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Course Overview

We’ve crafted this comprehensive Data Science Course to provide in-depth knowledge of all key aspects of data science, including Python programming, data visualization using tools like Power BI, and advanced machine learning and statistical analysis techniques. Whether you prefer offline or online learning, our program ensures flexibility and effectiveness, enabling you to become proficient in the most in-demand skills in the field of data science.

Who Can Attend This Data Science Certification Course?

Graduates and Freshers

Kickstart your career in tech with a strong foundation in Data Science.

Professionals Seeking Career Transition

Transition into the high-demand field of Data Science.

Coding Enthusiasts and Learners

Kickstart your career in tech with a strong foundation in Data Science.

Experienced Developers

Upskill with the latest technologies and industry practices in Data Science.

Key Features

6Months

Global Certification

15+ Tools

400 Hours

10+ Projects

50+ Assesments

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Level

Beginners

Students

113,290

Duration:

4 Months

Course Curriculum

Compilation and Interpretation

  • What is Compilation?
  • What is Interpretation?
  • How Python is both compiled and interpreted?
  • Role of Python Virtual Machine (PVM)

Python Execution

  • Steps in Python Execution
  • Bytecode Compilation
  • Role of Interpreter in Execution

Script Mode and Interactive Mode of Execution

  • Introduction to Interactive Mode (REPL)
  • Running Python Scripts in Script Mode
  • Differences and Use Cases

Data Types

  • Numeric Types (int, float, complex)
  • Sequence Types (list, tuple, range)
  • Set and Dictionary Types
  • Mutable vs Immutable Types
  • Type Conversion

Command Line Arguments

  • What are Command Line Arguments?
  • Using sys.argv to Access Arguments
  • Parsing Arguments with the argparse Module

Functions

  • Defining and Calling Functions
  • Arguments and Return Values
  • Default and Keyword Arguments
  • Lambda Functions
  • Function Scope and Global Variables

List, Tuple, Set, Dictionary

  • Properties and Differences
  • List Operations and Methods
  • Tuple Immutability
  • Set Operations (Union, Intersection, Difference)
  • Dictionary Key-Value Operations

Collections Module

  • Counter – Counting Elements
  • defaultdict – Dictionary with Default Values
  • OrderedDict – Maintaining Key Order
  • deque – Double-Ended Queue
  • namedtuple – Creating Readable Tuples

Strings

  • String Creation and Manipulation
  • String Formatting (format(), f-strings)
  • String Methods (replace(), split(), join())
  • Working with Unicode and Encoding

Regular Expressions

  • Introduction to re Module
  • Pattern Matching with Metacharacters
  • Finding and Replacing Text
  • Using Groups and Special Sequences

Exception Handling

  • Types of Errors in Python
  • Using try-except-finally Blocks
  • Raising Custom Exceptions
  • Handling Multiple Exceptions

Object-Oriented Programming (OOP)

  • Introduction to OOP
  • Classes and Objects
  • Instance vs Class Variables
  • Encapsulation (private, protected, public access)
  • Setters and Getters
  • Constructors (__init__ method)
  • Inheritance (Single, Multiple, Multilevel)
  • Polymorphism (Method Overloading & Overriding)
  • Abstraction (ABC module and abstract methods)

Getting Started with Files

  • Reading and Writing Files (open(), read(), write())
  • File Modes (r, w, a, rb, wb)
  • Working with File Paths

Files Inventory Management

  • Managing Large Data Files
  • Reading and Writing Inventory Data
  • Appending and Updating Inventory Files

JSON Inventory Management

  • Introduction to JSON (json module)
  • Reading and Writing JSON Data
  • Converting JSON to Python and Vice Versa
  • Managing Inventory Using JSON

NumPy (Numerical Python)

  • Introduction to NumPy
  • Creating and Manipulating Arrays
  • NumPy Array Operations
  • Broadcasting and Vectorization
  • Working with Mathematical and Statistical Functions

Pandas (Data Analysis Library)

  • Introduction to Pandas and DataFrames
  • Series vs DataFrame
  • Data Cleaning and Manipulation
  • Handling Missing Data
  • Data Aggregation and Grouping

Matplotlib (Data Visualization)

  • Introduction to Matplotlib
  • Creating Line, Bar, and Scatter Plots
  • Customizing Graphs (Labels, Titles, Legends)
  • Working with Multiple Subplots
  • Saving and Displaying Plots

