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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 theory

120

Hours

Enroll

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

online Training

online Training

Course Syllabus

Download 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.

1. Python Basics
  • Python IDE
  • Hello World Program
  • Variables & Names
  • String Basics
  • Showcase
  • 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
  • Regular Expressions
  • Python PIP
  • Read Excel Data in Python
  • Iterators
  • Pickling
  • Python JSON
  • 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
  • Inferential Statistics: Sample vs Population
  • CLT and Its Proof
  • Chi-Squared Distribution and Its Properties
  • Point and Interval Estimators
  • Estimation Technique: MLE
  • Interval Estimator of μ with Unknown σ
  • Examples of Estimators
  • Hypothesis Testing I
  • Hypothesis Testing II
  • Hypothesis Testing III
  • Assessment
  • Inferential Statistics: Sample vs Population
  • CLT and Its Proof
  • Chi-Squared Distribution and Its Properties
  • Point and Interval Estimators
  • Estimation Technique: MLE
  • Inferential Statistics: Sample vs Population
  • CLT and Its Proof
  • Chi-Squared Distribution and Its Properties
  • Point and Interval Estimators
  • Estimation Technique: MLE
  • Read Complex JSON Files
  • Styling Tabulation
  • Distribution of Data: Histogram
  • Box Plot
  • Data Visualization Recap
  • Pie Chart
  • Donut Chart
  • Stacked Bar Plot
  • Scatter Plots
  • Bar Plot
  • Continuous vs Continuous Plot
  • Line Plot
  • Line Plot: COVID Data
  • Dash by Plotly Setup
  • Dash Core Components
  • Styling Dash Application
  • Callbacks
  • Adding Interactivity to Dash Apps Using Callbacks
  • 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
  • Introduction to Machine Learning
  • Expert Systems and 6 Jars
  • Supervised Learning: Regression and Classification
  • Evaluation Metrics and Measuring Accuracy
  • Introduction to Regression
  • Interpreting Models
  • Feature Selection
  • Regularization: Ridge and Lasso
  • Introduction to Classification
  • Evaluation Metrics: TP, FP, and AUC
  • Classification Using Logistic Regression
  • Classification Using KNN
  • SVM
  • Introduction to Decision Trees
  • Building, Pruning, and Interpreting Trees
  • Ensemble Techniques: Bagging and Boosting
  • Random Forests
  • Boosted Trees: Gradient Boosting
  • Comparison of Supervised Techniques: When to Use What?
  • Do’s and Don’ts While Training ML Models
  • Handling Imbalanced Data
  • Undersampling
  • Oversampling
  • Other Methods: ROSE, SMOTE, etc.
  • Introduction to Unsupervised Learning
  • Market Basket Analysis
  • K Means Algorithm
  • Assessment
  • Part of Speech Tagging (PoS Tagging)
  • Lemmatization and Stemming
  • Stop Word Removal
  • Semantic Analysis
  • Word Sense Disambiguation
  • Relationship Extraction
  • Sentiment Analysis
  • Case Study I: Credit Card Fraud Detection
  • Case Study II: Airline Customer Segmentation
  • Case Study III: Product Recommendation Engine

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A.R. Nallathambi

President

A.R. Nallathambi is a visionary leader with over 33 years of experience in the IT industry providing cutting-edge IT Training, Placement, and Development services. Under his leadership, the company focuses on bridging the gap between academic learning and industry requirements. 

He is instrumental in shaping the company’s training programs to include the latest technologies such as AI, Data Science, Cybersecurity, and Cloud Computing. Under his leadership, IAT Solutions is dedicated to delivering top-quality solutions while fostering innovation and growth. His vast industry knowledge and leadership experience continue to shape the company’s strategic direction.