Data Science Training

Introduction to Data Science

• Understanding the role of data science in modern business and technology
• Exploring the lifecycle of a data science project and its components

Data Acquisition and Cleaning

• Gathering data from various sources, including databases, APIs, and web scraping
• Preprocessing and cleaning raw data to ensure quality and consistency

Exploratory Data Analysis (EDA)

• Visualizing and summarizing data to gain insights and identify patterns
• Performing statistical analysis and hypothesis testing to uncover relationships and correlations

Machine Learning Fundamentals

• Understanding the principles of machine learning and its applications in data science
• Exploring supervised and unsupervised learning techniques for classification, regression, and clustering tasks

Feature Engineering and Selection

• Engineering informative features from raw data to improve model performance
• Selecting relevant features using techniques such as correlation analysis and feature importance ranking

Model Evaluation and Validation

• Evaluating the performance of machine learning models using cross-validation and performance metrics
• Tuning model hyperparameters to optimize predictive performance and generalization

Data Visualization

• Creating interactive and informative visualizations to communicate insights effectively
• Leveraging libraries such as Matplotlib, Seaborn, and Plotly for data visualization

Big Data and Distributed Computing

• Introduction to big data technologies such as Hadoop and Spark
• Processing and analyzing large-scale datasets using distributed computing frameworks