Data Preprocessing

  • Handling Missing Data
  • Data Cleaning and Transformation
  • Feature Scaling and Encoding
  • Handling Outliers

Data Analysis

  • Exploratory Data Analysis (EDA)
  • Statistical Data Analysis
  • Data Visualization Techniques
  • Using Pandas and Matplotlib for Analysis
  1. Introduction to DBMS
    • What is a Database?
    • DBMS vs. RDBMS
    • SQL Overview
  2. Installing MySQL
    • Downloading and Installing MySQL
    • Setting Up MySQL Server
    • MySQL Workbench Overview
    • Connecting MySQL with
    • Command Line & GUI
  3. SQL Command Types
    • DDL (Data Definition Language) – CREATE, ALTER, DROP, TRUNCATE
    • DML (Data Manipulation Language) – INSERT, UPDATE, DELETE
    • DCL (Data Control Language) – GRANT, REVOKE
    • TCL (Transaction Control Language) – COMMIT, ROLLBACK, SAVEPOINT
    • DQL (Data Query Language) – SELECT
  4. Creating Databases & Tables
    • Creating a Database
    • Creating a Table
    • Specifying Column Data Types
    • Primary Key & Foreign Key
  5. Inserting & Retrieving Data
    • Inserting Data into Tables
    • Retrieving Data using SELECT
    • Filtering Data with WHERE Clause
  6. Data Types in SQL
    • Numeric Data Types
    • Character & String Data Types
    • Date & Time Data Types
    • NULL Values in SQL
  7. Constraints in SQL
    • NOT NULL, UNIQUE
    • PRIMARY KEY, FOREIGN KEY
    • CHECK, DEFAULT
  8. Updating and Deleting Data
    • Modifying Data with UPDATE
    • Removing Data with DELETE
  9. Nested Queries (Subqueries)
    • Subqueries in WHERE Clause
    • Correlated Subqueries
    • EXISTS and NOT EXISTS
  10. Operators in SQL
    • Arithmetic, Logical, and Comparison Operators
    • LIKE, IN, BETWEEN, IS NULL
  11. Aggregation in SQL
    • COUNT, SUM, AVG, MIN, MAX
    • GROUP BY and HAVING Clause
  12. Joins in SQL
    • INNER JOIN
    • LEFT JOIN / RIGHT JOIN
    • FULL JOIN
    • CROSS JOIN
  13. UNION & UNION ALL
    • Combining Multiple Queries
    • Differences Between UNION and UNION ALL
  14. Aliases in SQL
    • Column Aliases
    • Table Aliases
  15. Indexes in SQL
    • What is an Index?
    • Creating and Managing Indexes
  16. ALTER, TRUNCATE, and DROP
    • Modifying Tables with ALTER
    • Removing Data with TRUNCATE
    • Dropping Tables and Databases
  17. Transactions in SQL
    • ACID Properties
    • COMMIT, ROLLBACK, and SAVEPOINT
  18. Cloning Tables in SQL
    • Duplicating Table Structure & Data
  19. Handling Duplicates
    • Using DISTINCT
    • Removing Duplicates
  20. SQL Injection
    • What is SQL Injection?
    • Preventing SQL Injection
  21. GRANT and REVOKE (Permissions in SQL)
    • Managing User Access
    • Assigning Privileges
  22. SQL Functions
    • Scalar Functions (UPPER, LOWER, LEN, CONCAT, etc.)
    • Aggregate Functions (SUM, AVG, COUNT, etc.)
  23. Views in SQL
    • Creating and Managing Views
    • Materialized vs. Non-Materialized Views
  24. Miscellaneous Topics
    • Stored Procedures
    • Triggers in SQL
    • Common Table Expressions (CTE)
  25. Normalization in SQL
    • First Normal Form (1NF)
    • Second Normal Form (2NF)
    • Third Normal Form (3NF)
    • Boyce-Codd Normal Form (BCNF)
  1. Introduction to Excel
    • What is Excel?
    • Understanding the Excel Interface
    • Workbook vs. Worksheet
    • Cells, Rows, and Columns
  2. Data Entry in Excel
    • Entering Text, Numbers, and Dates
    • AutoFill and Flash Fill
    • Using Drop-Down Lists
    • Data Entry Shortcuts
  3. Data Formatting & Validation
    • Formatting Cells (Font, Colors, Borders)
    • Number Formats (Currency, Percentage, Date, etc.)
    • Conditional Formatting
    • Data Validation (Restricting Input, Custom Rules)
  4. Functions in Excel
    • Basic Functions (SUM, AVERAGE, MIN, MAX)
    • Logical Functions (IF, AND, OR)
    • Lookup Functions (VLOOKUP, HLOOKUP, XLOOKUP)
    • Text Functions (LEFT, RIGHT, MID, CONCATENATE, TRIM)
    • Date & Time Functions (TODAY, NOW, DATEDIF)
    • Error Handling Functions (IFERROR, ISERROR)
  5. Hyperlinks & Illustration in Excel
    • Adding Hyperlinks to Websites, Files, and Sheets
    • Inserting and Formatting Images
    • Using Shapes and SmartArt
