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Data Science Beginner

Course Information

Level:Beginner

Modules:4

Duration:1 Month

Category:Data Science

Language:English

Certificate:Yes

Course Overview

Course Description:Learn data analysis, visualization, and basic Python for data. Get started with real-world datasets and tools like Excel and Pandas.

Topics Covered:

Excel
Data Clean
Python

Syllabus

Module 1: Introduction to Data Science & Python Basics

What is Data Science?
Real-world applications & career paths
Python fundamentals (variables, data types, loops, functions)
Libraries overview: NumPy, Pandas, Matplotlib

LAB 1

Setup Google Colab/Jupyter Notebook
Write Python programs (calculator, loops, list handling)
Use Pandas to load & explore CSV data

Module 2: Data Handling & Preprocessing

Data collection (CSV, Excel, APIs, web scraping basics)
Cleaning missing values, duplicates, outliers
Encoding categorical data (One-hot, Label encoding)
Normalization & Standardization

LAB 2

Load a dataset from Kaggle
Perform data cleaning using Pandas
Handle missing values & outliers

Module 3: Data Visualization & Exploratory Data Analysis (EDA)

Graphs: bar, line, scatter, histogram, boxplot, heatmaps
Correlation analysis
Feature importance overview

LAB 3

Use Matplotlib & Seaborn for visualization
Create a heatmap of correlations
Visualize trends in real-world dataset (COVID, Sales, etc.)

Module 4: Probability & Statistics for Data Science

Descriptive statistics (mean, median, variance, std dev)
Probability distributions (Normal, Binomial, Poisson)
Hypothesis testing (t-test, chi-square test, ANOVA)

LAB 4

Simulate coin toss & dice using Python
Perform hypothesis testing on dataset in Colab

Learning Outcome

Learn data basics, Python, and visualization. Build simple models and explore datasets with hands-on tools.

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Home > Courses

Data Science level 1

Course Information

Level:Level 1

Modules:8

Duration:2 Months

Category:Data Science

Language:English

Certificate:Yes

Course Overview

Course Description:Dive into machine learning, model building, and statistics. Work on projects in prediction, classification, and clustering.

Topics Covered:

Excel
Data Clean
Python
Analysis
Pandas
Visuals

Syllabus

Module 1: Introduction to Data Science & Python Basics

What is Data Science?
Real-world applications & career paths
Python fundamentals (variables, data types, loops, functions)
Libraries overview: NumPy, Pandas, Matplotlib

LAB 1

Setup Google Colab/Jupyter Notebook
Write Python programs (calculator, loops, list handling)
Use Pandas to load & explore CSV data

Module 2: Data Handling & Preprocessing

Data collection (CSV, Excel, APIs, web scraping basics)
Cleaning missing values, duplicates, outliers
Encoding categorical data (One-hot, Label encoding)
Normalization & Standardization

LAB 2

Load a dataset from Kaggle
Perform data cleaning using Pandas
Handle missing values & outliers

Module 3: Data Visualization & Exploratory Data Analysis (EDA)

Graphs: bar, line, scatter, histogram, boxplot, heatmaps
Correlation analysis
Feature importance overview

LAB 3

Use Matplotlib & Seaborn for visualization
Create a heatmap of correlations
Visualize trends in real-world dataset (COVID, Sales, etc.)

Module 4: Probability & Statistics for Data Science

Descriptive statistics (mean, median, variance, std dev)
Probability distributions (Normal, Binomial, Poisson)
Hypothesis testing (t-test, chi-square test, ANOVA)

LAB 4

Simulate coin toss & dice using Python
Perform hypothesis testing on dataset in Colab

Module 5: SQL & Databases for Data Science

SQL basics (SELECT, WHERE, JOIN, GROUP BY, HAVING)
Query optimization
Connecting SQL with Python

LAB 5

Use SQLite / MySQL free version
Write queries on sample dataset
Integrate SQL with Pandas

Module 6: Machine Learning Fundamentals (Supervised Learning)

ML pipeline (train-test split, model evaluation)
Regression: Linear, Multiple, Polynomial
Classification: Logistic Regression, k-NN, Decision Trees

LAB 6

Predict house prices (Regression) – Kaggle dataset
Build a classification model (Iris dataset)

Module 7: Advanced Machine Learning (Ensemble & Feature Engineering)

Random Forest, Gradient Boosting, XGBoost
Feature engineering & selection
Cross-validation techniques

LAB 7

Build a Random Forest model for Titanic survival prediction
Use Scikit-learn feature selection methods

Module 8: Unsupervised Learning & Clustering

k-Means Clustering
Hierarchical clustering
PCA (Dimensionality Reduction)

LAB 8

Customer segmentation (K-Means)
Reduce dimensions of dataset using PCA

Learning Outcome

Apply machine learning algorithms, data cleaning, and model evaluation. Work on real-time datasets and industry cases.

