Data Science with Python
Course Overview

The Data Science with Python course offers a hands-on learning experience, covering a wide range of topics in data science, statistical analysis, machine learning, and data visualization. Through this course, you will learn how to use Python libraries and tools to manipulate, analyze, and visualize data, as well as build machine learning models for predictive analytics. You will also gain practical experience by working on real-world projects that simulate industry scenarios.

Data Science with Python
Data Science with Python Content

1.1 Understanding the fundamentals of data science
1.2 Overview of the data science process and lifecycle
1.3 Introduction to Python for data science

2.1 Data exploration and cleaning with Pandas library
2.2 Handling missing data and outliers
2.3 Data transformation and feature engineering

3.1 Creating static and interactive visualizations
3.2 Plotting charts, histograms, and scatter plots
3.3 Customizing visualizations for effective communication

4.1 Descriptive statistics and data summarization
4.2 Analyzing distributions and relationships between variables
4.3 Extracting insights from data using statistical techniques

5.1 Understanding the basics of machine learning
5.2 Supervised vs. unsupervised learning
5.3 Evaluation metrics for machine learning models

6.1 Linear regression for regression problems
6.2 Logistic regression for classification problems
6.3 Decision trees and random forests

7.1 Clustering techniques (K-means, hierarchical clustering)
7.2 Dimensionality reduction (Principal Component Analysis)
7.3 Association rule mining (Apriori algorithm)

8.1 Cross-validation techniques
8.2 Model evaluation metrics (accuracy, precision, recall, F1-score)
8.3 Hyperparameter tuning and model selection

9.1 Text preprocessing techniques (tokenization, stemming, lemmatization)
9.2 Sentiment analysis and text classification
9.3 Building text-based predictive models

10.1 Understanding time-dependent data
10.2 Forecasting techniques (ARIMA, Exponential Smoothing)
10.3 Analyzing trends, seasonality, and anomalies

    Data Science with Python Projects

    1.1 Analyze customer data to predict churn behavior
    1.2 Build a machine learning model to identify customers at risk of churn
    1.3 Evaluate model performance and develop retention strategies

    2.1 Create a text classification model to classify emails as spam or non-spam
    2.2 Preprocess text data using NLP techniques
    2.3 Deploy the model to identify spam emails in real-time

    3.1 Build a collaborative filtering-based recommender system
    3.2 Recommend movies based on user preferences and historical data
    3.3 Evaluate the performance of the recommender system

    4.1 Develop a fraud detection model to identify fraudulent credit card transactions
    4.2 Handle imbalanced datasets and implement anomaly detection techniques
    4.3 Evaluate the model's performance using appropriate metrics

    5.1 Build a convolutional neural network (CNN) for image classification
    5.2 Preprocess and augment image data for improved model performance
    5.3 Classify images into predefined categories and evaluate model accuracy

    Big Data Hadoop Course Fee


    Online Classroom


    Corporate Training
    • 36 hours of instructor-led online training
    • Flexibility to choose classes
    All Our Programs Include

    This training course is designed to help you clear the Cloudera Spark and Hadoop Developer Certification (CCA175) exams.

    Real-world projects from industry experts

    With real world projects and immersive content built in partnership with top tier companies, you’ll master the tech skills companies want.

    Technical mentor support support

    With real world projects and immersive content built in partnership with top tier companies, you’ll master the tech skills companies want.

    Personal career coach and career services

    With real world projects and immersive content built in partnership with top tier companies, you’ll master the tech skills companies want.

    Flexible learning program

    With real world projects and immersive content built in partnership with top tier companies, you’ll master the tech skills companies want.

    Data Science with Python Certification

    Student Reviews
    4.5   (1.2k)

    The prerequisites for this course include a basic understanding of programming concepts and familiarity with Python. Knowledge of statistics and mathematics is beneficial but not mandatory.

    Yes, this course is suitable for beginners. It covers the fundamental concepts of data science and provides hands-on training with Python.

    Yes, a basic understanding of coding and Python is required for this course. However, the course provides guidance and support for learners at all levels.

    Yes, you will have access to the course materials through our dedicated learning management system. You can access them online at any time.

    Yes, upon successful completion of the course, you will receive a certificate that validates your skills as a Data Scientist with Python.

    Yes, you can interact with the instructors during the course through interactive sessions, discussion forums, and Q&A sessions.

    Yes, we provide career guidance to help you explore job opportunities in the field of data science. We also offer job placement support, including resume building and interview preparation

    This course covers essential Python libraries for data science, including Pandas, NumPy, Matplotlib, Seaborn, and scikit-learn.

    The course offers a balanced approach with both theoretical concepts and practical applications. The hands-on projects allow you to apply the learned concepts to real-world scenarios.

    Yes, you can work on the projects at your own pace. However, it is recommended to follow the suggested project timeline to stay on track with the course schedule.

    We offer financial assistance options, including scholarships and installment plans. Please contact our admissions team for more information.

    If you decide to switch to a different course within a specific timeframe, we can assist you with the transition. Please contact our support team for more details.

    While the emphasis is on individual projects, there may be opportunities for group projects to foster collaboration and teamwork.

    Yes, you will have lifetime access to the course materials even after completing the course. You can refer to them for future reference or to refresh your knowledge.

    He course offers flexible learning options, allowing you to study at your own pace. However, there may be suggested timelines and deadlines for better learning outcomes.

    We have a refund policy in place. Please refer to our refund policy for more information on eligibility and terms.

    Yes, you will have the option to download the course materials for offline access through our learning management system.

    Yes, there will be assessments and exams throughout the course to evaluate your understanding and progress.

    Yes, our technical support team is available to assist you with any technical difficulties you may encounter during the course.

    To enroll in the course, simply visit our website and follow the instructions for enrollment. Our admissions team will guide you through the process and assist with any questions or concerns.

    Limitless learning,
    more possibilities

    Online courses open the opportunity for learning to almost anyone, regardless of their scheduling commitments.


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