  6. Tables & Charts in Excel
    • Creating and Formatting Tables
    • Introduction to Charts
    • Column, Bar, Pie, Line, and Combo Charts
    • Customizing Chart Elements
  7. Formula Mastery
    • Understanding Cell References (Relative, Absolute, Mixed)
    • Using Named Ranges in Formulas
    • Array Formulas and Dynamic Arrays
    • Creating Complex Nested Formulas
  8. Excel Shortcuts
    • Essential Keyboard Shortcuts for Speed
    • Navigation and Selection Shortcuts
    • Formula and Function Shortcuts
    • Formatting Shortcuts
  9. Advanced Filters, Slicers, and Timelines in Pivot Tables
    • Using Filters and Custom Views
    • Applying Advanced Filters
    • Creating and Using Slicers in Pivot Tables
    • Using Timelines for Date-Based Data
  10. Data Analysis in Excel
    • Understanding Pivot Tables
    • Sorting & Filtering Data
    • Data Consolidation
    • Using What-If Analysis (Goal Seek, Scenario Manager)
    • Introduction to Power Query
  11. Visual Basic for Applications (VBA) in Excel
    • Introduction to VBA and Macros
    • Recording and Running Macros
    • Writing Simple VBA Code
    • Automating Repetitive Tasks
  1. Introduction to Power BI
    • What is Power BI?
    • Key Features and Benefits
    • Power BI Components (Desktop, Service, Mobile)
    • Understanding Data Visualization
    • Installing and Setting Up Power BI
  2. Understanding Parameters in Power BI
    • What are Parameters?
    • Creating and Managing Parameters
    • Dynamic Filtering with Parameters
    • Connecting Parameters with Data Sources
  3. Data Transformation in Power BI
    • Introduction to Power Query Editor
    • Cleaning and Shaping Data
    • Merging and Appending Queries
    • Handling Missing Values & Data Types
    • Creating Custom Columns
  4. Fundamentals of Power BI
    • Connecting to Different Data Sources
    • Understanding Data Modeling
    • Relationships Between Tables
    • Introduction to Measures and Calculated Columns
  5. Basic Plots in Power BI
    • Creating Bar, Line, and Pie Charts
    • Working with Scatter Plots and Tree Maps
    • Using Tables and Matrix Visuals
    • Customizing Visuals with Formatting Options
  6. Designing Plots in Power BI
    • Best Practices for Effective Visualization
    • Adding Titles, Labels, and Tooltips
    • Using Colors and Themes for Readability
    • Enabling Interactivity (Drill-through, Slicers, Filters)
  7. Introduction to DAX (Data Analysis Expressions)
    • What is DAX?
    • Basic DAX Functions (SUM, COUNT, AVERAGE)
    • Logical Functions (IF, SWITCH, AND, OR)
    • Aggregate and Time Intelligence Functions
    • Creating Measures and Calculated Columns
  • Understanding Data (Types, Distributions)
  • Identifying Outliers
  • Feature Engineering
  • Handling Missing Data
  • Data Transformation Techniques
  1. Introduction to Web Scraping
    • What is Web Scraping? – Understanding the Basics
    • Why is Web Scraping Important? – Real-Life Applications
    • Ethical Web Scraping – Legal Considerations and Best Practices
  2. Basics of Web Scraping
    • Understanding HTML & CSS Structure – How Web Pages are Built
    • Inspecting Web Pages – Using Browser Developer Tools
    • Introduction to Web Scraping Libraries – BeautifulSoup, Scrapy, Selenium
  3. Scraping Famous Websites
    • Wikipedia Scraping – Extracting Text and Links
    • YouTube Scraping – Fetching Video Titles, Views, and Comments
    • Amazon Scraping – Extracting Product Prices, Reviews, and Ratings
    • Handling Anti-Scraping Measures – Dealing with CAPTCHAs and Restrictions
  4. Image Scraping
  • Downloading Images from Websites – Using Python and Requests
  • Extracting Image Metadata – File Type, Size, and Resolution
  • Automating Image Scraping – Using Selenium for Dynamic Content
  1. Introduction to Machine Learning & AI
    • What is AI & ML? – Understanding the Difference
    • Types of Machine Learning – Supervised, Unsupervised, Reinforcement Learning
    • Real-World Applications of AI & ML – Healthcare, Finance, Automation
  2. Data Science’s Role in AI
    • Importance of Data in AI & ML
    • Data Preprocessing & Cleaning
    • Feature Selection & Engineering
