MongoDB Works?
  • Insert First Data
  • CRUD Operations
  • Insert Many
  • Update and Update Many
  • Delete and Delete Many
  • Diving Deep into find
  • MongoDB 2
  • MongoDB 3
  • Difference between Update and Update Many
  • Projection
  • Intro to Embed Documents
  • Embed Documents in Action
  • Adding Arrays
  • Fetching Data from Structured Data
  • Aggregation
  • Schema Types
  • Types of Data in MongoDB
  • Relationship between Data
  • One to One Using Embed Method
  • MongoDB 4
  • One to One Using Reference Many
  • One to Many Embed
  • One to Many Reference Method
  • Assessment – MongoDB
  • Why Counting and Probability Theory?
  • Basics of Sample and Event Space
  • Axioms of Probability
  • Total Probability Theorem and Bayes Theorem
  • Random Variables, PMF, and CDF
  • Discrete Distributions: Bernoulli, Binomial, and Geometric
  • Expectation and Its Properties
  • Variance and Its Properties
  • Continuous Distributions: Uniform, Exponential, and Normal
  • Sampling from Continuous Distributions
  • Simulation Techniques: Simulating in NumPy
  • Assessment
  • Read Complex JSON Files
  • Styling Tabulation
  • Distribution of Data: Histogram
  • Box Plot
  • Data Visualization Recap
  • Pie Chart
  • Donut Chart
  • Stacked Bar Plot
  • Relative Stacked Bar Plot
  • Stacked Area Plot
  • Scatter Plots
  • Bar Plot
  • Continuous vs Continuous Plot
  • Line Plot
  • Line Plot: COVID Data
  • Handling Missing Data
  • Techniques to Impute Missing Values
  • Encoding the Data
  • Outlier Detection and Correction
  • Meaningful Data Transformation
  • Assessment
  • How Computers Process and Understand Images
  • Basic Properties of Images
  • Grayscale, Processing Pixel Values
  • Masking
  • Image Processing
  • Text Data Preprocessing
  • Cleaning Text Data
  • Exploratory Data Analysis on Image and Text Data
  • Assessment
  • Part of Speech Tagging (PoS Tagging)
  • Lemmatization and Stemming
  • Stop Word Removal
  • Semantic Analysis
  • Word Sense Disambiguation
  • Relationship Extraction
  • Sentiment Analysis
  • Text Extraction
  • Case Study I: Credit Card Fraud Detection
  • Case Study II: Airline Customer Segmentation
  • Case Study III: Product Recommendation Engine