Data Science
By Admin
4,8
500+ students
Course Duration:35+ hrs
Course level:Beginner to Advance
What I will learn?
- Python Basics – Data manipulation with Pandas, NumPy, Matplotlib.
- Machine Learning – Regression, classification, clustering.
- Deep Learning – Neural networks, TensorFlow, PyTorch.
- Real-World Projects – Big data, automation, predictive analytics.
About Course
Data Science with Python: Beginner to Advanced
Unlock the power of data with our comprehensive Data Science with Python course. Learn Python programming, data manipulation with Pandas and NumPy, and visualization with Matplotlib and Seaborn. Master machine learning techniques, including regression, classification, and clustering. Dive into deep learning with TensorFlow and PyTorch for AI applications. Gain hands-on experience with real-world projects in big data, automation, and predictive analytics. Whether you’re a beginner or an aspiring data scientist, this course equips you with the skills to analyze data, build models, and make data-driven decisions. Enroll now and start your data science journey!
Course Curriculum
- Basics of Python syntax, variables, and data types.
- Working with essential libraries: Pandas, NumPy, and SciPy.
- Understanding loops, conditional statements, and functions.
- Introduction to object-oriented programming (OOP) in Python.
- Data cleaning: handling missing values, duplicates, and outliers.
- Feature engineering and transformation techniques.
- Data visualization using Matplotlib, Seaborn, and Plotly.
- Creating interactive dashboards with Power BI/Tableau integration.
- Descriptive statistics: mean, median, mode, variance, and standard deviation.
- Probability concepts: distributions, Bayes’ theorem, and random variables.
- Hypothesis testing: t-tests, chi-square tests, and ANOVA.
- Identifying trends, correlations, and feature importance in datasets.
- Introduction to supervised learning (Linear Regression, Decision Trees, SVM).
- Understanding unsupervised learning (K-Means Clustering, PCA, DBSCAN).
- Model training, evaluation (ROC-AUC, confusion matrix, precision-recall).
- Hyperparameter tuning using GridSearchCV and RandomizedSearchCV.
- Basics of artificial neural networks (ANNs) and activation functions.
- Building deep learning models with TensorFlow & PyTorch.
- Understanding CNNs for image processing and RNNs for sequential data.
- Transfer learning and fine-tuning pre-trained models.
- Introduction to Hadoop, Spark, and distributed data processing.
- Working with large-scale datasets using PySpark.
- Cloud-based data storage with AWS S3, Google Cloud Storage, and Azure.
- Real-time data processing and ETL pipelines.
- Applying ML to finance: fraud detection, stock price prediction.
- Healthcare analytics: disease prediction, patient data analysis.
- E-commerce and marketing: customer segmentation, recommendation systems.
- Hands-on projects and Kaggle competitions for practical exposure.
- Deploying ML models using Flask, FastAPI, and Docker.
- Integrating models with cloud platforms like AWS Lambda and GCP AI.
- Automating workflows with MLOps and CI/CD pipelines.
- Using analytics for strategic decision-making and business growth.
Material Includes
- 35+ Hours of Step by Step Video Lectures by a MNC Certified Trainer
- Life Time LMS Access.
- Section Quizzes to Test Your Knowledge on the Lecture Topics
- Industry Based Hands on Projrcts.

₹3,999/-
₹7999/- Discount 50% off
Benefits Obtained :
- 35+ hrs of Training
- Industry based assements
- Outcome based learning
- Industry based hands on projects
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