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30,000.00 Duration: 50 Hours
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About your Data Science with Python Certification Training

Skills Covered

Python programming for data science
Data manipulation and cleaning with Pandas
Data visualization with Matplotlib and Seaborn
Exploratory Data Analysis (EDA)
Statistical analysis
Machine learning algorithms and techniques
Model evaluation and tuning

Tools Covered

Python
Jupyter Notebook
NumPy
Pandas
Matplotlib
Seaborn
Scikit-Learn
TensorFlow/Keras (for deep learning)

Data Science with Python Certification Training Syllabus

Curriculum Designed by Experts

Introduction to Data Science
Topics
  • Overview of Data Science
  • Data Science Workflow
  • Setting up the Python environment
Topics
  • Python basics for data science
  • Data types and structures
  • Control flow and functions
  • Working with libraries (NumPy, Pandas)
Topics
  • Introduction to Pandas
  • DataFrames and Series
  • Data cleaning and preprocessing
  • Data transformation and aggregation
Topics
  • Introduction to data visualization
  • Matplotlib and Seaborn basics
  • Creating various types of plots
  • Customizing plots and visualizations
Topics
  • Importance of EDA
  • Techniques for EDA
  • Identifying patterns and relationships
  • Using visualization for EDA
Topics
  • Descriptive statistics
  • Probability theory
  • Inferential statistics
  • Hypothesis testing
Topics
  • Overview of machine learning
  • Supervised vs. unsupervised learning
  • Common machine learning algorithms
  • Evaluation metrics
Topics
  • Linear Regression
  • Logistic Regression
  • Decision Trees and Random Forests
  • Support Vector Machines
Topics
  • K-Means Clustering
  • Hierarchical Clustering
  • Principal Component Analysis (PCA)
  • Anomaly detection
Topics
  • Ensemble methods
  • Gradient Boosting
  • Neural networks and deep learning basics
  • Natural Language Processing (NLP)
Topics
  • Train-test split and cross-validation
  • Model evaluation metrics
  • Hyperparameter tuning
  • Model selection and validation
Topics
  • End-to-End Data Science Project
  • Industry-Specific Projects
  • Best Practices in Data Science

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Why Data Science with Python Certification Training From Xpertised Learning

Live Interactive Classes

  • Top-Tier Trainers
  • Real Time Guidance by Industrial Experts
  • In Class Doubt Clearance

Resource Access

  • Life Time Access to Resource Library

Support

  • One-to-One Lab and Training Assistance
  • Help Desk Support
  • Doubt Clearance in Real-time

Project Based Learning

  • Labs and Learning Based from Industrial Experts
  • Learning Based on Project Scenarios

Certification

  • Xpertised Learning Training Certificate
  • Certificate of Completion

Labs

  • Cloud based Labs *Labs based on course requirement only

Data Science with Python Certification Training Details

This course aims to equip learners with the ability to analyze complex data sets, create data visualizations, and build predictive models using Python. It combines theoretical knowledge with hands-on exercises and real-world projects to ensure that learners can apply their skills in professional settings.

  • Gain proficiency in Python, a leading programming language for data science, enhancing your ability to analyze data and build machine learning models.
  • Boost your career opportunities by mastering data science techniques in Python, making you a valuable asset for roles in data analysis and machine learning.
  • Stay ahead in the tech industry by acquiring advanced data science skills, ensuring your ability to tackle complex data-driven projects.
  • Hands-on experience with Python and data science libraries.
  • Practical knowledge applicable to real-world scenarios.
  • Enhanced career opportunities in data science and analytics.
  • Certification that validates your data science skills and knowledge.
  • Python programming for data science
  • Data manipulation and cleaning with Pandas
  • Data visualization with Matplotlib and Seaborn
  • Exploratory Data Analysis (EDA)
  • Statistical analysis
  • Machine learning algorithms and techniques
  • Model evaluation and tuning
  • Python
  • Jupyter Notebook
  • NumPy
  • Pandas
  • Matplotlib
  • Seaborn
  • Scikit-Learn
  • TensorFlow/Keras (for deep learning)
  • Understand the core concepts of data science and Python programming.
  • Perform data manipulation, cleaning, and transformation using Pandas.
  • Create data visualizations to communicate insights effectively.
  • Implement machine learning algorithms and build predictive models.
  • Evaluate and tune machine learning models for better performance.
  • Apply data science techniques to real-world projects.
  • Aspiring Data Scientists
  • Data Analysts and Scientists
  • Machine Learning Engineers
  • IT Professionals
  • Anyone interested in data science and analytics
  • Basic knowledge of programming concepts is beneficial
  • Familiarity with Python is recommended but not mandatory
  • Core concepts of data science and Python programming
  • Data manipulation, cleaning, and transformation using Pandas
  • Creating data visualizations with Matplotlib and Seaborn
  • Performing exploratory data analysis (EDA)
  • Implementing machine learning algorithms and building predictive models
  • Evaluating and tuning machine learning models
  • Applying data science techniques to real-world projects
  • A computer with at least 8GB RAM and 20GB free disk space
  • Stable internet connection
  • Python installed (Python 3.x version)
  • Integrated Development Environment (IDE) like Jupyter Notebook
  • Enhanced job prospects in data science and analytics
  • Opportunities to work with top companies leveraging data science for their business needs
  • Potential for career growth and higher salaries in roles such as Data Scientist, Data Analyst, and Machine Learning Engineer

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