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Data Science with Python – Master Data Analysis, Machine Learning & Visualisation

Unlock the power of data with Insta InfoTech®’s Data Science with Python Training, a comprehensive program designed to help you master Data Analysis, Machine Learning, and Data Visualization using Python. This course covers everything from Python basics to advanced techniques, including Data Manipulation, Statistical Analysis, Predictive Modeling, and Data Visualization with libraries like Pandas, NumPy, Matplotlib, and Scikit-Learn. Whether you're a beginner or a professional, this training will prepare you for high-demand roles like Data Scientist, Data Analyst, and Machine Learning Engineer. Earn a recognized Data Science Certification and elevate your career in the data-driven world.

Data Science with Python – Master Data Analysis, Machine Learning & Visualisation
  • Python Basics: Learn Python programming essentials for data science, including Data Types, Loops, and Functions.
  • Data Manipulation: Master Pandas and NumPy for data cleaning, transformation, and analysis.
  • Data Visualization: Create interactive and insightful visualizations using Matplotlib and Seaborn.
  • Machine Learning: Explore supervised and unsupervised learning techniques using Scikit-Learn.
  • Real-World Applications: Work on projects in Predictive Modeling, Data Analysis, and Data Visualization.
  • Certification: Receive a recognized Data Science Certification to validate your expertise.
  • Master Python for Data Science: Learn Python programming fundamentals and its applications in Data Analysis and Machine Learning.
  • Data Manipulation & Cleaning: Use Pandas and NumPy to clean, transform, and analyze datasets.
  • Data Visualization: Create insightful visualizations using Matplotlib and Seaborn.
  • Machine Learning: Build predictive models using Scikit-Learn and explore algorithms like regression, classification, and clustering.
  • Real-World Projects: Apply your skills to real-world projects in Data Science and Machine Learning.
  • Certification: Earn a recognized Data Science Certification upon course completion.
Why Choose Insta InfoTech®?
  • Expert trainers with real-world experience in Data Science and Machine Learning.
  • Hands-on training with practical projects and case studies.
  • Flexible learning options (online and offline).
  • Placement assistance to help you land roles like Data Scientist, Data Analyst, and Machine Learning Engineer.
  • Lifetime access to course materials and support.
Who Should Enroll?
  • Aspiring Data Scientists and Data Analysts.
  • IT Professionals looking to upskill in Data Science and Machine Learning.
  • Graduates seeking a career in Data Analysis and Predictive Modeling.
  • Anyone interested in mastering Data Visualization and Machine Learning using Python.
Transform Your Career Today!

Join Insta InfoTech®’s Data Science with Python Training and become a data-driven professional. Gain the skills to analyze, visualize, and model data effectively, and open doors to high-demand roles in the IT industry.

Insta Infotech Data Science with Python Course, you will have a strong foundation in data science principles, techniques, and tools using the Python programming language. You'll be equipped with the skills to analyze data, create visualizations, and build predictive models. Whether you're pursuing a career in data science or aiming to leverage data-driven insights in your field, this course will empower you to unlock the power of Python and make informed decisions based on data.

  • Understanding the data science lifecycle
  • Roles and responsibilities of a data scientist
  • Introduction to R for data science
  • Setting up the R development environment

  • R syntax and data structures
  • Working with variables and operators
  • Control flow statements (if, for, while)
  • Functions and packages in R

  • Introduction to package
  • Loading and exploring data with dplyr
  • Data cleaning and preprocessing
  • Filtering, sorting, and summarizing data

  • Introduction to data visualization
  • Creating plots with ggplot2
  • Customizing plots and aesthetics
  • Creating complex visualizations and faceting

  • EDA techniques and principles
  • Statistical summaries and descriptive statistics
  • Visualizing distributions and relationships
  • Feature engineering and transformation

  • Introduction to machine learning
  • Supervised vs. unsupervised learning
  • Model training and evaluation
  • Overfitting, underfitting, and model selection

  • Linear regression for regression tasks
  • Logistic regression for classification tasks
  • Decision trees and random forests
  • Support Vector Machines (SVM)

  • Clustering techniques (K-means, hierarchical clustering)
  • Dimensionality reduction with Principal Component Analysis (PCA)
  • Anomaly detection algorithms
  • Recommendation systems

  • Evaluating model performance metrics
  • Cross-validation and overfitting prevention
  • Hyperparameter tuning and model optimization
  • Model persistence and deployment

  • Basics of text mining and NLP
  • Preprocessing text data in R
  • Text classification and sentiment analysis
  • Topic modeling and text clustering

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