Data Science & Big Data Analytics

This course is designed for professionals in data-driven roles who are seeking to enhance their skills in data science and big data analytics. It is ideal for data analysts, business analysts, data engineers, IT professionals, and managers who want to understand how to extract valuable insights from large datasets to drive business decisions. Individuals from various industries, such as finance, healthcare, marketing, and manufacturing, will benefit from this course.

Day 1: Introduction to Data Science and Big Data

• Overview of data science and its applications in various industries
• Key concepts: Data collection, processing, and analysis
• Big Data: Definition, sources, and importance
• Types of data: Structured, unstructured, and semi-structured
• Introduction to data science tools: Python, R, SQL
• Practical exercise: Setting up a data science environment (e.g., installing Python, R, and Jupyter notebooks)
• Case study: Real-world applications of data science in business

Day 2: Data Processing and Data Cleaning

• The data preparation pipeline: From raw data to ready-to-use data
• Data wrangling techniques using Python and R
• Handling missing data and outliers
• Data transformations and normalization
• Feature engineering and creating new variables from existing data
• Practical exercise: Cleaning and preparing a messy dataset for analysis
• Case study: Data preprocessing for machine learning

Day 3: Data Visualization and Exploratory Data Analysis (EDA)

• Introduction to data visualization principles and best practices
• Tools for data visualization: Matplotlib, Seaborn, Tableau
• Creating various types of charts and plots: Bar, line, scatter, and heat maps
• Performing exploratory data analysis (EDA) to identify patterns and insights
• Visualizing and interpreting data distributions and relationships
• Practical exercise: Building visualizations to explore a dataset
• Case study: Using EDA to uncover business insights

Day 4: Introduction to Machine Learning and Algorithms

• Overview of machine learning: Supervised vs unsupervised learning
• Introduction to regression, classification, and clustering algorithms
• Building a simple linear regression model
• Decision trees, k-NN, k-means clustering, and random forests
• Model evaluation metrics: Accuracy, precision, recall, and F1 score
• Practical exercise: Building and evaluating a machine learning model
• Case study: Predicting customer behavior using machine learning

Day 5: Big Data Technologies and Advanced Analytics

• Introduction to Big Data technologies: Hadoop, Spark, NoSQL databases
• Working with large datasets using Hadoop and Spark
• Parallel processing and distributed computing with Spark
• Introduction to advanced analytics: Natural language processing (NLP) and deep learning
• Applying big data tools to analyze and process unstructured data
• Practical exercise: Using Spark for large-scale data processing
• Case study: Applying big data analytics to solve business problems