  3. Regression Techniques
    • Linear Regression – Predicting Continuous Values
    • Multiple Linear Regression – Handling Multiple Features
    • Polynomial Regression – Modeling Non-Linear Data
  4. Classification & Supervised Learning
    • Support Vector Machine (SVM) – Understanding Margins & Hyperplanes
    • Decision Tree – Rule-Based Classification
    • Random Forest – Ensemble Learning for Better Accuracy
    • Classification Algorithms – Logistic Regression, Naïve Bayes, KNN
  5. Clustering & Unsupervised Learning
    • Clustering Algorithms – K-Means, Hierarchical Clustering
    • Dimensionality Reduction Techniques – PCA, t-SNE
  6. Feature Engineering & Model Optimization
    • Handling Missing Data & Outliers
    • Feature Scaling & Encoding Techniques
    • Hyperparameter Tuning & Model Evaluation
  7. Hands-on Project: MNIST Handwritten Digit Recognizer
    • Understanding the MNIST Dataset
    • Building a Neural Network for Handwritten Digit Classification
    • Evaluating the Model Performance
  8. Practical Projects in AI & ML
    • House Price Prediction (Regression)
    • Spam Email Classification (NLP & Classification)
    • Customer Segmentation (Clustering & Unsupervised Learning)
    • AI-Powered Chatbots & Virtual Assistants
  1. Fundamentals of Image Processing
    • What is Image Processing? – Basics and Applications
    • Understanding Pixels & Color Models – RGB, Grayscale, CMYK
    • Image Representation in Python – NumPy Arrays and Matrices
    • Introduction to OpenCV – Popular Library for Image Processing
  2. Image Processing Techniques
    • Reading & Displaying Images – Using OpenCV
    • Resizing & Cropping Images
    • Image Filtering – Blurring, Sharpening, Edge Detection
    • Thresholding Techniques – Binary & Adaptive Thresholding
  3. Image Processing on Live Web Cam
    • Capturing Video from Webcam – Using OpenCV
    • Applying Filters & Effects in Real-Time
    • Converting Live Video to Grayscale or Sketch Effect
  4. Taking a Selfie with OpenCV
    • Capturing and Saving an Image Using Webcam
    • Adding Effects & Filters to the Captured Image
    • Automating Selfie Capture Based on Facial Expressions
  5. Image Manipulation
    • Adjusting Brightness & Contrast
    • Changing Colors & Applying Color Transformations
    • Rotating & Flipping Images
  6. Masking
    • Understanding Image Masking – Extracting Specific Regions
    • Creating Custom Masks for Selective Editing
  7. Adding Logo on a Live Video
    • Overlaying Images in Real-Time Video Feed
    • Positioning and Resizing Logos Dynamically
  8. Face Detection and Manipulation
    • Using Haar Cascades for Face Detection
    • Detecting Facial Features – Eyes, Nose, Mouth
    • Swapping Faces & Applying Fun Filters
    • Blurring Faces for Privacy Protection
  1. Introduction to Deep Learning
    • What is Deep Learning? – Overview and Importance
    • Difference Between Machine Learning & Deep Learning
    • Key Applications of Deep Learning – Image Recognition, NLP, AI Assistants
  2. Perceptrons
    • Understanding Artificial Neurons – The Building Blocks of Neural Networks
    • Single-Layer Perceptron – Basics of Binary Classification
    • Activation Functions – Sigmoid, ReLU, Tanh, Softmax
  3. Multi-Layer Perceptron (MLP) Architecture
    • Introduction to Feedforward Neural Networks
    • Hidden Layers & Backpropagation Algorithm
    • Optimization Techniques – Gradient Descent, Adam, RMSprop
    • Regularization Techniques – Dropout, L1/L2 Regularization
  4. Convolutional Neural Networks (CNNs)
    • What is a CNN? – Understanding Feature Extraction
    • Convolutional Layers & Filters – Edge Detection, Feature Maps
    • Pooling Layers (Max Pooling, Average Pooling) – Reducing Dimensionality
    • Fully Connected Layers & Softmax Classifier
    • Building a CNN Model for Image Classification – Hands-on Implementation
  1. Getting Started with NLP
    • Introduction to NLP – Basics and Applications
    • Understanding Text Data & Tokenization
    • Challenges in NLP – Ambiguity, Synonyms, Stopwords
  2. Mastering Strings and ASCII Codes
    • String Manipulation in NLP
