Data Science Online Courses

📋 What You'll Learn

Module 1: Introduction to Data Science
Module 2: Programming for Data Science (Python)
Module 3: Data Wrangling and Preprocessing
Module 4: Exploratory Data Analysis (EDA)
Module 5: Probability and Statistics for Data Science
Module 6: Introduction to Machine Learning
Module 7: Introduction to Deep Learning
Module 8: SQL and Data Access
Module 9: Model Deployment and MLOps Basics
Module 10: Capstone Project and Real-world Case Studies

📌 Requirements

Basic Requirements
Basic computer knowledge
Interest in programming and analytics
Laptop/Desktop with internet connection
No prior Data Science experience required

Recommended Knowledge
Basic mathematics understanding
Logical thinking and problem-solving skills
Basic programming knowledge is helpful but not mandatory

📖 Description

Data Science is one of the fastest-growing technologies transforming industries through data-driven decision making, Artificial Intelligence, automation, and predictive analytics. This comprehensive course is designed to provide students, engineers, and professionals with strong practical and theoretical knowledge in Data Science, Machine Learning, Deep Learning, SQL, and Model Deployment.

The program starts from the fundamentals of Python programming and gradually moves toward advanced Machine Learning and AI concepts. Students will learn how to collect, clean, analyze, visualize, and interpret data using industry-standard tools and libraries. The course also covers statistical analysis, data preprocessing, exploratory data analysis, machine learning algorithms, neural networks, database handling, deployment techniques, and real-world project implementation.

Through hands-on practice, case studies, and capstone projects, learners will gain real-world experience in solving industrial problems using data-driven approaches. The course is designed according to modern industry requirements and Industry 4.0 technologies, making students job-ready for careers in AI, analytics, automation, and intelligent systems.

This training is highly beneficial for:

Engineering Students
Diploma Students
IT and Computer Science Students
Working Professionals
Beginners interested in AI and Data Science

10 sections 61 lectures

📁 Module 1: Introduction to Data Science

6 lectures ▼

▶  What is Data Science?

▶  History and Evolution of Data Science

▶  Data Science vs. Machine Learning vs. AI vs. Business Intelligence

▶  Real-world Applications in Healthcare, Finance, Retail, etc.

▶  Roles: Data Scientist, Analyst, Engineer, ML Engineer

▶  Data Science Project Lifecycle Overview

📁 Module 2: Programming for Data Science (Python)

6 lectures ▼

▶  Python Basics: Variables, Data Types, Loops, Functions

▶  Python Libraries: Numpy, Pandas, Matplotlib, Seaborn

▶  Reading & Writing Files (CSV, Excel)

▶  List, Dictionary, Set, and Tuple Usage

▶  Jupyter Notebook Essentials

▶  Introduction to IDEs: VS Code, Colab

📁 Module 3: Data Wrangling and Preprocessing

7 lectures ▼

▶  Importing Data from Various Sources

▶  Handling Missing Values and Duplicates

▶  Data Type Conversion and Feature Engineering

▶  Encoding Categorical Variables (One-Hot, Label Encoding)

▶  Normalization & Standardization

▶  Data Transformation and Scaling

▶  Feature Selection Basics

📁 Module 4: Exploratory Data Analysis (EDA)

6 lectures ▼

▶  Descriptive Statistics

▶  Distribution Plots: Histograms, KDE, Boxplots

▶  Correlation Analysis (Heatmaps)

▶  Univariate, Bivariate, and Multivariate Analysis

▶  Outlier Detection (Z-Score, IQR)

▶  Creating Data Stories from Visuals

📁 Module 5: Probability and Statistics for Data Science

6 lectures ▼

▶  Types of Data: Nominal, Ordinal, Interval, Ratio

▶  Probability Theory Basics

▶  Distributions: Normal, Binomial, Poisson

▶  Central Limit Theorem

▶  Hypothesis Testing: p-value, t-test, chi-square

▶  Confidence Intervals and Statistical Significance

📁 Module 6: Introduction to Machine Learning

10 lectures ▼

▶  Supervised vs Unsupervised Learning

▶  Train-Test Split, Cross-Validation

▶  Performance Metrics: Accuracy, Precision, Recall, F1, AUC

▶  Linear and Logistic Regression

▶  Decision Trees and Random Forests

▶  K-Nearest Neighbors (KNN)

▶  Support Vector Machines (SVM)

▶  Naive Bayes Classifier

▶  Clustering: K-Means, Hierarchical

▶  Dimensionality Reduction: PCA

📁 Module 7: Introduction to Deep Learning

6 lectures ▼

▶  What is a Neural Network?

▶  Structure: Neurons, Layers, Weights, Activation Functions

▶  Feedforward and Backpropagation

▶  Overfitting and Regularization (Dropout)

▶  Intro to TensorFlow/Keras or PyTorch

▶  Build a Simple Neural Network (MNIST or Iris)

📁 Module 8: SQL and Data Access

5 lectures ▼

▶  Introduction to Relational Databases

▶  SQL Basics: SELECT, WHERE, GROUP BY, ORDER BY

▶  JOINs: Inner, Outer, Left, Right

▶  Aggregations and Subqueries

▶  SQL in Python using SQLite or SQLAlchemy

📁 Module 9: Model Deployment and MLOps Basics

5 lectures ▼

▶  Saving and Loading Models (Pickle, Joblib)

▶  REST APIs using Flask/FastAPI

▶  Deploying to Free Platforms (Heroku, Render)

▶  Basics of Docker for ML

▶  Monitoring Models and Retraining Needs (Intro to MLOps)

📁 Module 10: Capstone Project and Real-world Case Studies

4 lectures ▼

▶  Domain-specific Case Study (e.g., E-commerce Churn Prediction)

▶  Workflow: Data → EDA → Modeling → Evaluation → Deployment

▶  Hands-on Project Guidance

▶  Report Writing and Presentation Guidelines

Miss: Anushka Bhole

Expert Instructor · Computer and IT Courses

⭐ 5.0 Rating 👥 302 Students 📚 10 Courses

Miss: Anushka Bhole is an experienced instructor with deep expertise in Computer and IT Courses. With a focus on practical, real-world learning, the teaching approach ensures students gain skills they can apply immediately in their careers or projects. Committed to helping every student succeed regardless of their background or experience level.

5.0

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Gajanan Chaudhari

20 May 2026

★★★★★

best course to start learning data science

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🔥 Hurry up! Offer ends in --:--:--

₹5,999.00

Includes:

▶ 61 videos

📚 61 lessons · 0m total

♾️ Lifetime access

🏆 Certificate

📥 Source code & resources

This course includes:

📋 What You'll Learn

📌 Requirements

📖 Description

Miss: Anushka Bhole

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