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Data Science level 2

Course Information

Level:Level 2

Modules:12

Duration:3 Months

Category:Data Science

Language:English

Certificate:Yes

Course Overview

Course Description:Master deep learning, NLP, and scalable ML systems. Build AI solutions for healthcare, finance, IoT, and more.

Topics Covered:

Excel
Data Clean
Python
Analysis
Pandas
Visuals
Real Projects
AI Link

Syllabus

Module 1: Introduction to Data Science & Python Basics

What is Data Science?
Real-world applications & career paths
Python fundamentals (variables, data types, loops, functions)
Libraries overview: NumPy, Pandas, Matplotlib

LAB 1

Setup Google Colab/Jupyter Notebook
Write basic Python programs (calculator, loops, list handling)
Load and explore CSV data using Pandas

Module 2: Data Handling & Preprocessing

Data collection (CSV, Excel, APIs, web scraping basics)
Cleaning missing values, duplicates, outliers
Encoding categorical data (One-hot, Label Encoding)
Normalization & Standardization

LAB 2

Load Kaggle dataset
Perform data cleaning using Pandas
Handle missing values & outliers

Module 3: Data Visualization & EDA

Graphs: bar, line, scatter, histogram, boxplot, heatmaps
Correlation analysis
Feature importance overview

LAB 3

Visualize data using Matplotlib & Seaborn
Create correlation heatmaps
EDA on real-world dataset (COVID, Sales, etc.)

Module 4: Probability & Statistics for Data Science

Descriptive statistics (mean, median, variance, std dev)
Probability distributions (Normal, Binomial, Poisson)
Hypothesis testing (t-test, chi-square, ANOVA)

LAB 4

Simulate coin toss & dice in Python
Perform hypothesis testing in Colab

Module 5: SQL & Databases for Data Science

SQL basics (SELECT, WHERE, JOIN, GROUP BY, HAVING)
Query optimization
Connecting SQL with Python

LAB 5

Use SQLite / MySQL
Write and execute SQL queries
Integrate SQL with Pandas

Module 6: Machine Learning Fundamentals (Supervised Learning)

ML pipeline (train-test split, model evaluation)
Regression: Linear, Multiple, Polynomial
Classification: Logistic Regression, k-NN, Decision Trees

LAB 6

Predict house prices (Regression)
Build a classification model (Iris dataset)

Module 7: Advanced ML & Feature Engineering

Random Forest, Gradient Boosting, XGBoost
Feature engineering & selection
Cross-validation techniques

LAB 7

Titanic survival prediction using Random Forest
Feature selection using Scikit-learn

Module 8: Unsupervised Learning & Clustering

k-Means Clustering
Hierarchical Clustering
PCA (Dimensionality Reduction)

LAB 8

Customer segmentation using K-Means
Dimensionality reduction using PCA

Module 9: Deep Learning & Neural Networks

Basics of neural networks
Activation functions & backpropagation
TensorFlow & Keras introduction

LAB 9

Build a simple ANN in TensorFlow/Keras
MNIST digit classification

Module 10: Natural Language Processing (NLP)

Text preprocessing (tokenization, stopwords, stemming, lemmatization)
Bag of Words, TF-IDF
Sentiment analysis basics

LAB 10

Text cleaning using NLTK
Build a sentiment classifier

Module 11: Big Data & Cloud

Introduction to Big Data (Hadoop, Spark)
Cloud tools (Google BigQuery, AWS, Azure)
Handling large datasets

LAB 11

Run queries on BigQuery free tier
Data processing using PySpark

Module 12: Capstone Project & Deployment

End-to-End Project Workflow
Model deployment using Flask/Streamlit
Version control & documentation

LAB 12

Select dataset (finance/healthcare/retail/social media)
Perform EDA → ML/DL → Visualization → Deployment
Deploy on Streamlit Cloud / Heroku

Learning Outcome

Master advanced ML, deep learning, and data processing techniques to build deployable, real-world data-driven solutions.

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Introduces data collection, cleaning, and visualization techniques. Learners gain an overview of how data drives decisions in various industries.

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Focuses on exploratory data analysis, statistical methods, and machine learning foundations. Students work on structured datasets to draw insights.

Machine Learning Beginner

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Introduces fundamental ML concepts, data preprocessing, and supervised learning. Learners understand how machines identify patterns and make predictions.