    • Understanding ASCII, Unicode & Encoding Formats
    • Handling Special Characters in Text Processing
  3. Regular Expressions from Scratch
    • Understanding Regex in NLP
    • Pattern Matching & Text Extraction
    • Cleaning Text Data using Regex
  4. Getting Started with Spacy
    • Introduction to Spacy – A Powerful NLP Library
    • Tokenization, Lemmatization & Stopword Removal
    • Named Entity Recognition (NER) & POS Tagging
  5. Text Sequencing using Word Cloud
    • Understanding Word Cloud & Its Importance
    • Creating a Word Cloud from Text Data
    • Visualizing Most Frequent Words in a Dataset
  6. Hands-on NLP Projects
    • Spam Email Classification – Using NLP & Machine Learning
    • Sentiment Analysis on Movie Reviews – Positive vs. Negative Sentiments
    • Chatbot Development – Creating a Simple NLP-Based Chatbot
    • Named Entity Recognition (NER) in Real-World Applications
  1. Introduction to Git & Version Control
    • What is Git? – Importance of Version Control
    • Difference Between Git & GitHub
    • Installing Git & Initial Setup
  2. Basic Git Commands & Workflow
    • Initializing a Repository (git init)
    • Cloning a Repository (git clone)
    • Checking Status (git status)
    • Adding Files to Staging (git add)
    • Committing Changes (git commit -m "message")
    • Viewing Commit History (git log)
  3. Working with Branches
    • Creating a New Branch (git branch)
    • Switching Between Branches (git checkout)
    • Merging Branches (git merge)
    • Resolving Merge Conflicts
  4. Pushing & Pulling Code on GitHub
    • Connecting Local Repo to GitHub (git remote add origin)
    • Pushing Code to GitHub (git push)
    • Pulling Latest Changes (git pull)
  5. Collaboration & Open-Source Contribution
    • Forking a Repository
    • Cloning & Contributing to Open Source Projects
    • Submitting a Pull Request (PR)
    • Code Reviews & Best Practices
  6. Advanced Git Features
    • Reverting Changes (git revert & git reset)
    • Stashing Changes (git stash)
    • Tagging Releases (git tag)
    • Git Rebase vs. Merge
  7. Hands-on GitHub Projects & Best Practices
    • Creating & Managing a Real-World Project
    • Automating CI/CD Pipelines with GitHub Actions
    • Using GitHub Issues & Wiki for Project Management
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What Our Students Says

Mahanthesh

The Java course at SkyllX was a game-changer for me. The hands-on projects and expert guidance made learning seamless.

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The Java Full Stack training was incredibly interactive. I now feel confident in backend development and frontend integration.

Shiva Prasad

Data Analytics at SkyllX helped me master SQL and Python. The Pay After Placement model removed financial stress.

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Thanks to SkyllX, I can now build full-stack applications with ease. The project-based learning made all the difference!

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The structured approach of the Data Analytics course helped me transition into a new career smoothly. Highly recommend it!

Manoj Gowda

SkyllX’s Data Analytics course gave me real-world insights. The practical approach helped me land a job with confidence.

FAQ's

Data Science involves extracting insights from data using tools like Python, statistics, and machine learning. In this course, you’ll learn programming, data analysis, visualization, machine learning, and real-world project implementation.

No prior experience is required. We start from the basics and gradually build up, making the course beginner-friendly.

We offer both online and offline classes. You can choose the mode that suits your learning preference.

You pay nothing upfront. Start paying the course fee only after you get placed in a qualifying job through SkyllX.

Yes! We provide complete placement support, including mock interviews, resume building, and job referrals with top companies.

We are an industry-driven training and placement company dedicated to equipping fresh graduates and professionals with the tech skills that matter.

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