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Online Data Science Course
A complete course that covers all of the basics to the advanced level.
Why #Web D School?
We're committed to helping every student build a meaningful, successful career. We don’t just teach — we walk with you every step of the way, from your first design to your first job.
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Placement Record
Showcasing the top companies where our students have been successfully placed.
What Our Students Say
Hear from our students about their journey of support, learning, and growth
Kaushik N
Data Science
★★★★★
I genuinely loved the data science course offered here. The course was very helpful for someone like myself who wanted to transition to data science from a non-IT domain. The syllabus was well-tailored and covered everything from basic all the way till advanced topics. The instructors were very knowledgeable and made the classes more engaging.
Priya
Data Science
★★★★★
Coming from a non-technical background, I was initially intimidated by Data Science. But this course made everything approachable and easy to understand. The hands-on projects and real-world examples gave me the confidence to apply for jobs in the field.
Rahul
Data Science
★★★★★
The instructors were incredibly supportive and knowledgeable. They didn’t just teach the tools—they helped us understand the 'why' behind every concept. This course truly built a strong foundation for my career in data science.
Jenifer
Data Science
★★★★★
I was looking for a career shift and this course delivered beyond my expectations. The combination of theory, assignments, and case studies was spot on. I landed my first role as a Data Analyst right after completing the program.
Online Certification
Online Data Science Course
Data Science Course = Excellent career choice
Data science is a new and popular profession with several employment options for recent grads.
Data science is a branch of computer science that combines programming, statistics, and business intelligence with subjects such as machine learning, artificial intelligence, and business analytics to extract useful information from massive volumes of data.
Data scientists are in great demand because they can apply their abilities in various industries, including healthcare, finance, retail, education, and many more. The need for data scientists has increased by 31% each year over the previous decade. In India, their typical compensation ranges from 6 to 10 lacs per year, depending on location and company size.
If you have robust Data Science abilities, you may acquire jobs such as Data Analyst, Business Intelligence Analyst, Data Visualizer, and so on.
Data science is one of the top five jobs that young people globally wish to study, and it will only grow in popularity in the coming years.
Master Data Science with the Best
Why Learn Online Data Science Course at Web D School?
Learn from industry experts with a hands-on curriculum, real-time projects, and personalized mentorship. We prepare you not just to learn data science, but to launch a successful career in it.
Trainers with Industry Experience
All our instructors have real-time experience working in reputable organizations and a thorough knowledge of the Data Science domain.
Limited Batch Size
We limit our Online Data Science course to 6 students every batch so that we can give individual attention to each one.
Frills-Free Syllabus
Unlike other institutes that attempt to attract students with fancy terminology and unnecessary topics, we only teach what a student needs to know to enter the Data Science field.
Projects Based Training (SOAP)
We provide our online Data science course students a lot of tasks and assignments using a SOAP system (Student Output Assessment Plan) and give them valuable feedback.
Lively & Interactive Classes
Because our batch sizes are small, the classes will be highly interactive; with our data science course, students are encouraged to ask any number of questions during the session.
Recorded Sessions
All sessions would be recorded and shared with students so they could watch and learn from them afterward.
Placement Support
We have a professional placement team that assists all our students in their placement journey, from resume creation to finding a good job.
Master the Science of Data
What you will learn?
Few things to know before joining our Data science online training program.
Data science
Data science is an umbrella term that incorporates, among other things, data analytics, data mining, and machine learning. A data scientist is an expert who captures, analyses, and interprets massive amounts of data. Data scientists often deal with uncertainty by predicting the future using advanced data tools.
Data analyst
Data Analytics is a subset of Data Science. A data analyst uses data to discover critical insights about a company's consumers and to devise problem-solving methods. Data analysts frequently employ structured information to address actual business issues by utilizing technologies such as SQL, R, or Python programming languages, data visualization applications, and statistical analysis.
Data visualization
A subfield of data science is data visualization. The pictorial depiction of data and information is known as data visualization. Data visualization tools use visual components such as charts, graphs, and maps to make it simple to identify and comprehend trends, outliers, and patterns in data. Tableau, Power BI, and Excel are three of the most popular data visualization tools.
Python
Python is an essential tool in the data analyst's toolbox because it is well-suited for conducting repeated operations and data manipulation, and anybody who has worked with large amounts of data understands how frequently repetition occurs. Once you've grasped the foundations of Python, you should look into libraries like NumPy, Pandas, and Matplotlib, which aid the data analyst in carrying out their obligations. Tensorflow, Keras, and Theano are just a few of the Python packages that may assist data scientists in developing deep learning algorithms.
Mathematics & Statistics
Math and statistics are essential for Data Science since they are the foundation for all machine learning algorithms. Data scientists use statistics to collect, assess, analyze, and derive conclusions from data and apply appropriate quantitative mathematical models and variables. The use of linear algebra enables the efficient completion of various critical computations.
