Introduction:

Data Science and Big Data Technologies are revolutionizing industries by enabling businesses to analyze vast amounts of data and derive valuable insights for decision-making. This 5-day course on “Data Science & Big Data Technologies” will provide participants with the knowledge and practical skills needed to work with large-scale data sets, apply advanced data science techniques, and leverage big data technologies for real-time data processing and analysis. Participants will explore essential tools, algorithms, and frameworks that enable efficient data processing, machine learning, and data visualization in the modern data-driven world.

Objectives:

By the end of this course, participants will be able to:

1. Understand Data Science Concepts: Learn the key principles and techniques in data science, including data exploration, cleaning, and modeling.
2. Work with Big Data Frameworks: Gain hands-on experience with big data tools and technologies, such as Hadoop, Spark, and NoSQL databases, to process and analyze large datasets.
3. Apply Machine Learning Techniques: Learn how to apply supervised and unsupervised machine learning algorithms to extract insights from data and build predictive models.
4. Utilize Data Visualization: Master the art of data visualization using tools like Python’s Matplotlib, Seaborn, and Tableau to communicate findings effectively.
5. Understand Big Data Ecosystem: Explore the big data ecosystem, including distributed computing, cloud technologies, and data warehousing.
6. Prepare for Future Trends in Data Science: Stay up-to-date with emerging technologies in AI, deep learning, and data engineering for building scalable data pipelines.

Who Should Attend:

This course is ideal for:

• Data Analysts and Business Intelligence Professionals who wish to enhance their skills in big data analytics and machine learning.
• Software Engineers and Developers who want to explore data science and big data technologies for building scalable applications.
• Data Scientists looking to deepen their knowledge in advanced data analysis, big data tools, and machine learning algorithms.
• IT Architects and Cloud Engineers who want to learn how to implement big data technologies in cloud environments.
• Students and Professionals looking to shift into the field of data science and big data.

Day-by-Day Outline:

Day 1: Introduction to Data Science and Big Data Technologies

• Morning Session:

  • Introduction to Data Science:
    • Key concepts and lifecycle of data science projects
    • Types of data analysis: Descriptive, Predictive, and Prescriptive Analytics
    • Overview of the data science workflow: Data collection, cleaning, exploration, modeling, evaluation, and deployment
  • Introduction to Big Data:
    • What is Big Data? The 5 Vs of Big Data: Volume, Velocity, Variety, Veracity, and Value
    • Challenges in managing big data: Storage, processing, and analysis
    • Big Data tools and technologies: Hadoop, Spark, NoSQL databases, Cloud platforms (AWS, Azure, GCP)

• Afternoon Session:

  • Data Science Tools and Libraries:
    • Python for Data Science: Numpy, Pandas, and Scikit-learn
    • Jupyter Notebooks for interactive data exploration and visualization
    • Introduction to SQL for querying structured data
  • Hands-on Labs:
    • Setting up Python, Jupyter Notebook, and key libraries (Numpy, Pandas)
    • Loading, cleaning, and visualizing sample datasets using Pandas and Matplotlib

Day 2: Data Preparation and Data Wrangling

• Morning Session:

  • Data Preparation:
    • Types of data: Structured, unstructured, and semi-structured data
    • Data cleaning techniques: Handling missing data, outliers, duplicates
    • Data transformation: Normalization, scaling, encoding categorical variables
  • Big Data Storage Systems:
    • Traditional relational databases vs. NoSQL databases (MongoDB, Cassandra, HBase)
    • Introduction to data warehousing and cloud data storage (Amazon S3, Azure Blob)
    • Distributed file systems (HDFS) and storage for big data applications

• Afternoon Session:

  • Data Wrangling Techniques:
    • Merging and joining datasets, reshaping data, and aggregating data
    • Time series data manipulation and analysis
    • Handling large datasets using Pandas and Dask
  • Hands-on Labs:
    • Data wrangling exercises: Cleaning, transforming, and preparing data for analysis
    • Exploring data with Pandas and Dask for big data handling

Day 3: Machine Learning Fundamentals

• Morning Session:

  • Introduction to Machine Learning:
    • Supervised vs. Unsupervised Learning
    • Common machine learning algorithms: Linear Regression, Decision Trees, k-NN, k-Means, SVM
    • Model evaluation metrics: Accuracy, Precision, Recall, F1-Score, Cross-validation
  • Working with Big Data and Machine Learning:
    • Using Apache Spark for large-scale machine learning (MLlib)
    • Distributed machine learning on big data platforms
    • Challenges and considerations for scaling machine learning models to big data

• Afternoon Session:

  • Hands-on Labs:
    • Implementing linear regression and decision trees on small datasets
    • Building and evaluating a classification model using Scikit-learn
    • Running machine learning models on large datasets using Apache Spark

Day 4: Advanced Big Data Technologies and Real-time Processing

• Morning Session:

  • Advanced Big Data Frameworks:
    • Apache Hadoop Ecosystem: HDFS, MapReduce, Hive, Pig
    • Apache Spark: RDDs, DataFrames, and SparkSQL for big data processing
    • Using Apache Kafka for real-time data streaming and processing
  • Real-Time Data Processing:
    • Stream processing with Apache Flink, Apache Storm
    • Handling real-time data feeds: IoT, social media, financial data

• Afternoon Session:

  • Hands-on Labs:
    • Implementing big data processing using Hadoop and Spark
    • Real-time data processing with Apache Kafka and Spark Streaming
    • Working with large datasets on AWS using Elastic MapReduce (EMR)

Day 5: Data Visualization, Big Data Analytics, and Future Trends

• Morning Session:

  • Data Visualization Techniques:
    • Visualizing data using Python’s Matplotlib and Seaborn
    • Interactive dashboards using Plotly and Tableau
    • Best practices for creating effective data visualizations
  • Big Data Analytics:
    • Applying advanced analytics techniques: Predictive modeling, anomaly detection, and clustering
    • Building scalable data pipelines for big data analytics
    • Leveraging AI and deep learning for big data analysis

• Afternoon Session:

  • Emerging Trends in Data Science and Big Data:
    • The role of AI and deep learning in big data analytics
    • Data engineering and building automated data pipelines
    • Cloud computing and serverless computing in big data applications
  • Hands-on Labs:
    • Building a predictive model and visualizing the results
    • Creating a dashboard for big data insights using Tableau or Power BI
    • Exploring future trends: AI-powered analytics, edge computing for real-time processing

Conclusion and Certification

• Summary of Key Learnings
• Final Q&A session
• Distribution of certificates of completion
• Post-training resources, career guidance, and continued learning opportunities