Big Data Analytics with Python Training Course

Learning Objectives

A Big Data Analytics with Python course typically covers the following topics:

1. Introduction to Big Data: The course will provide an overview of big data concepts, technologies and tools. It will also introduce the concept of distributed computing and parallel processing.
2. Data Processing with Python: The course will cover data processing with Python, including the use of Python libraries for data manipulation and analysis such as Pandas, Numpy and SciPy.
3. Big Data Storage and Retrieval: The course will cover big data storage and retrieval technologies such as Hadoop, Spark and NoSQL databases.
4. Data Visualization: The course will cover data visualization techniques and tools such as Matplotlib and Seaborn.
5. Machine Learning with Python: The course will cover machine learning techniques and their implementation in Python, including supervised and unsupervised learning, feature engineering, model selection and evaluation.
6. Big Data Analytics with Python: The course will cover the use of Python for big data analytics, including analyzing large datasets and developing predictive models.
7. Project Work: The course will typically include a project that will allow students to apply the skills and concepts learned in the course to real-world problems.

By the end of the course, students should have a strong understanding of big data concepts and technologies, as well as the skills and knowledge needed to analyze and extract insights from large datasets using Python. They should also be able to develop machine learning models to predict outcomes based on big data.

Our Unique Training Methodology

• Lectures: Experienced instructors will provide comprehensive lectures on the key topics of blockchain technology.
• Hands-On Labs: Participants will apply their knowledge through practical lab exercises and projects using blockchain development platforms and tools.
• Group Discussions and Case Studies: Participants will analyze real-world blockchain case studies and engage in group discussions to understand its various applications.
• Assessment and Certification: Participants will take a final assessment to test their understanding and receive a certificate upon completion.
• Ongoing Support: Participants will have access to ongoing support from instructors and support staff.

This training methodology emphasizes hands-on learning and real-world application to provide a well-rounded and practical learning experience.

Training Medium

This Behavioral Interviewing training is designed in a way that it can be delivered face-to-face and virtually.

Course Duration

This Big Data analytics with Python course skills training is versatile in its delivery. The training can be delivered as a full-fledged 60-hours training program or a 25- hours crash course covering 5 hours of content each day over 5 days

Pre-course Assessment

• Knowledge of basic programming concepts: Students should have a solid foundation in programming concepts such as variables, data types, control structures, and functions.
• Knowledge of Python programming: Students should have a good understanding of Python programming, including object-oriented programming, control structures, and functions. Experience working with Python libraries such as NumPy and Pandas can also be helpful.
• Mathematics and Statistics: Students should have a good foundation in basic mathematics and statistics concepts, such as linear algebra, probability, and statistical inference.
• Understanding of databases: Familiarity with relational databases and SQL is recommended, as many big data technologies rely on these concepts.
• Knowledge of Data Structures: Understanding of basic data structures such as arrays, linked lists, queues, stacks, and trees is also useful.

Course Modules

Chapter 1: Introduction to Big Data Analytics with Python

• Overview of big data analytics and why it’s important
• Introduction to Python and its capabilities for data analytics
• Overview of Python libraries commonly used in big data analytics (NumPy, Pandas, Matplotlib, etc.)

Chapter 2: Data Cleaning and Preprocessing

• Understanding the importance of data cleaning and preprocessing
• Techniques for cleaning and transforming data in Python (removing missing values, handling outliers, feature scaling, etc.)
• Introduction to data preprocessing libraries in Python (Scikit-learn, SciPy, etc.)

Chapter 3: Data Exploration and Visualization

• Overview of data exploration and visualization techniques
• Data visualization libraries in Python (Matplotlib, Seaborn, etc.)
• Exploratory data analysis techniques (univariate and bivariate analysis, correlation analysis, etc.)

Chapter 4: Data Analysis and Machine Learning

• Introduction to machine learning and its importance in big data analytics
• Techniques for data analysis and modeling (regression, classification, clustering, etc.)
• Machine learning libraries in Python (Scikit-learn, TensorFlow, Keras, etc.)

Chapter 5: Big Data Technologies

• Understanding the basics of big data technologies (Hadoop, Spark, etc.)
• Overview of distributed computing and its advantages for big data analytics
• Introduction to big data processing with Python (PySpark, Dask, etc.)

Chapter 6: Real-World Applications of Big Data Analytics with Python

• Case studies and examples of big data analytics in various industries (finance, healthcare, marketing, etc.)
• Understanding the challenges and limitations of big data analytics
• Best practices for implementing big data analytics projects in Python

Module topics could include specific techniques or libraries within each chapter, such as:

• Data cleaning techniques (module 2)
• Visualizing data with Matplotlib (module 3)
• Regression analysis with Scikit-learn (module 4)
• Processing big data with PySpark (module 5)
• Case studies in healthcare analytics (module 6)