Data Science with Python Training
Introduction to Data Science
Understand the fundamentals of data science and its importance in analyzing and interpreting complex data. Learn about the data science workflow and key concepts such as data exploration, visualization, and modeling.
Python for Data Science
Learn how to use Python for data science tasks. Explore essential Python libraries such as NumPy, pandas, Matplotlib, and Seaborn for data manipulation, analysis, and visualization.
Data Cleaning and Preparation
Study techniques for data cleaning and preparation. Learn how to handle missing data, outliers, and data transformations to prepare datasets for analysis and modeling.
Exploratory Data Analysis (EDA)
Explore methods for exploratory data analysis. Learn how to use statistical techniques and visualization tools to uncover patterns, relationships, and insights in your data.
Statistical Analysis and Hypothesis Testing
Understand statistical analysis techniques and hypothesis testing. Learn about key concepts such as probability distributions, confidence intervals, p-values, and statistical tests.
Machine Learning with Python
Dive into machine learning using Python. Study algorithms for classification, regression, clustering, and dimensionality reduction. Learn how to implement and evaluate machine learning models using libraries such as scikit-learn.
Data Visualization
Learn techniques for effective data visualization. Explore how to create informative and visually appealing charts, graphs, and plots using Python visualization libraries.
Advanced Data Science Topics
Explore advanced topics in data science, including deep learning, natural language processing, and big data analytics. Learn how to apply these techniques to solve complex data problems.
Case Studies and Practical Exercises
Engage in case studies and practical exercises to apply data science concepts using Python. Practice analyzing real-world datasets, building models, and deriving actionable insights.
Exam Preparation and Certification
Prepare for data science certifications with study tips, practice exams, and review materials. Familiarize yourself with exam formats, question types, and strategies for success.
Data Science with Python Syllabus
Introduction to Python for Data Science
- Introduction to Python programming language
- Installation and setup of Python environment (Anaconda)
- Basic data types, variables, and operators
- Introduction to NumPy and Pandas for data manipulation
Data Manipulation with Pandas
- Data structures in Pandas: Series and DataFrame
- Indexing, slicing, and filtering data
- Data cleaning and preprocessing techniques
- Handling missing data and outliers
Data Visualization with Matplotlib and Seaborn
- Introduction to data visualization
- Basic plotting techniques with Matplotlib
- Advanced visualization with Seaborn
- Customizing plots and adding annotations
Exploratory Data Analysis (EDA)
- Understanding the structure and distribution of data
- Statistical summaries and descriptive statistics
- Univariate and bivariate analysis
- EDA case studies and practical exercises
Introduction to Machine Learning
- Overview of machine learning concepts and algorithms
- Supervised vs. unsupervised learning
- Model evaluation and validation techniques
- Introduction to scikit-learn library for machine learning in Python
Supervised Learning: Regression
- Introduction to regression analysis
- Simple linear regression
- Multiple linear regression
- Polynomial regression and regularization techniques
Supervised Learning: Classification
- Introduction to classification algorithms
- Logistic regression
- Decision trees and random forests
- Support Vector Machines (SVM)
Unsupervised Learning
- Introduction to unsupervised learning algorithms
- Clustering techniques: K-means clustering, hierarchical clustering
- Dimensionality reduction techniques: Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE)
Model Evaluation and Hyperparameter Tuning
- Cross-validation techniques for model evaluation
- Hyperparameter tuning using grid search and random search
- Model selection and performance metrics
- Overfitting, underfitting, and bias-variance trade-off
Feature Engineering and Selection
- Introduction to feature engineering
- Handling categorical variables: one-hot encoding, label encoding
- Feature scaling and normalization
- Feature selection techniques: filter methods, wrapper methods, embedded methods
Introduction to Deep Learning with TensorFlow and Keras
- Overview of deep learning concepts
- Introduction to TensorFlow and Keras libraries
- Building and training neural networks
- Convolutional Neural Networks (CNNs) for image classification
Capstone Project
- Final project where students apply their knowledge and skills to solve a real-world data science problem
- Project planning, data exploration, modeling, and evaluation
- Presentation of projects and peer feedback
Training
Basic Level Training
Duration : 1 Month
Advanced Level Training
Duration : 1 Month
Project Level Training
Duration : 1 Month
Total Training Period
Duration : 3 Months
Course Mode :
Available Online / Offline
Course Fees :
Please contact the office for details