Data Science with Python — Course #408

Turn Raw Data into

Powerful Insights 📊

Python is the #1 language for data science — powering Netflix recommendations, Google Search, and Tesla Autopilot. Master Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, and ML fundamentals to extract insights and build intelligent data-driven products.

Pandas NumPy Matplotlib Seaborn Scikit-learn EDA Statistics ML Algorithms Feature Engineering Model Deployment

Enroll Now Brochure

Python — Pandas EDA & Visualization

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Load and explore dataset
df = pd.read_csv('dataset.csv')
print(df.describe())
# Visualize correlations
sns.heatmap(df.corr(),
           annot=True, cmap='YlOrBr')
plt.title('Feature Correlation Matrix')
plt.show()

40K+

Jobs

DS roles in India

39 hrs

Duration

Video content

Modules

Full syllabus

Cert

Included

Industry recognised

24/7

Support

Expert guidance

What You Master

The Complete Python DS Skill Set

From data wrangling and visualization to machine learning, statistics, and production deployment.

🐍

Data Manipulation & Analysis

Master Pandas and NumPy for real-world data workflows — clean messy datasets, handle missing values, merge and group data, reshape arrays, and perform vectorized operations at scale. Build efficient data pipelines from raw CSV/JSON/SQL sources to analysis-ready structures.

Pandas NumPy Data Cleaning Merging GroupBy Reshaping Broadcasting Pipelines

📈

Data Visualization

Matplotlib, Seaborn, and Plotly — create insightful charts, heatmaps, pairplots, and interactive dashboards.

📐

Statistics & Probability

Hypothesis testing, t-tests, ANOVA, confidence intervals, distributions — the mathematical backbone of data science.

🤖

Machine Learning with Scikit-learn

Regression, classification, clustering, PCA, model evaluation, and hyperparameter tuning — end-to-end ML workflows.

Regression Classification Clustering

Syllabus

Course Curriculum

9 comprehensive modules covering Python data science from fundamentals to real-world deployment.

Why Python for Data Science?
Installing Python & Jupyter Notebook
Python Basics — Variables, Data Types, Operators
Control Flow — if-else, loops, functions, lambda
Collections — List, Tuple, Dictionary, Set, Comprehensions

Importing Data — CSV, Excel, JSON, SQL
Introduction to Pandas — Series & DataFrame
Data Cleaning & Handling Missing Values
Data Transformation, Merging & Grouping
NumPy — Arrays, Broadcasting, Performance Optimization

Introduction to Data Visualization & Storytelling
Matplotlib — Line, Bar, Scatter, Histogram, Pie Charts
Seaborn — Heatmaps, Pairplots, Boxplots, Violin Plots
Plotly & Dash for Interactive Visualizations
Dashboard building with Streamlit

Understanding Data Distributions
Detecting Outliers & Anomalies
Feature Engineering Basics
Correlation Analysis & Feature Selection
Handling Categorical Data & Encoding

Descriptive Statistics — Mean, Median, Variance, Std Dev
Probability Distributions — Normal, Binomial, Poisson
Hypothesis Testing — p-value, significance level
t-tests, ANOVA, Chi-square Tests
Confidence Intervals & Bayesian Thinking

Supervised vs Unsupervised Learning
Regression — Linear, Polynomial, Ridge, Lasso
Classification — Logistic Regression, Decision Trees, SVM, KNN, Random Forest
Unsupervised — K-Means, Hierarchical Clustering, PCA
Model Evaluation — Accuracy, Precision, Recall, F1, ROC-AUC

Feature Engineering & Feature Importance
Hyperparameter Tuning — GridSearchCV, RandomizedSearchCV
Handling Imbalanced Data — SMOTE, class weights
Ensemble Methods — Bagging, Boosting, XGBoost, LightGBM
Time Series Analysis — ARIMA, LSTM, Prophet

Introduction to Neural Networks
Building Deep Learning Models with Keras
Convolutional Neural Networks (CNNs) for images
Recurrent Neural Networks (RNNs) for sequences
Introduction to NLP with TensorFlow

Working with Large Datasets — Dask, PySpark Basics
Model Deployment with Flask & FastAPI
Building Interactive Data Apps with Streamlit
Docker Basics for Data Science Applications
Capstone Project — End-to-End Data Science Solution

Career Outcomes

Data Science Careers in 2026

Data Scientists are among the highest-paid professionals globally. Every major company — Amazon, Google, Netflix, Flipkart, Zomato — is building data-driven products and hiring Python data experts at record compensation packages.

₹10–35L

India Avg LPA

$100K–$185K

USA Avg

40K+

Active DS Jobs

38% Growth

Year-on-Year

Data Scientist

Build ML models, perform statistical analysis, and extract actionable insights from large datasets

₹12–35 LPA · $110K–$185K USA

Data Analyst

Clean, analyze, and visualize data to support business decisions using Python, SQL, and BI tools

₹8–22 LPA · $80K–$140K USA

ML Engineer

Design production ML pipelines, feature engineering, and model deployment infrastructure at scale

₹14–40 LPA · $120K–$195K USA

Business Intelligence Analyst

Create dashboards, reports, and KPI tracking systems to drive data-informed business strategy

₹10–28 LPA · $90K–$155K USA

Turn Raw Data into

Powerful Insights 📊

The Complete Python DS Skill Set

Course Curriculum

Data Science Careers in 2026

Ready to Become a Data Scientist?