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Data Science with Python Training Key Features

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Hands-On Data Analysis & ML Labs

Get real-time practice with diverse datasets, applying Python libraries like NumPy, Pandas, and Scikit-learn in our dedicated lab environment.

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Flexible Online and In-Person Classes

Learn at your convenience through our classroom sessions at Ameerpet or Kukatpally, or join live interactive online classes from anywhere in the world.

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Continuous Project Support

Whether it's a technical question or guidance on your capstone project, our support team is available to assist you during and after your course.

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Career Transition & Placement Guidance

We help you prepare for data science interviews with mock sessions, resume building, and direct opportunities to land roles in data-driven organizations.

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Capstone Projects with Real Data

Gain invaluable experience by working on end-to-end data science projects guided by experienced mentors and developed with enterprise-level datasets.

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Vibrant Learning Community

Engage with a supportive community of peers and instructors, fostering collaborative learning and networking opportunities for your data science journey.

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Data Science with Python Training Overview

Value Learning offers comprehensive Data Science with Python training courses at both Ameerpet and Kukatpally (KPHB), Hyderabad. Our programs are designed to equip you with the practical skills and analytical capabilities needed to extract insights and drive decision-making from complex datasets.

Data Science with Python leverages Python's extensive ecosystem of libraries, including NumPy, Pandas, Matplotlib, and Scikit-learn, to perform data manipulation, statistical analysis, visualization, and machine learning. This powerful combination allows professionals to uncover patterns, build predictive models, and communicate data-driven insights effectively. Our expert-led training ensures you understand both the theoretical foundations and the practical application of data science methodologies in real-world scenarios.

320

Successful Learners

68k

Training Hours Delivered

540

Enterprise Projects Covered

Data Science with Python Training Objectives

The Data Science with Python course at Value Learning, delivered at our Ameerpet and Kukatpally (KPHB) centers in Hyderabad, is designed to give learners a solid understanding of modern data analysis and machine learning techniques using Python's powerful ecosystem.

Through this training, you will gain hands-on experience with Python libraries for data collection, cleaning, visualization, statistical modeling, and building predictive machine learning models. You'll learn to interpret complex data, derive actionable insights, and effectively communicate your findings.

The primary goal of the training is to help learners understand how to apply data science methodologies to solve real-world business problems and contribute to data-driven decision-making in various industries.

To equip learners with comprehensive, practical experience in the end-to-end data science project lifecycle, from data manipulation to model deployment, preparing them for successful careers as data scientists or analysts.

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Course Curriculum - Data Science with Python

Overview:
  • What is Data Science? Roles, Responsibilities, and Applications
  • Understanding the Data Science Life Cycle (CRISP-DM)
  • Introduction to Python for Data Science: Setup (Anaconda, Jupyter)
  • Python Fundamentals: Data Types, Variables, Operators, Control Flow
  • Working with Python Functions, Modules, and Packages

  • **NumPy**: Array Creation, Operations, Indexing, and Broadcasting
  • **Pandas**: Introduction to Series and DataFrames
  • Data Loading and Saving (CSV, Excel, SQL)
  • Data Cleaning and Preprocessing: Handling Missing Values, Duplicates
  • Data Transformation: Merging, Joining, Grouping, Pivoting DataFrames

  • Understanding Data Distributions and Central Tendency
  • Identifying Outliers and Anomalies
  • Correlation Analysis and Feature Relationships
  • Univariate and Bivariate Analysis Techniques
  • Generating Summary Statistics and Insights

  • **Matplotlib**: Creating Basic Plots (Line, Bar, Scatter, Histogram)
  • Customizing Plots: Titles, Labels, Legends, Colors
  • **Seaborn**: Enhanced Statistical Data Visualization
  • Creating Advanced Plots (Heatmaps, Box Plots, Pair Plots, Violin Plots)
  • Interpreting Visualizations for Data Insights

  • Probability Distributions (Normal, Binomial, Poisson)
  • Hypothesis Testing: Null and Alternative Hypotheses, p-value
  • Confidence Intervals and Z/t-Tests
  • ANOVA and Chi-Squared Tests
  • Regression Analysis and Correlation Coefficients

  • Introduction to Machine Learning: Supervised vs. Unsupervised Learning
  • Model Training Workflow: Data Splitting, Training, Validation
  • Feature Scaling and Engineering Techniques
  • Introduction to Scikit-learn Library
  • Evaluation Metrics for Regression and Classification Models

  • Linear Regression: Simple and Multiple Linear Regression
  • Polynomial Regression and Ridge/Lasso Regression
  • Decision Tree Regressor
  • Random Forest Regressor
  • Evaluating Regression Models (MAE, MSE, R-squared)

  • Logistic Regression for Binary Classification
  • Support Vector Machines (SVM)
  • Decision Tree Classifier and Random Forest Classifier
  • K-Nearest Neighbors (KNN)
  • Evaluating Classification Models (Accuracy, Precision, Recall, F1-Score, ROC-AUC)

  • Introduction to Unsupervised Learning
  • K-Means Clustering Algorithm
  • Hierarchical Clustering
  • DBSCAN Clustering
  • Principal Component Analysis (PCA) for Dimensionality Reduction

  • Cross-Validation Techniques (K-Fold, Stratified K-Fold)
  • Understanding Bias-Variance Tradeoff
  • Grid Search and Random Search for Hyperparameter Tuning
  • Ensemble Methods: Bagging, Boosting (Gradient Boosting, AdaBoost, XGBoost)
  • Saving and Loading Trained Models (Joblib, Pickle)

  • Fundamentals of Neural Networks and Deep Learning
  • Building Simple Feedforward Neural Networks
  • Introduction to TensorFlow and Keras API
  • Activation Functions, Loss Functions, and Optimizers
  • Concepts of Convolutional Neural Networks (CNNs - overview)

  • Text Preprocessing: Tokenization, Stemming, Lemmatization
  • Bag-of-Words and TF-IDF Vectorization
  • Introduction to NLTK and SpaCy Libraries
  • Sentiment Analysis (basic concepts)
  • Text Classification Fundamentals

  • SQL Basics for Data Extraction and Manipulation
  • Connecting Python to Databases (SQLite, PostgreSQL)
  • Overview of NoSQL Databases (MongoDB, Cassandra)
  • Introduction to Big Data Ecosystems (Hadoop, Spark - concepts)
  • Distributed Computing for Data Science (overview)

  • Version Control for Data Science Projects (Git/GitHub)
  • Introduction to MLOps Concepts
  • Building Simple Web Applications for Model Deployment (Flask/Streamlit overview)
  • Containerization with Docker (basics)
  • Monitoring and Maintaining Models in Production

  • Case Studies and End-to-End Data Science Project Walkthroughs
  • Building a Capstone Project
  • Interview Preparation: Common Data Science Questions
  • The Data Science Job Market in Hyderabad, Telangana, India
  • Continuous Learning and Specializations (e.g., AI, ML Engineering)
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