Machine learning
Machine learning examines and assesses enormous volumes of data automatically. It automates data processing and delivers predictions in real-time without the need for human interaction. A Data Model is created automatically and then trained to produce real-time predictions.
SQL
SQL (Structured Query Language) is a computer language used in relational databases to query and manage data. SQL performs a variety of actions on database-stored data, such as updating and deleting entries, creating and modifying tables and views, and so on.
Deep Learning
Deep learning is an essential part of data science, along with statistics and predictive modeling. Deep learning is a subset of machine learning that allows a computer to do human-like tasks such as speech recognition, image identification, and prediction. It increases the ability to classify, identify, detect, and define data-driven objects.
Artificial Intelligence
The core objective of Artificial Intelligence is to use computers to replicate human intelligence so that machines can make intelligent decisions in difficult situations. Machine Learning is a Supervised type of Artificial Intelligence and Data Science together, whereas Artificial Intelligence is a tool for Data Science.
Expansive Concepts
Data Science Course Syllabus
Turn numbers into insights! Master data analysis, prediction models, and visualization to solve real-world challenges.
Module 1:
Introduction to Data Science
- What is Data Science
- What is Machine Learning
- What is Deep Learning
- What is AI?
- Data Analytics and its types
- Why python?
Module 2:
Python (Basics to advanced)
- Installation and google colab setup
- Understanding various python notebooks like jupyter,spider.
- Variables and data types: numbers, Boolean and strings
- Operators
- Conditional statements
- Functions
- Sequences
- Files and Classes
- Object oriented programming
- Inheritance
Module 3:
Python Packages
- Numpy
- Pandas
- Matplotlib
- Seaborn
Module 4:
Statistics
- Types of statistics
- Descriptive statistics
- Types of data
- Population and sample
- Mean, median, mode
- Regression
- Variability
- R-squared
- Correlation
- Covariance
- Distribution
- Normal distribution
- Standard normal distribution
- Central limit theorem
- Standard error
- Confidence intervals
- Z-score
- Margin of errors
- Hypothesis Testing
Module 5:
Linear Algebra Basics
- Vectors
- Matrix
Module 6:
Probability
- Basic probability
- Computing expected values
- Events
- Combinatorics
- Factorials
- Bayesian inference
- Sets and Events
- Probability distributions
- Discrete distributions
- Applications of probability in statistics
- Applications of probability in Data Science
Module 7:
Data Preprocessing
- Handling missing values
- Encoding categorical data
- Split the dataset
- Feature scaling
Module 8:
Exploratory Data Analysis
- Feature Engineering
- Data Visualization - PowerBi (Basic to Advanced)
- Different chart types
Module 9:
Machine Learning
- Introduction to ML
- Types of AI
- Stages of ML projects
- Types of ML algorithms
Module 10:
Regression
- Simple linear regression
- Multi linear regression
- Model Evaluation
- Project : Kaggle bike demand prediction
Module 11:
Classification
- Logistics Regression
- Bagging and Boosting
- SVM
- KNN
- Decision Trees
- Random Forest
- XG Boost Classifier
- Naive Bayes
- Model Evaluation
- Project : open Kaggle Competition Project
Module 12:
Clustering and Time Series Analysis
- K means Cluster
- Hierarchical Clustering
- Model Evaluation
- parameter Tunning
- Model Visualization
- Introduction to Time Series Data
- Time Series Forecasting Methods
Module 13:
Natural Language Processing (NLP)
- Introduction to NLP
- Text Preprocessing
- Bag of Words Model
- TF-IDF Model
- Sentiment Analysis
- NLP Applications (Chatbots, Text Summarization, Language Translation)
Module 14:
Model Tuning
- Hyperparameter Optimization
- Grid Search
- Random Grid Search
- Bayesian Optimization
Module 15:
Recommendation System
- Content-based filtering
- Collaborative based filtering
- Market basket Analysis
Module 16:
Databases
- SQL
Module 17:
Deep Learning
- Tensor flow and Keras
- Deep learning frame work
- CNN and RNN
Module 18:
Flask
- Creating RestFul API with Flask
- Postman / ARC Chrome
Skills you will master
- Data Science & AI Fundamentals
- Programming Logic & Problem Solving
- Data Wrangling & Cleaning
- Statistical Analysis & Hypothesis Testing
- Linear Algebra for Data Science
- Probability & Inference
- Data Preprocessing & Feature Scaling
- Exploratory Data Analysis & Visualization
- Machine Learning Algorithms & Evaluation
- Regression & Predictive Modeling
- Classification Techniques & Model Tuning
- Clustering & Time Series Forecasting
- Natural Language Processing & Sentiment Analysis
- Hyperparameter Optimization
- Recommendation Systems & Market Analysis
- Database Querying & Management
- Deep Learning Architectures (CNN, RNN)
- API Development & Model Deployment
